diff --git "a/4848.jsonl" "b/4848.jsonl" new file mode 100644--- /dev/null +++ "b/4848.jsonl" @@ -0,0 +1,698 @@ +{"seq_id":"369627469","text":"#2020-12-04\n#embedding(벡터화)\n#LSTM\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nimport numpy as np\n\n\n#X\ndocs = [\"너무 재밌어요\", \"참 최고예요\", \"참 잘 만든 영화예요\", \"추천하고 싶은 영화입니다\",\n \"한 번 더 보고 싶네요\", \"글쎄요\", \"별로예요\", \"생각보다 지루해요\", \"연기가 어색해요\",\n \"재미없어요\", \"너무 재미없다\", \"참 재밌네요\"] \n\n#Y\n#1. 긍정 1, 부정 0\nlabels = np.array([1, 1, 1, 1,1, 0, 0, 0, 0, 0, 0, 1])\n\n#docs를 수치화하면 처리 가능 \n\ntoken = Tokenizer()\ntoken.fit_on_texts(docs)\nprint(token.word_index) #많이 나오는 애가 앞으로 감(빈도수 높은 애): 25개\n\nx = token.texts_to_sequences(docs)\n# [[2, 3], [1, 4], [1, 5, 6, 7], [8, 9, 10], [11, 12, 13, 14, 15], [16], [17], [18, 19], [20, 21], [22], [2, 23], [1, 24]]\n# 긴 쪽으로 맞추자 (짧은 쪽에 맞추면 데이터 날아감) 빈 앞을 0으로 채우기(의미 있는 숫자가 뒤로 밀림) \n# 일정하지 않음\n\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences #0으로 채우는\npad_x = pad_sequences(x, padding='pre') #뒤로 채우는 건 post\nprint(pad_x) #(12, 5) docs의 개수, 5(제일 긴 것)\nprint(pad_x.shape) #25개 vector화\n\n\nword_size = len(token.word_index) + 1 #padding\n# print(\"전체 토큰 사이즈: \", word_size) 전체 토큰 사이즈: 25\n# 12, 5지만 그 안에 들어가는 종류는 25가지\n\n\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Embedding, LSTM, Flatten, Conv1D\n\n\nmodel = Sequential()\n\nmodel.add(Embedding(25, 10, input_length=5)) #12, 5였음(명시해 줄 때는 column 개수 같아야)\n# model.add(Embedding(25, 10)) #25, 9도 오케이 25, 8도 오케이... \n\n# model.add(LSTM(32))\nmodel.add(Conv1D(32, 2))\nmodel.add(Flatten())\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.summary()\n\n#input_length 없이 flatten -> dense? x\n#input_length + flatten -> dense o \n\n\n#embedding은 처음에 output node 개수 아님#\n#embedding = one hot encoding을 벡터화\n#단어 사전의 수, output node 개수, \n\n#input_length 다른 걸로 해도 돌아가긴 함 \n#WARNING:tensorflow:Model was constructed with shape (None, 3) for input Tensor(\"embedding_input:0\", shape=(None, 3), dtype=float32), but it was called on an input with incompatible shape (None, 5).\n#하지만 column 개수 맞춰 주는 게 좋다\n\n##### 다 대충 써도 되지만... input_length는 가급적이면 column 개수랑 맞춰 주고, 사전 수는 25보다 작게....\n\n\n#명시 안 하면 자동으로 먹힌다\n #26, 10 -> 이래도 에러 남...\n #input_length만 잘 맞춰 주면... 잘 돌아가는 거 아닌지 \n #단, 사전의 개수보다 작게 주면 터짐 \n\n# 원핫인코딩이었다면 25, 25 -> 임베딩 레이어로 하면 25, 10으로 벡터화(10은 그냥 임의로 해도 됨)\n# 3차원 input -> LSTM 가능\n\n\nmodel.compile(optimizer='adam', \n loss='binary_crossentropy', \n metrics=['acc'])\n\nmodel.fit(pad_x, labels, epochs=30)\n\n#data 적으니까 그냥 이렇게 한다 \nacc = model.evaluate(pad_x, labels)[1]\nprint(\"acc: \", acc)\n\n\n\n'''\n# model.add(Embedding(25, 10, input_length=5)) #12, 5였음\n\nModel: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param #\n=================================================================\nembedding (Embedding) (None, 5, 10) 250 = 25 * 10 \n_________________________________________________________________\nlstm (LSTM) (None, 32) 5504\n_________________________________________________________________\ndense (Dense) (None, 1) 33\n=================================================================\nTotal params: 5,787\nTrainable params: 5,787\nNon-trainable params: 0\n_________________________________________________________________\n'''\n\n'''\nmodel.add(Embedding(25, 10))\n#5가 사라졌는데도 똑같음 None이 됐는데 ;;\nModel: \"sequential\"\n_________________________________________________________________\nLayer (type) Output Shape Param #\n=================================================================\nembedding (Embedding) (None, None, 10) 250\n_________________________________________________________________\nlstm (LSTM) (None, 32) 5504\n_________________________________________________________________\ndense (Dense) (None, 1) 33\n=================================================================\nTotal params: 5,787\nTrainable params: 5,787\nNon-trainable params: 0\n_________________________________________________________________\n'''\n\n","sub_path":"keras2/keras79_embedding2.py","file_name":"keras79_embedding2.py","file_ext":"py","file_size_in_byte":4900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"455512239","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport os\nfrom scipy.optimize import curve_fit, fmin\n\n\n\ndef quadr(x, a, b, c):\n return a + b*x + c*x**2\n\ndef line (x, a, b):\n return a - b*x\n\ndef line2 (x, a, b):\n return -b*(a-x)\n\n\ndef size_distribution (x, sigma, m):\n ln = np.log(x)-m\n exp = np.exp(-(ln**2)/(2*sigma**2))\n const = 1/((np.sqrt(2*np.pi))*sigma*x)\n return const * exp\n\n\n#a = 0.3219 #nanometer\nwl = 1.4235e-2 #nm\n\n\n\n\n\nloaddir = '/Users/ivanov/Yandex.Disk.localized/DESY_2018/Ti/FFT/'\nsavepng = '/Users/ivanov/Yandex.Disk.localized/DESY_2018/Ti/Results/Pictures/xarea.png'\nxarea_save = '/Users/ivanov/Yandex.Disk.localized/DESY_2018/Ti/Results/Data/Calculated/xarea.txt'\n\n\nborder = 169\ntemperature = np.linspace(4, border, border-4)\n\ndirectorylist = []\nfor file in os.listdir('/Users/ivanov/Yandex.Disk.localized/DESY_2018/Ti/Integrated/'):\n if file.endswith('.txt'):\n directorylist.append(file[:-4])\ndirectorylist.sort()\n\nfilenums = [int(directoryname) for directoryname in directorylist]\n\nxarea_list = []\n\n\n\ndirectorylist = directorylist[4:border]\nx_axis = temperature\n\nfor k, directory in enumerate(directorylist):\n reversedir = loaddir+directory\n filelist = []\n\n #b = burgers_list[k, 1]\n\n for file in os.listdir(reversedir):\n if file.endswith('.txt'):\n filelist.append(file)\n filelist.sort()\n allLlist = []\n alist = []\n blist = []\n\n ran = 12\n for i in range(ran):\n\n ALlist = []\n Xlist = []\n hkdotl = []\n Llist = []\n\n lnALlist = []\n mark = ['o', 'v', 'P', 's', '^', 'h', 'X']\n approx = ['black', 'orangered', 'mediumblue', 'crimson', 'green', 'maroon', 'rebeccapurple']\n style = ['--', '-.', ':']\n mark = mark * 50\n approx = approx * 50\n style = style * 50\n\n for j, file in enumerate(filelist):\n data = np.loadtxt(os.path.join(reversedir, file))\n\n if j == 0:\n L_ = data[i, 0]\n allLlist.append(L_)\n\n L = data[i, 0]\n Llist.append(L)\n K = float(file[6:-4])\n\n X = K**2\n Xlist.append(X)\n AL0 = data[0, 1]\n AL = data[i, 1]\n lnAL = np.log(AL/AL0)\n lnALlist.append(lnAL)\n\n Xlist = np.array(Xlist)\n lnALlist = np.array(lnALlist)\n\n popt, pcov = curve_fit(quadr, Xlist, lnALlist, bounds=(-np.inf, [np.inf, np.inf, 0]))\n alist.append(popt[0])\n blist.append(popt[1])\n\n\n '''\n plt.scatter(Xlist, lnALlist, marker='%s' % mark[i], edgecolor='indianred', color='white', label='L = %.2f' % L)\n Xlist = np.linspace(np.min(Xlist) - 0.25, np.max(Xlist) + 0.25, 50)\n plt.plot(Xlist, quadr(Xlist, *popt), linestyle='%s' % style[i], color='%s' % approx[i], alpha=0.8)\n plt.ylabel(r'$lnA(L)$')\n plt.xlabel(r'$K^2\\overline{C_{hkl}}$')\n # plt.xlim(0, 0.1)\n # plt.ylim(-5, 1)\n # plt.xticks(np.arange(0, 2.1, step=1))\n #plt.title('%s' % object[num], loc='right')\n plt.legend(edgecolor='white')\n\n #plt.savefig(savelnAL, dpi=150)\n #plt.savefig(savelnALpdf, dpi=150)\n plt.show()\n '''\n\n\n length = 6\n\n allLlist = np.array(allLlist)\n alist = np.array(alist)\n blist = np.array(blist)\n\n allL_list = allLlist[:length]\n a_list = alist[:length]\n b_list = blist[:length]\n\n AS = np.exp(a_list)\n AS_full = np.exp(alist)\n\n # AS_full = AS_full[:-15]\n # allLlist = allLlist [:-15]\n\n popt, pcov = curve_fit(line, allL_list, AS)\n perr = np.sqrt(np.diag(pcov)) / np.sqrt(len(allL_list))\n\n L0 = popt[0] / popt[1]\n xarea = 1.5*L0\n xarea_list.append(xarea)\n\n print (directory)\n print ('xarea = %.2f nm'%xarea)\n\n '''\n A_ = AS[0] * 1.5 * 2\n L_ = L0 * 1.5\n beta = 2.5 / 4.5\n m = ((A_ ** beta) / L_) ** (1.5)\n\n D_ = 1 / 2.5 * np.log(L_ / m)\n sigma = np.sqrt(D_)\n print(L_, m, sigma)\n\n xarea = m * np.exp(2.5 * sigma ** 2)\n xvol = m * np.exp(3.5 * sigma ** 2)\n d = 3 / 4 * m * np.exp(7 / 4 * (np.sqrt(2) * sigma) ** 2)\n\n \n x = np.linspace(0, 100, 1000)\n plt.plot(x, size_distribution(x, sigma, m), label = 'm %.2f\\ns %.2f\\nx %.2f'%(m, sigma, 1.5*L0))\n plt.ylim(0, 1)\n plt.xlim(0, 10)\n plt.legend()\n plt.show()\n '''\n\n\n'''\n #fig, ax = plt.subplots(figsize=(7, 3.5))\n # plt.subplot (211) #211 for two\n\n plt.scatter(allLlist, AS_full, marker='o', edgecolor='darkred', color='white', alpha=0.7)\n fitallLlist = np.linspace(0, popt[0] / popt[1] + 0.5, 50)\n plt.plot(fitallLlist, line(fitallLlist, *popt), linestyle='-.', color='midnightblue', alpha=0.7)\n plt.plot(fitallLlist, line(fitallLlist, *popt), linestyle='-.', color='white', alpha=0) # ,\n # label = ' L0 %.2f(%.2f)\\n %.2f(%.2f)' %(L0, dL0, 1.5*L0, 1.5*dL0))\n plt.ylabel(r'$A^s(L)$')\n plt.xlabel(r'$L$')\n # plt.title('%s' % object[num], loc='right')\n plt.legend(edgecolor='white')\n # plt.text (popt[0]/popt[1], 0.2, r'$L_0 = %.2f(%.2f) nm$'%(L0, dL0), horizontalalignment='left')\n plt.xlim(-2, 100)\n # plt.yticks(np.arange(0, 1.1, step=0.5))\n # if num == 0 or num == 1:\n # plt.ylim(-0.2, AS[0]+0.7)\n #plt.savefig(saveASpng, dpi=150)\n #plt.savefig(saveASpdf, dpi=150)\n plt.show()\n # plt.clf()\n'''\n\n\n#xarea_data = np.array((np.row_stack((np.array(filenums), np.array(xarea_list))).T))\n#np.savetxt(xarea_save, xarea_data, fmt = '%.6d %.6f')\n\n\nfig, ax = plt.subplots(figsize=(7, 3.5))\nplt.scatter(x_axis, xarea_list, color='white', edgecolor='darkred',\n marker='s', s=14)\nplt.ylabel(r'$x_{area}\\/[nm]$')\nplt.xlabel(r'$Approximate\\/\\/temperature$')\nplt.title(r'$MWA\\/\\beta Ti\\/ $', loc = 'right')\n\n#plt.savefig(savepng, dpi = 150)\n\nplt.show()\n","sub_path":"DESY_2018/Ti/12_mwa_size.py","file_name":"12_mwa_size.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"461589056","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 1 01:53:51 2018\r\n\r\n@author: dell\r\n\"\"\"\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\n\r\ndef main ():\r\n imgpath = \"//home//vaibhav//Documents//OMR Project//omr samples//sheet1.jpg\"\r\n #imgpath = \"C:\\\\Users\\\\dell\\\\Desktop\\\\Documents\\\\OMR project\\\\standard_test_images\\\\lena_color_256.tif\"\r\n img = cv2.imread(imgpath,0)\r\n \r\n block_size = 11\r\n constant = 2\r\n #th1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, block_size, constant)\r\n \r\n th1 = cv2.adaptiveThreshold (img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, block_size, constant)\r\n \r\n plt.imshow(th1,cmap = 'gray')\r\n plt.show()\r\n\r\nif __name__ ==\"__main__\":\r\n main() \r\n \r\n","sub_path":"thresholding.py","file_name":"thresholding.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"318309533","text":"# -*- coding: utf-8 -*-\nimport os\nimport re\nimport sys\nfrom optparse import make_option\nfrom pyflakes.scripts import pyflakes\nfrom cStringIO import StringIO\nfrom django_jenkins.functions import relpath\nfrom django_jenkins.tasks import BaseTask, get_apps_locations\n\n\nclass Task(BaseTask):\n option_list = [\n make_option(\"--pyflakes-with-migrations\",\n action=\"store_true\", default=False,\n dest=\"pyflakes_with_migrations\",\n help=\"Don't check migrations with pyflakes.\")]\n\n def __init__(self, test_labels, options):\n super(Task, self).__init__(test_labels, options)\n self.test_all = options['test_all']\n self.with_migrations = options['pyflakes_with_migrations']\n\n if options.get('pyflakes_file_output', True):\n output_dir = options['output_dir']\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n self.output = open(os.path.join(output_dir, 'pyflakes.report'), 'w')\n else:\n self.output = sys.stdout\n\n def teardown_test_environment(self, **kwargs):\n locations = get_apps_locations(self.test_labels, self.test_all)\n\n # run pyflakes tool with captured output\n old_stdout, pyflakes_output = sys.stdout, StringIO()\n sys.stdout = pyflakes_output\n try:\n for location in locations:\n if os.path.isdir(location):\n for dirpath, dirnames, filenames in os.walk(relpath(location)):\n if not self.with_migrations and 'migrations' in dirpath:\n continue\n for filename in filenames:\n if filename.endswith('.py'):\n pyflakes.checkPath(os.path.join(dirpath, filename))\n else:\n pyflakes.checkPath(relpath(location))\n finally:\n sys.stdout = old_stdout\n\n # save report\n pyflakes_output.reset()\n while True:\n line = pyflakes_output.readline()\n if not line:\n break\n message = re.sub(r': ', r': [E] PYFLAKES:', line)\n self.output.write(message)\n\n self.output.close()\n","sub_path":"django_jenkins-0.13.0-py2.7.egg/django_jenkins/tasks/run_pyflakes.py","file_name":"run_pyflakes.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"593161145","text":"'''\r\nUsage: python ./dtld_to_tfrecord.py --input_yaml input_file_name.yaml --output_path output_file_name.record\r\n'''\r\n\r\nimport tensorflow as tf\r\nimport yaml\r\nimport os, sys\r\nimport io\r\nfrom PIL import Image\r\n# from utilities import dataset_util\r\nsys.path.append('C:/Users/xinch/Documents/Python/TensorFlow/models/research/object_detection')\r\nfrom utils import dataset_util\r\nimport hashlib\r\nfrom random import shuffle\r\n\r\nflags = tf.app.flags\r\nflags.DEFINE_string('output_path', '', 'Path to output TFRecord')\r\nflags.DEFINE_string('input_yaml', '', 'Path to labeling YAML')\r\nFLAGS = flags.FLAGS\r\n\r\nLABEL_DICT = {\r\n \"Circle\" : 0,\r\n \"Straight\" : 1,\r\n \"Left\" : 2,\r\n \"StraightLeft\" : 3,\r\n \"Right\": 4,\r\n \"Bus\":7, # in digital 3\r\n \"Pedestrian\": 8, \r\n \"Bike\": 9\r\n }\r\n # classes are saved as in bstld\r\n\r\nLABEL_DICT_R={v: k for k, v in LABEL_DICT.items()}\r\n\r\ndef create_tf_example(example):\r\n \r\n filename = example['path'] # Filename of the image. Empty if image is not from file\r\n filename = filename.encode()\r\n\r\n with tf.gfile.GFile(example['path'], 'rb') as fid:\r\n encoded_image = fid.read()\r\n encoded_jpg_io = io.BytesIO(encoded_image)\r\n image = Image.open(encoded_jpg_io)\r\n width, height = image.size\r\n key = hashlib.sha256(encoded_image).hexdigest()\r\n\r\n image_format = 'jpg'.encode() \r\n\r\n xmins = [] # List of normalized left x coordinates in bounding box (1 per box)\r\n xmaxs = [] # List of normalized right x coordinates in bounding box\r\n # (1 per box)\r\n ymins = [] # List of normalized top y coordinates in bounding box (1 per box)\r\n ymaxs = [] # List of normalized bottom y coordinates in bounding box\r\n # (1 per box)\r\n classes_text = [] # List of string class name of bounding box (1 per box)\r\n classes = [] # List of integer class id of bounding box (1 per box)\r\n\r\n for box in example['objects']:\r\n # adding box, one image may have multiple detected boxes\r\n class_id_d3=int(str(box['class_id'])[2])\r\n class_id=str(box['class_id'])[-1]\r\n class_id=int(class_id)\r\n if class_id == 3: # ignore \"StraightLeft\"\r\n continue\r\n if class_id == 9: # ignore \"Bike\"\r\n continue\r\n if box['x'] + box['width'] > width or box['y']+ box['height'] > height:\r\n continue\r\n \r\n xmins.append(float(box['x']) / width)\r\n xmaxs.append(float(box['x'] + box['width']) / width)\r\n ymins.append(float(box['y']) / height)\r\n ymaxs.append(float(box['y']+ box['height']) / height)\r\n classes_text.append((LABEL_DICT_R[class_id]).encode())\r\n # to match with bstld_label_map\r\n\r\n if class_id==0:\r\n if class_id_d3==7:\r\n #classes.append(class_id_d3)\r\n classes.append(int(6))\r\n\r\n else:\r\n classes.append(class_id+1)\r\n\r\n\r\n elif class_id==1 or class_id==2:\r\n classes.append(class_id+1)\r\n\r\n elif class_id == 4:\r\n classes.append(int(4))\r\n \r\n elif class_id == 8:\r\n classes.append(int(5))\r\n\r\n # elif class_id == 9:\r\n # classes.append(int(6))\r\n\r\n\r\n tf_example = tf.train.Example(features=tf.train.Features(feature={\r\n 'image/height': dataset_util.int64_feature(height),\r\n 'image/width': dataset_util.int64_feature(width),\r\n 'image/filename': dataset_util.bytes_feature(filename),\r\n 'image/source_id': dataset_util.bytes_feature(filename),\r\n 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),\r\n 'image/encoded': dataset_util.bytes_feature(encoded_image),\r\n 'image/format': dataset_util.bytes_feature(image_format),\r\n 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),\r\n 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),\r\n 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),\r\n 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),\r\n 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\r\n 'image/object/class/label': dataset_util.int64_list_feature(classes),\r\n }))\r\n\r\n return tf_example\r\n\r\n\r\ndef main(_):\r\n \r\n writer = tf.python_io.TFRecordWriter(FLAGS.output_path)\r\n \r\n INPUT_YAML = FLAGS.input_yaml\r\n examples = yaml.load(open(INPUT_YAML, 'rb').read())\r\n\r\n len_examples = len(examples)\r\n print(\"Loaded \", len(examples), \"examples\")\r\n\r\n # for i in range(len(examples)):\r\n # examples[i]['path'] = os.path.abspath(os.path.join(os.path.dirname(INPUT_YAML), examples[i]['path']))\r\n \r\n shuffle(examples)\r\n \r\n counter = 0\r\n\r\n for example in examples:\r\n tf_example = create_tf_example(example)\r\n writer.write(tf_example.SerializeToString())\r\n\r\n if counter % 10 == 0:\r\n print(\"Percent done\", (counter/len_examples)*100)\r\n counter += 1.\r\n writer.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n tf.app.run()\r\n","sub_path":"DTLD/DTLD_to_tfrecord_pictogram_simple_old.py","file_name":"DTLD_to_tfrecord_pictogram_simple_old.py","file_ext":"py","file_size_in_byte":4687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"424122880","text":"import collections\nimport platform\nimport usb1\nfrom usb1 import USBError\n\nG13_KEY_BYTES = collections.namedtuple('G13_KEY_BYTES', [\n 'stick_x', 'stick_y', 'keys'])\n\nKeyPress = collections.namedtuple('KeyPress', ['key_name', 'is_pressed'])\nNamedColor = collections.namedtuple('NamedColor', [\n 'name', 'red_value', 'green_value', 'blue_value'])\n\nG13_KEYS = [ # Which bit should be set\n # /* byte 3 */\n 'G01',\n 'G02',\n 'G03',\n 'G04',\n\n 'G05',\n 'G06',\n 'G07',\n 'G08',\n\n # /* byte 4 */\n 'G09',\n 'G10',\n 'G11',\n 'G12',\n\n 'G13',\n 'G14',\n 'G15',\n 'G16',\n\n # /* byte 5 */\n 'G17',\n 'G18',\n 'G19',\n 'G20',\n\n 'G21',\n 'G22',\n 'UN1', # 'UNDEF1',\n 'LST', # 'LIGHT_STATE',\n\n # /* byte 6 */\n 'BD',\n 'L1',\n 'L2',\n 'L3',\n\n 'L4',\n 'M1',\n 'M2',\n 'M3',\n\n # /* byte 7 */\n 'MR',\n 'LFT',\n 'DWN',\n 'TOP',\n\n 'UN2', # 'UNDEF2',\n 'LT1', # 'LIGHT',\n 'LT2', # 'LIGHT2',\n # 'MISC_TOGGLE',\n]\n\n\nclass LedColors(object):\n PINK = NamedColor('pink', 100, 100, 100)\n FUSCHIA = NamedColor('fuschia', 255, 100, 255)\n PURPLE = NamedColor('purple', 128, 0, 128)\n RED = NamedColor('red', 255, 0, 0)\n MAROON = NamedColor('maroon', 128, 0, 0)\n YELLOW = NamedColor('yellow', 250, 250, 0)\n GREEN = NamedColor('green', 5, 200, 5)\n LIME = NamedColor('lime', 0, 255, 0)\n AQUA = NamedColor('aqua', 0, 255, 255)\n BLUE = NamedColor('blue', 0, 128, 255)\n\n\nclass MissingG13Error(Exception):\n \"\"\"No G13 found on USB.\"\"\"\n\n\nclass G13Device(object):\n VENDOR_ID = 0x046d\n PRODUCT_ID = 0xc21c\n INTERFACE = 0\n MODE_LED_CONTROL = 0x301 # Could be 0x302?\n COLOR_CONTROL = 0x301 # Could be 0x307? Graham: Nope, so far just 0x301\n KEY_ENDPOINT = 1\n REPORT_SIZE = 8\n REQUEST_TYPE = usb1.REQUEST_TYPE_CLASS | usb1.RECIPIENT_INTERFACE\n\n LCD_WIDTH = 160\n LCD_HEIGHT = 43\n\n def __init__(self):\n # 160 across and 43 down (6 bytes down)\n self.pixels = self.get_new_buffer()\n self.device = None\n self.device_handle = None\n self.device_context = None\n self.open()\n\n def open(self):\n try:\n self.device = self._try_obtain_device()\n self._try_obtain_device_handle()\n except IOError as io_ex:\n raise io_ex # nothing we can do to recover from this\n except usb1.USBError as ex:\n if self.device is not None and self.device_handle is not None:\n self.device_handle.resetDevice()\n self._try_obtain_device_handle()\n\n # interruptRead -> R\n # controlWrite -> Out\n\n def _try_obtain_device(self):\n try:\n self.device_context = usb1.USBContext()\n self.device = self.device_context.getByVendorIDAndProductID(self.VENDOR_ID, self.PRODUCT_ID)\n\n if self.device is None:\n raise MissingG13Error()\n return self.device\n except MissingG13Error as missing_ex:\n raise missing_ex\n except Exception as ex:\n print(ex)\n raise IOError(ex, \"Did you run utils/set_perms.sh?\")\n\n def _try_obtain_device_handle(self):\n\n try:\n self.device_handle = self.device.open()\n\n except Exception as ex:\n raise IOError(ex, \"Did you run utils/set_perms.sh?\")\n\n if platform.system() == 'Linux' and \\\n self.device_handle.kernelDriverActive(self.INTERFACE):\n self.device_handle.detachKernelDriver(self.INTERFACE)\n\n self.device_handle.claimInterface(self.INTERFACE)\n\n def close(self):\n if self.device_handle is not None:\n self.device_handle.releaseInterface(self.INTERFACE)\n self.device_handle.close()\n if self.device_context is not None:\n self.device_context.exit()\n\n def get_key_press_bytes(self):\n try:\n data = None\n try:\n data = self.device_handle.interruptRead(\n endpoint=self.KEY_ENDPOINT, length=self.REPORT_SIZE, timeout=500)\n except USBError as usb_ex:\n if usb_ex.value == -7:\n pass\n if data is not None:\n print(data)\n # ord() expected string of length 1, but int found\n keys = list(map(ord, [chr(byte) for byte in data]))\n keys[7] &= ~0x80 # knock out a floating-value key\n return G13_KEY_BYTES(keys[1], keys[2], keys[3:])\n return None\n except Exception as ex:\n print(ex)\n self.close()\n raise ex\n\n def parse_keys(self):\n keys = self.get_key_press_bytes()\n if not any(keys.keys):\n return None\n key_press_bit_map = []\n for i, key in enumerate(G13_KEYS):\n b = keys.keys[int(i / 8)]\n key_press_bit_map.append(KeyPress(key, b & 1 << (i % 8)))\n return key_press_bit_map\n\n def set_led_mode(self, mode):\n try:\n data = ''.join(map(chr, [5, mode, 0, 0, 0]))\n self.device_handle.controlWrite(\n request_type=self.REQUEST_TYPE, request=9,\n value=self.MODE_LED_CONTROL, index=0, data=data.encode(),\n timeout=1000)\n except Exception as ex:\n self.close()\n\n def set_color(self, color):\n self.set_color_from_rgb(color[0], color[1], color[2])\n\n def set_color_from_named_color(self, named_color):\n self.set_color_from_rgb(named_color[1], named_color[2], named_color[3])\n\n def set_color_from_rgb(self, red_value, green_value, blue_value):\n try:\n data = ''.join(map(chr, [7, red_value, green_value, blue_value, 0]))\n self.device_handle.controlWrite(\n request_type=self.REQUEST_TYPE, request=9,\n value=self.COLOR_CONTROL, index=0, data=data.encode(),\n timeout=1000)\n except Exception as ex:\n self.close()\n\n @staticmethod\n def get_new_buffer():\n new_buffer = bytearray(992)\n new_buffer[0] = 3\n return new_buffer\n\n def update_lcd_from_pixels(self):\n try:\n self.device_handle.interruptWrite(endpoint=2, data=memoryview(self.pixels).tobytes(), timeout=1000)\n except Exception as ex:\n self.close()\n\n def update_lcd_from_buffer(self, bytesarray_buffer):\n self.pixels = bytesarray_buffer\n self.update_lcd_from_pixels()\n\n def set_pixel(self, x, y, val):\n x = min(x, 159)\n y = min(y, 43)\n idx = 32 + x + (y / 8) * 160\n if val:\n self.pixels[int(idx)] |= 1 << (y % 8)\n else:\n self.pixels[int(idx)] &= ~(1 << (y % 8))\n\n def __del__(self):\n self.close()\n","sub_path":"LikeAG13/g13_device.py","file_name":"g13_device.py","file_ext":"py","file_size_in_byte":6796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"603098016","text":"# -*- coding: utf-8 -*-\n\"\"\"\nDictionary-based Thai Word Segmentation\nusing maximal matching algorithm and Thai Character Cluster (TCC).\n\nThe code is based on the notebooks created by Korakot Chaovavanich.\n\n:See Also:\n * \\\n https://colab.research.google.com/notebook#fileId=1V1Z657_5eSWPo8rLfVRwA0A5E4vkg7SI\n * \\\n https://colab.research.google.com/drive/14Ibg-ngZXj15RKwjNwoZlOT32fQBOrBx#scrollTo=MYZ7NzAR7Dmw\n\"\"\"\nimport re\nfrom collections import defaultdict\nfrom heapq import heappop, heappush # for priority queue\nfrom typing import Generator, List\n\nfrom pythainlp.tokenize import DEFAULT_DICT_TRIE\n\nfrom .tcc import tcc_pos\nfrom .trie import Trie\n\n# To tokenize English words, for example\n_PAT_ENG = re.compile(\n r\"\"\"(?x)\n[-a-zA-Z]+| # Latin\n\\d[\\d,\\.]*| # number\n[ \\t]+| # space\n\\r?\\n # newline\n\"\"\"\n)\n\n_PAT_TWOCHARS = re.compile(\"[ก-ฮ]{,2}$\")\n\n_TEXT_LIMIT = 120\n_TEXT_SCAN_LEFT = 20\n_TEXT_SCAN_RIGHT = 20\n\n\ndef _bfs_paths_graph(\n graph: defaultdict, start: int, goal: List[int]\n) -> Generator[List[int], None, None]:\n queue = [(start, [start])]\n while queue:\n (vertex, path) = queue.pop(0)\n for next in graph[vertex]:\n if next == goal:\n yield path + [next]\n else:\n queue.append((next, path + [next]))\n\n\ndef _onecut(text: str, custom_dict: Trie) -> Generator[str, None, None]:\n graph = defaultdict(list) # main data structure\n allow_pos = tcc_pos(text) # separating position should aligned with TCC\n\n q = [0] # min-heap queue\n last_p = 0 # last position for yield\n while q[0] < len(text):\n p = heappop(q)\n\n for w in custom_dict.prefixes(text[p:]):\n p_ = p + len(w)\n if p_ in allow_pos: # only pick one that is TCC-valid\n graph[p].append(p_)\n if p_ not in q:\n heappush(q, p_)\n\n # if length == 1 means no longer ambiguous, return previous result\n if len(q) == 1:\n pp = next(_bfs_paths_graph(graph, last_p, q[0]))\n # will eventually start at last_p = pp[0]\n for p in pp[1:]:\n yield text[last_p:p]\n last_p = p\n # will eventually stop at last_p == q[0]\n\n # if length == 0 means not found in dictionary\n if len(q) == 0:\n m = _PAT_ENG.match(text[p:])\n if m: # Latin characters, numeric, space\n i = p + m.end()\n else: # as mininum skip as possible\n for i in range(p + 1, len(text)):\n if i in allow_pos: # only if TCC-valid\n ww = [\n w\n for w in custom_dict.prefixes(text[i:])\n if (i + len(w) in allow_pos)\n ]\n ww = [w for w in ww if not _PAT_TWOCHARS.match(w)]\n m = _PAT_ENG.match(text[i:])\n if ww or m:\n break\n else:\n i = len(text)\n w = text[p:i]\n graph[p].append(i)\n yield w\n last_p = i\n heappush(q, i)\n\n\ndef segment(\n text: str, custom_dict: Trie = DEFAULT_DICT_TRIE, safe_mode: bool = False\n) -> List[str]:\n \"\"\"\n Dictionary-based maximal matching word segmentation, constrained with\n Thai Character Cluster boundaries.\n\n :param str text: text to be tokenized to words\n :param pythainlp.trie.Trie custom_dict: dictionary for tokenization\n :param bool safe_mode: True to avoid long wait for long continuous text\\\n (edge case); Default is False\n :return: list of words, tokenized from the text\n \"\"\"\n if not text or not isinstance(text, str):\n return []\n\n if not custom_dict:\n custom_dict = DEFAULT_DICT_TRIE\n\n if not safe_mode:\n return list(_onecut(text, custom_dict))\n\n text_len = len(text)\n\n if text_len < (_TEXT_LIMIT + _TEXT_SCAN_RIGHT):\n # if the text is shorter than the limit,\n # tokenizes the whole text at once\n return list(_onecut(text, custom_dict))\n else:\n # if the text is longer than the limit,\n # breaks them into smaller chunks then tokenizes each chunk\n text_parts = []\n\n while text_len >= (_TEXT_LIMIT + _TEXT_SCAN_RIGHT):\n sample_start = _TEXT_LIMIT - _TEXT_SCAN_LEFT\n sample_end = _TEXT_LIMIT + _TEXT_SCAN_RIGHT\n sample = text[sample_start:sample_end]\n\n # find possible break positions\n cut_pos = sample_end\n\n # try to break by space first\n space_idx = sample.rfind(\" \")\n if space_idx >= 0:\n cut_pos = space_idx + 1\n else:\n tokens = list(_onecut(sample, custom_dict))\n token_max_idx = 0\n for i, token in enumerate(tokens):\n token_max_len = 0\n if len(token) > token_max_len:\n token_max_len = len(token)\n token_max_idx = i\n\n # choose the position that covers longest token\n cut_pos = sample_start\n for i in range(0, token_max_idx):\n cut_pos = cut_pos + len(tokens[i])\n\n text_parts.append(text[:cut_pos])\n text = text[cut_pos:]\n text_len = len(text)\n\n # append remaining text\n if text_len:\n text_parts.append(text)\n\n # tokenizes each text parts\n tokens = []\n for text_part in text_parts:\n tokens.extend(list(_onecut(text_part, custom_dict)))\n\n return tokens\n","sub_path":"pythainlp/tokenize/newmm.py","file_name":"newmm.py","file_ext":"py","file_size_in_byte":5719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"592999320","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nfrom progressbar import ProgressBar\n\nimport cifar10_input\ncifar10_input.maybe_download_and_extract()\n\n\nlearning_rate = 0.01\ntraining_epochs = 20\nbatch_size = 10\ndislay_step = 1\n\n\ndef inputs(eval_data=True):\n data_dir = os.path.join('data/cifar10_data', 'cifar-10-batches-bin')\n return cifar10_input.inputs(eval_data=eval_data,\n data_dir=data_dir,\n batch_size=batch_size)\n\n\ndef distorted_inputs():\n data_dir = os.path.join('data/cifar10_data', 'cifar-10-batches-bin')\n return cifar10_input.distorted_inputs(data_dir=data_dir,\n batch_size=batch_size)\n\n\ndef conv_batch_norm(x, n_out, phase_train):\n beta_init = tf.constant_initializer(value=0.0, dtype=tf.float32)\n gamma_init = tf.constant_initializer(value=1.0, dtype=tf.float32)\n\n beta = tf.get_variable(\"beta\", [n_out], initializer=beta_init)\n gamma = tf.get_variable(\"gamma\", [n_out], initializer=gamma_init)\n\n batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name=\"moments\")\n ema = tf.train.ExponentialMovingAverage(decay=0.9)\n ema_apply_op = ema.apply([batch_mean, batch_var])\n ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)\n\n def mean_var_with_update():\n with tf.control_dependencies([ema_apply_op]):\n return tf.identity(batch_mean), tf.identity(batch_var)\n\n mean, var = tf.cond(\n phase_train,\n mean_var_with_update,\n lambda: (ema_mean, ema_var)\n )\n\n normed = tf.nn.batch_norm_with_global_normalization(\n x,\n mean,\n var,\n beta,\n gamma,\n 1e-3,\n True\n )\n return normed\n\n\ndef layer_batch_norm(x, n_out, phase_train):\n beta_init = tf.constant_initializer(value=0.0, dtype=tf.float32)\n gamma_init = tf.constant_initializer(value=1.0, dtype=tf.float32)\n\n beta = tf.get_variable(\"beta\", [n_out], initializer=beta_init)\n gamma = tf.get_variable(\"gamma\", [n_out], initializer=gamma_init)\n\n batch_mean, batch_var = tf.nn.moments(x, [0], name=\"moments\")\n ema = tf.train.ExponentialMovingAverage(decay=0.9)\n ema_apply_op = ema.apply([batch_mean, batch_var])\n ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)\n\n def mean_var_with_update():\n with tf.control_dependencies([ema_apply_op]):\n return tf.identity(batch_mean), tf.identity(batch_var)\n\n mean, var = tf.cond(\n phase_train,\n mean_var_with_update,\n lambda: (ema_mean, ema_var)\n )\n\n x_r = tf.reshape(x, [-1, 1, 1, n_out])\n normed = tf.nn.batch_norm_with_global_normalization(\n x_r,\n mean,\n var,\n beta,\n gamma,\n 1e-3,\n True\n )\n return tf.reshape(normed, [-1, n_out])\n\n\ndef conv2d(input, weight_shape, bias_shape, phase_train, visualize=False):\n weight_prod = weight_shape[0] * weight_shape[1] * weight_shape[2]\n weight_init = tf.random_normal_initializer(stddev=(2.0/weight_prod)**0.5)\n W = tf.get_variable(\"W\",\n weight_shape,\n initializer=weight_init)\n\n bias_init = tf.constant_initializer(value=0)\n b = tf.get_variable(\"b\",\n bias_shape,\n initializer=bias_init)\n logits = tf.nn.bias_add(\n tf.nn.conv2d(input, W, strides=[1, 1, 1, 1], padding='SAME'),\n b\n )\n return tf.nn.relu(conv_batch_norm(logits, weight_shape[3], phase_train))\n\n\ndef max_pool(input, k=2):\n return tf.nn.max_pool(input,\n ksize=[1, k, k, 1],\n strides=[1, k, k, 1],\n padding='SAME')\n\n\ndef layer(input, weight_shape, bias_shape, phase_train):\n weight_stdev = (2.0/weight_shape[0])**0.5\n weight_init = tf.random_normal_initializer(stddev=weight_stdev)\n bias_init = tf.constant_initializer(value=0)\n W = tf.get_variable(\n \"W\",\n weight_shape,\n initializer=weight_init\n )\n b = tf.get_variable(\n \"b\",\n bias_shape,\n initializer=bias_init\n )\n logits = tf.matmul(input, W) + b\n return tf.nn.relu(\n layer_batch_norm(logits, weight_shape[1], phase_train)\n )\n\n\ndef inference(x, keep_prob, phase_train):\n\n with tf.variable_scope(\"conv_1\"):\n conv_1 = conv2d(x, [5, 5, 3, 64], [64], phase_train)\n pool_1 = max_pool(conv_1)\n\n with tf.variable_scope(\"conv_2\"):\n conv_2 = conv2d(pool_1, [5, 5, 64, 64], [64], phase_train)\n pool_2 = max_pool(conv_2)\n\n with tf.variable_scope(\"fc_1\"):\n dim = 1\n for d in pool_2.get_shape()[1:].as_list():\n dim *= d\n\n pool_2_flat = tf.reshape(pool_2, [-1, dim])\n fc_1 = layer(pool_2_flat, [dim, 384], [384], phase_train)\n fc_1_drop = tf.nn.dropout(fc_1, keep_prob)\n\n with tf.variable_scope(\"fc_2\"):\n fc_2 = layer(fc_1_drop, [384, 192], [192], phase_train)\n # apply dropout\n fc_2_drop = tf.nn.dropout(fc_2, keep_prob)\n\n with tf.variable_scope(\"output\"):\n output = layer(fc_2_drop, [192, 10], [10], phase_train)\n return output\n\n\ndef loss(output, y):\n print(output, y)\n xentropy = tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=y)\n loss = tf.reduce_mean(xentropy)\n return loss\n\n\ndef training(cost, global_step):\n tf.summary.scalar(\"cost\", cost)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n train_op = optimizer.minimize(cost, global_step=global_step)\n return train_op\n\n\ndef evaluate(output, y):\n correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y, 1))\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.summary.scalar(\"validation\", accuracy)\n return accuracy\n\n\npb = ProgressBar()\ntotal = 0\nfor i in pb(range(10)):\n total += i\n\nwith tf.Graph().as_default():\n # cifar10_input data image of shape 24 * 24 * 3 (color)\n x = tf.placeholder(tf.float32, name=\"x\", shape=[None, 24, 24, 3])\n y = tf.placeholder(tf.float32, name=\"y\", shape=[None])\n keep_prob = tf.placeholder(tf.float32)\n phase_train = tf.placeholder(tf.bool)\n with tf.variable_scope(\"mlp_model\"):\n output = inference(x, 0.5, phase_train)\n cost = loss(output, y)\n\n global_step = tf.Variable(0, name='global_step', trainable=False)\n train_op = training(cost, global_step)\n eval_op = evaluate(output, y)\n\n summary_op = tf.summary.merge_all()\n saver = tf.train.Saver()\n\n distorted_images, distorted_labels = distorted_inputs()\n val_images, val_labels = inputs()\n\n # sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n sess = tf.Session()\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n summary_writer = tf.summary.FileWriter(\n \"ch6-02-logistic_logs/\",\n graph_def=sess.graph_def\n )\n\n init_op = tf.initialize_all_variables()\n sess.run(init_op)\n\n valid_errors = []\n\n pb = ProgressBar()\n\n for epoch in range(training_epochs):\n avg_cost = 0.\n total_batch = int(\n cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN/batch_size\n )\n\n # Loop over all batches\n for i in pb(range(total_batch)):\n print(\"run session againt sistords ?\")\n train_x, train_y = sess.run([distorted_images, distorted_labels])\n # fit the training set\n feed_dict = {\n x: train_x,\n y: train_y,\n keep_prob: 1,\n phase_train: True\n }\n print(\"run feed dict?\")\n sess.run(train_op, feed_dict=feed_dict)\n # computer average loss\n minibach_cost = sess.run(cost, feed_dict=feed_dict)\n avg_cost += minibach_cost / total_batch\n valid_errors.append(avg_cost)\n\n # displays logs per epoch step\n if epoch % dislay_step == 0:\n val_feed_dict = {\n x: cifar10_input.validation.images,\n y: cifar10_input.validation.labels\n }\n accuracy = sess.run(eval_op, feed_dict=val_feed_dict)\n print(\"Epoch \", epoch, \"Validation Error: \", (1 - accuracy))\n summary_str = sess.run(summary_op, feed_dict=feed_dict)\n summary_writer.add_summary(summary_str, sess.run(global_step))\n\n save_path = saver.save(\n sess,\n \"ch6-02-logistic_logs/model-checkpoint\"\n )\n print(\"Model saved in file: %s\" % save_path)\n\n print(\"Optimization Finished!\")\n\n test_feed_dict = {\n x: cifar10_input.test.images,\n y: cifar10_input.test.labels\n }\n accuracy = sess.run(eval_op, feed_dict=test_feed_dict)\n print(\"Test Accuracy \", accuracy)\n\n print(\"plot the results\")\n plt.plot(np.arange(0, training_epochs, 1), valid_errors, 'ro')\n plt.ylabel('Error Incurred')\n plt.xlabel('Alpha')\n plt.show()\n","sub_path":"ch6-02-convolution-norm-net.py","file_name":"ch6-02-convolution-norm-net.py","file_ext":"py","file_size_in_byte":9036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"129366659","text":"n=int(input())\nres=[]\nfor _ in range(n):\n res.append(int(input()))\ndef findclose(num):\n if num==1:\n return 0\n k=1\n while num>pow(2,k)-1:\n if num<=pow(2,k+1)-1:\n return pow(2,k+1)-1\n k+=1\nfor h in res:\n minus=findclose(h)-h\n res=str(minus)+\" \"+str(findclose(h))\n print(res)","sub_path":"Code/CodeRecords/2668/60749/257831.py","file_name":"257831.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"29209840","text":"\"\"\"\n队列Block模式\n\"\"\"\nimport gevent\nimport gevent.monkey\nimport time\nimport datetime\nimport requests\nfrom gevent import queue\ngevent.monkey.patch_all()\n\ndata_queue = queue.Queue()\n\n\ndef push_data():\n data_list = []\n\n while True:\n item = data_queue.get()\n data_list.append(item)\n\n while len(data_list) < 30:\n try:\n item = data_queue.get(block=False)\n data_list.append(item)\n time.sleep(0.31)\n continue\n except queue.Empty:\n break\n print(\"pushed: {}\".format(data_list))\n data_list = []\n time.sleep(1)\n\n\ndef create_data():\n count = 0\n while True:\n count += 1\n if count % 100 == 0:\n time.sleep(0.2)\n data_queue.put(count)\n print(\"created: {}\".format(count))\n time.sleep(0.3)\n\n\ndef gevent_main():\n spawns = []\n spawns = [gevent.spawn(create_data), gevent.spawn(push_data)]\n\n gevent.joinall(spawns)\n\n\nif __name__ == '__main__':\n gevent_main()\n","sub_path":"Gevent/queue_with_get_block.py","file_name":"queue_with_get_block.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"150455289","text":"N = int(input())\na = list(map(int,input().split()))\nans = 0\na = sorted(a)\na = a[::-1]\nfor i in range(N):\n if i % 2 == 0:\n ans += a[i]\n else:\n ans -=a[i]\nprint(ans)","sub_path":"Python_codes/p03434/s086331937.py","file_name":"s086331937.py","file_ext":"py","file_size_in_byte":183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"229584049","text":"# 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\nfrom __future__ import annotations\nimport copy\n\nclass Solution:\n def pruneTree(self, root: TreeNode) -> TreeNode:\n\n def pruneLeave(root: TreeNode) -> TreeNode:\n if root is None:\n return None\n else:\n if root.left is None and root.right is None and root.val == 0:\n return None\n else:\n return root\n \n level = 0\n stack = [(level, root)]\n while stack != []:\n level, curr = stack.pop()\n curr = pruneLeave(curr)\n if curr is not None:\n print(level, curr.val)\n if not curr.left and not curr.right and curr.val == 0:\n curr = None\n else:\n if curr.left:\n curr.left = pruneLeave(curr.left)\n if curr.left:\n stack.append((level+1, curr.left))\n if curr.right:\n curr.right = pruneLeave(curr.right)\n if curr.right:\n stack.append((level+1, curr.right))\n if not curr.left and not curr.right and curr.val == 0:\n stack.append( (level, curr) )\n\n return root\n\nif __name__ == '__main__':\n from binarytree import TreeNode\n\n sol = Solution()\n\n input_list = [\n [0, None, 3],\n [1, None, 0, 0, 1],\n [1,0,1,0,0,0,1],\n ]\n\n for i, x in enumerate(input_list):\n print(\"=============={}==============\".format(x))\n tree = TreeNode.list2tree(x)\n #TreeNode.printTree(tree)\n\n \n tree_ = sol.pruneTree(tree)\n TreeNode.printTree_bfs(tree_)\n ","sub_path":"ex814_binary_tree_pruning/sol1.py","file_name":"sol1.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"444920650","text":"# -*- coding: utf-8 -*-\n\n# OCGUI ... Open Campus Graphical User Interface\n\nimport pygame\nfrom pygame.locals import *\nimport codecs\nimport Util\nimport sys\nimport Window\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport datetime\nimport PrototypeScene\nimport IScene\nimport MakeScene\n\nclass OCGUI:\n __dirPathOfInitInfoFiles = './init'\n __pathOfInitInfoForWindow = __dirPathOfInitInfoFiles + '/window.info'\n\n # コンストラクタ\n def __init__(self):\n # ウィンドウの初期設定を行う\n self.__initWindow()\n # シーンを作成する\n self.__scene = MakeScene.makeScene(\n IScene.SceneID.PROTOTYPE_SCENE,\n #IScene.SceneID.PLAY_GAME_SCENE ,\n self.__window\n )\n #Scene.Scene(self.__window)\n\n\n # 描画処理を行うメソッド\n def __draw(self):\n self.__window.fill() # ウィンドウの描画内容を消す\n self.__scene.draw() # 描画処理を行う\n self.__window.flip() # バッファに描画された内容を画面へ表示する\n \n\n # 更新処理を行うメソッド\n def __update(self): \n self.__scene.update() # シーンの状態を更新する\n\n\n # メインループ用のメソッド\n def do(self):\n # Sceneが終了状態出ない限りループ\n while self.__scene.isRunning():\n self.__draw() # 描画処理\n self.__update() # 更新処理 \n \n if self.__scene.isExit():\n self.__scene = MakeScene.makeScene(\n self.__scene.getNextSceneID(),\n self.__window\n )\n\n self.__clock.tick(self.__FPS) # FPS調整\n # ウィンドウを閉じる\n self.__window.quit() \n\n\n # ウィンドウの初期設定を行うメソッド\n def __initWindow(self):\n # ウィンドウに関する情報を読み込む\n windowInfoFile = open(self.__pathOfInitInfoForWindow, 'r') # ウィンドウの初期設定が記述されたファイルを開く\n windowCaption = windowInfoFile.readline() # ウィンドウのキャプションを読み込む\n windowCaption = windowCaption.strip(\"\\n\") # readlineでは改行\"\\n\"も読み込まれるので\"\\n\"を削除する\n windowSize = windowInfoFile.readline().split() # 空白区切りでウィンドウサイズを読み込む\n windowSize = [ int(windowSize[0]), int(windowSize[1]) ] # 読み込まれたデータは文字列なので各々をint型へ変換する\n windowBgColor = windowInfoFile.readline().split() # ウィンドウの画面の初期RGBを設定する\n windowFontName = windowInfoFile.readline() # 読み込まれたデータは文字列なので各々をint型へ変換する\n windowFontName = windowFontName.strip(\"\\n\") # フォントの名前を読み込む\n windowFontSize = int(windowInfoFile.readline()) # フォントのサイズを読み込む\n windowFontColor = windowInfoFile.readline().split() # フォントの色を設定する\n windowFontColor = [ int(windowFontColor[0]), # 読み込まれたデータは文字列なので各々をint型へ変換する\n int(windowFontColor[1]), \n int(windowFontColor[2]),\n ]\n windowFPS = int( (windowInfoFile.readline()).strip(\"\\n\") )# FPSを読み込む\n\n self.__window = Window.Window(windowSize[0], windowSize[1]) # ウィンドウを作成する\n self.__window.setWindowCaption(windowCaption) # ウィンドウのキャプションを作成する\n self.__window.setFont( # ウィンドウで使用されるフォントを設定する\n windowFontName, # フォント名\n windowFontSize, # フォントサイズ\n [ int(windowFontColor[0]), int(windowFontColor[1]), int(windowFontColor[2]) ] # フォントの色（R,G,B）\n )\n self.__clock = pygame.time.Clock() # FPS計算用のインスタンスを作成\n self.__FPS = windowFPS # FPSを設定する\n","sub_path":"結合テスト（仮）/Decoration(old)/OCGUI.py","file_name":"OCGUI.py","file_ext":"py","file_size_in_byte":4736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"56597075","text":"# vim: set et sw=2 :\n\n\"\"\" buildbot resource for submitting try server patches via HTTP \"\"\"\n\nfrom twisted.web.resource import Resource\n#from twisted.web.error import ErrorPage\nfrom twisted.web import http\nimport twisted.web.server\nfrom buildbot.sourcestamp import SourceStamp\nimport os, time, random\n\nDEBUG = False\n\nclass TrySubmitter(Resource):\n \"\"\" a IResource to accept submission of a patch for the try server \"\"\"\n\n def __init__(self, trydir, builders=[], userpass=None):\n Resource.__init__(self)\n self.builders = builders\n self.path = trydir\n if userpass is not None:\n assert len(userpass) is 2\n self.userpass = userpass\n\n def render(self, request):\n \"\"\" Check for HTTP basic auth, and pass to normal rendering if accepted \"\"\"\n\n try:\n self.projectName = request.site.buildbot_service.parent.projectName\n except:\n self.projectName = \"\"\n\n if self.userpass is not None:\n if request.getUser() != self.userpass[0] or request.getPassword() != self.userpass[1]:\n realm = \"%s buildbot tryserver\" % self.projectName\n\n request.setHeader('WWW-Authenticate',\n 'Basic Realm=\"%s\"' % realm)\n errpage = ErrorPage(http.UNAUTHORIZED,\n \"Unauthorized\",\n \"401 Authentication required\")\n return errpage.render(request)\n return Resource.render(self, request) \n\n def render_GET(self, request):\n request.write(\"\"\"\n \n \n Buildbot try submit\n \n \n \n
\n

%s buildbot try server request submission

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \"\"\" % (self.projectName))\n checked = 'checked=\"true\"'\n for builder in self.builders:\n request.write(\"\"\"\n \n \n \n \n \"\"\" % (builder, builder, checked, builder, builder))\n checked = \"\"\n request.write(\"\"\"\n \n
\n \n \n \"\"\")\n\n if DEBUG:\n object = request.site.buildbot_service.parent\n for i in dir(object):\n request.write(\"%s
\" % i)\n request.write(\"
%s
\" % str(object).replace(\"<\", \"<\"))\n\n request.finish()\n return twisted.web.server.NOT_DONE_YET\n\n\n def render_POST(self, request):\n request.write(\"\"\"\n \n \"\"\")\n if not request.args.has_key(\"patch\"):\n request.write(\"no patch submitted\")\n request.finish()\n return twisted.web.server.NOT_DONE_YET\n\n patch = \"\\n\".join(request.args[\"patch\"])\n \n #ss = SourceStamp(self.branch, self.baserev, (self.patchlevel, patch))\n # generate random unique build stamp id\n bsid = \"%d-%s\" % (time.time(), random.randint(0, 1000000))\n\n if request.args.has_key(\"branch\"):\n branch = str(request.args[\"branch\"][0])\n else:\n branch = None\n\n if request.args.has_key(\"rev\"):\n rev = str(request.args[\"rev\"][0])\n else:\n rev = None\n\n if request.args.has_key(\"builder\"):\n builders = request.args[\"builder\"]\n else:\n builders = self.builders\n for builder in builders:\n if not builder in self.builders:\n request.write(\"\"\"
builder %s unknown
\"\"\" % builder)\n\n patchlevel = 1 # default vaule\n if request.args.has_key(\"patchlevel\"):\n try:\n lvl = int(request.args[\"patchlevel\"][0])\n if lvl >= 0:\n patchlevel = lvl\n except (TypeError, ValueError):\n pass\n\n request.write(\"\"\"submitting patch:
\n revision %s branch %s with patchlevel %s to builders %s\n 
%s
\n \"\"\" % ((rev or \"HEAD\"), (branch or \"trunk\"), patchlevel, \", \".join(builders), patch))\n\n def ns(s):\n return \"%d:%s,\" % (len(s), s)\n \n job = \"\"\n job += ns(\"1\") # jobspec version\n job += ns(bsid) # build stamp id\n job += ns(str(branch or \"\"))\n job += ns(str(rev or \"\"))\n job += ns(\"%d\" % patchlevel)\n job += ns(patch)\n for name in builders:\n job += ns(name)\n\n if DEBUG:\n request.write(\"\"\"
%s
\"\"\" % job)\n else:\n path = os.path.join(self.path, \"new\", bsid)\n f = open(path, \"w\")\n f.write(job)\n f.close()\n\n request.finish()\n return twisted.web.server.NOT_DONE_YET\n\n\n","sub_path":"experimental/buildbot/trysubmitter.py","file_name":"trysubmitter.py","file_ext":"py","file_size_in_byte":5333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"609202349","text":"import unittest\nimport random\n\nN_RANGE = (1, 50)\nGRID_RANGE = (0, 50)\n\nclass Solution(object):\n def solution(self, s, t):\n if not s:\n return False\n if not t:\n return True\n if self.isSameTree(s, t):\n return True\n\n return self.solution(s.left, t) or self.solution(s.right, t)\n\n def isSameTree(self, p, q):\n if not p and not q:\n return True\n if not p or not q:\n return False\n if p.val != q.val:\n return False\n \n return self.isSameTree(p.left, q.left) and self.isSameTree(p.right, q.right)\n\n\nclass Test(unittest.TestCase):\n\n def __init__(self, _):\n super(Test, self).__init__(_)\n self.s = Solution()\n\n def test_example1(self):\n self.assertEqual(self.s.solution(['a','b','c','a','c','c']), 3) # ['a','a'], ['b'], ['c','c','c']\n self.assertEqual(self.s.solution(['aa','bb','ab','ba']), 4) # ['aa'], ['bb'], ['ab'], ['ba']\n self.assertEqual(self.s.solution(['abc','acb','bac','bca','cab','cba']), 3) # ['abc','cba'], ['acb','bca'], ['bac','cab']\n self.assertEqual(self.s.solution(['abcd','cdab','adcb','cbad']), 1) # ['abcd','cdab','adcb','cbad']\n\n # def test_example2(self):\n # self.assertEqual(self.s.solution([[2], [1, 2]]), 22)\n \n\ndef main():\n unittest.main()\n\nif __name__ == '__main__':\n main()","sub_path":"leetcode/572_Subtree_of_Another_Tree.py","file_name":"572_Subtree_of_Another_Tree.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"2643215","text":"import os\r\n\r\ndef cal(filename):\r\n\tf=open(filename)\r\n\tfor i in range(3):\r\n\t\tf.readline()\r\n\tsize=f.readline()\r\n\tsize=size.split(',')[2].split(':')[1][1:]\r\n\tt=f.readline()\r\n\tt=t.split(',')[0]\r\n\tf.close()\r\n\treturn (size,t)\r\n\r\n\r\nif __name__=='__main__':\r\n files=os.listdir()\r\n models=list(filter(lambda x:'.model' in x,files))\r\n models=list(map(lambda x:x.split('.')[0],models))\r\n res=dict()\r\n for i in models:\r\n \tres[i]=[[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0]]\r\n results=list(filter(lambda x:'result' in x,files))\r\n for i in results:\r\n \tname=i.split('_')\r\n \tmodel=name[0]\r\n \tstrength=int(name[2])\r\n \trepeat=int(name[3])\t\r\n \tres[model][strength-2][repeat-1]=cal(i)\r\n f=open('res.csv','w')\r\n f.write('model,2_1,2_2,2_3,3_1,3_2,3_3,4_1,4_2,4_3,5_1,5_2,5_3,6_1,6_2,6_3\\n')\r\n for key in res:\r\n \tf.write(key+',')\r\n \tvalue=res[key]\r\n \tfor i in range(5):\r\n for j in range(3):\r\n \tif not value[i][j]==0:\r\n \t\tf.write(value[i][j][0]+'/'+value[i][j][1]+',')\r\n \telse:\r\n \t\tf.write('-,')\r\n \tf.write('\\n')\r\n f.close()\r\n \r\n\r\n","sub_path":"results/issta20model/cal_old.py","file_name":"cal_old.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"122418576","text":"import logging\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')\n\n\ndef _input_path_sanitizer(func):\n def inner(trie, path):\n if not path.startswith(\"/\"):\n raise SyntaxError(\"Path must starts with a / character\")\n return func(trie, path)\n return inner\n\n\nclass _Trie(object):\n\n _paths = {}\n __end = 'end'\n\n def make_trie(self, *args):\n for path in args:\n self.add(path)\n\n @staticmethod\n def get_pieces(path):\n return path.split(\"/\")[1:]\n\n @_input_path_sanitizer\n def add(self, path):\n logging.info(\"Adding directory {}\".format(path))\n pieces = self.get_pieces(path)\n temp_trie = self._paths\n for piece in pieces:\n # if we're at the end of the trie, take out the delimiter.\n if piece in temp_trie and self.__end in temp_trie[piece]:\n del temp_trie[piece][self.__end]\n # setdefault() tries to get the key, if it exists. if it doesn't set it to the second parameter\n temp_trie = temp_trie.setdefault(piece, {})\n # set the delimiter here in all cases\n temp_trie.setdefault(self.__end, self.__end)\n\n @_input_path_sanitizer\n def exists(self, path):\n pieces = self.get_pieces(path)\n temp = self._paths\n for piece in pieces:\n if piece not in temp:\n logging.info(\"{} does not exist\".format(path))\n return False\n temp = temp[piece]\n logging.info(\"{} exists\".format(path))\n return True\n\n @_input_path_sanitizer\n def remove(self, path):\n pass # this one is a bit harder...\n\n\n @property\n def paths(self):\n return self._paths\n\n @property\n def end(self):\n return self.__end\n\n\nclass _FkDir(object):\n\n files = []\n\n def __init__(self, path):\n self.path = path\n\n\nclass _FkFile(object):\n\n def __init__(self, path, contents):\n self.path = path\n self.contents = contents\n\n\nclass FakeFs(object):\n\n def __init__(self):\n self._dirs = _Trie()\n self._paths = set(\"/\")\n\n def mkdir(self, directory):\n self._dirs.add(directory)\n self._compile_dirs()\n\n def dir_exists(self, dir_path):\n return self._dirs.exists(dir_path)\n\n def rm(self, dir_path):\n if self.dir_exists(dir_path):\n return self._dirs.remove(dir_path)\n else:\n logging.error(\"Cannot remove directory.\")\n\n def rmdir(self, directory):\n pass\n\n def touch(self, filename):\n pass\n\n def _compile_dirs(self):\n paths = set()\n for k, v in self._dirs.paths.items():\n path = [k]\n has_more = type(v) is dict\n while has_more and type(v) is not str:\n for inside_k, inside_v in v.items():\n has_more = type(v) is dict\n v = inside_v\n if inside_v != self._dirs.end:\n path.append(inside_k)\n paths.add(\"/{}\".format(\"/\".join(path)))\n self._paths = sorted(paths)\n\n @property\n def paths(self):\n return self._paths\n\nfs = FakeFs()\nfs.mkdir(\"/poo/woo/soo/doo/poo\")\nfs.mkdir(\"/etc\")\nfs.mkdir(\"/\")\nfs.mkdir(\"/tmp\")\nfs.mkdir(\"/tmp/whatever\")\nfs.mkdir(\"/var/www/mysite\")\nfs.dir_exists(\"/sadsad\")\nfs.dir_exists(\"/etc\")\nprint(fs.paths)","sub_path":"fakefs.py","file_name":"fakefs.py","file_ext":"py","file_size_in_byte":3373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"28618975","text":"from __future__ import division\nfrom scipy.ndimage import zoom\nfrom random import randint\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom datetime import datetime, timedelta, time\nfrom data_helper_functions_webapp import *\nfrom time import sleep\n\ndef return_power(month_data, day_data, hour_data, sat_to_sensor_model, sensor_to_power_mod):\n '''Input: datetime\n Output: power\n Info: also makes satellite image'''\n\n ######### Satellite image ###############\n # get sat image first, so it may be redered by computation is done\n desired_channel = 'BAND_01'\n desired_date = datetime(2014, month_data, day_data)\n desired_timedelta = timedelta(hours = hour_data)\n desired_datetime = desired_date + desired_timedelta\n satellite_filefolder = '../../data/satellite/colorado/summer6months/data/'\n sensor_filefolder = '../../data/sensor_data/colorado6months/'\n pvoutput_filefolder = '../../data/pvoutput/pvoutput6months/'\n\n #satellite data\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data = return_satellite_data(satellite_filename, satellite_filefolder)\n\n plt.figure(figsize=(8, 8))\n imgplot = plt.imshow(data)\n imgplot.set_interpolation('none')\n plt.savefig('static/images/foo.png', bbox_inches='tight') # save sat image to foo.png\n\n ############## Model for satellite to sensor ############################\n\n X = [] #sat data\n Y = [] #sensor data\n\n desired_date = (desired_datetime - timedelta(hours=6)).date() #make sure correct date\n desired_date = datetime.combine(desired_date, time.min) #get into datetime format\n\n desired_channel = 'BAND_01' #problems with an inner for loop (doesn't look good, but works)\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data1 = return_satellite_data(satellite_filename, satellite_filefolder)\n\n desired_channel = 'BAND_02'\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data2 = return_satellite_data(satellite_filename, satellite_filefolder)\n\n desired_channel = 'BAND_03'\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data3 = return_satellite_data(satellite_filename, satellite_filefolder) \n\n desired_channel = 'BAND_04'\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data4 = return_satellite_data(satellite_filename, satellite_filefolder)\n\n desired_channel = 'BAND_06'\n satellite_filename = find_filename(desired_datetime, desired_channel, satellite_filefolder)\n lons, lats, data5 = return_satellite_data(satellite_filename, satellite_filefolder)\n\n sensor_filename = find_file_from_date(desired_date, sensor_filefolder)\n df_sensor = return_sensor_data(sensor_filename, sensor_filefolder).ix[:,-15:-1]\n df_sensor[df_sensor.index == desired_datetime]\n\n Y.append(df_sensor[df_sensor.index == desired_datetime].values[0])\n X.append(np.hstack( ( np.ravel(data1) , np.ravel(data2), np.ravel(data3) , np.ravel(data4), np.ravel(data5) ) ) )\n\n X,Y = (np.array(X),np.array(Y))\n\n ####################### Make sat data useful ####################\n X_ratio_1_2 = []\n for i in xrange(X.shape[0]): #a little awkward since X is only one row, but no need to change\n CH1 = zoom(X[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)\n CH2 = X[:,1972:2476][i].reshape((14,36))\n X_ratio_1_2.append(25000* (CH2) / (CH1 + CH2+1.0) )\n X_ratio_1_2 = np.array(X_ratio_1_2)\n\n X_ratio_1_6 = []\n for i in xrange(X.shape[0]):\n CH1 = zoom(X[:,0:1972][i].reshape((29,68)),zoom=(0.48, 0.53), order=5)\n CH6 = X[:,3484:3988][i].reshape((14,36))\n X_ratio_1_6.append(25000* CH6 / (CH1 + CH6 + 0.1) )\n X_ratio_1_6 = np.array(X_ratio_1_6)\n\n X_ratio_2_6 = []\n for i in xrange(X.shape[0]):\n CH2 = X[:,1972:2476][i].reshape((14,36))\n CH6 = X[:,3484:3988][i].reshape((14,36))\n X_ratio_2_6.append(25000* CH6 / (CH2 + CH6 + 0.1) )\n X_ratio_2_6 = np.array(X_ratio_2_6)\n\n ######## change X into histogram #############\n X_hist = []\n bins = 25\n for i in xrange(X.shape[0]):\n myval1 = pd.DataFrame(np.ravel(X_ratio_1_2[i])).fillna(np.mean).values.flatten();\n myval2 = pd.DataFrame(np.ravel(X_ratio_1_6[i])).fillna(np.mean).values.flatten();\n myval3 = pd.DataFrame(np.ravel(X_ratio_2_6[i])).fillna(np.mean).values.flatten();\n\n hist1, _ = np.histogram(X[:,0:1972][i], density=True, bins=bins, range=(0,25000))\n hist2, _ = np.histogram(X[:,1972:2476][i], density=True, bins=bins, range=(0,25000))\n hist3, _ = np.histogram(X[:,2476:2980][i], density=True, bins=bins, range=(0,25000))\n hist4, _ = np.histogram(X[:,2980:3484][i], density=True, bins=bins, range=(0,25000))\n hist5, _ = np.histogram(X[:,3484:3988][i], density=True, bins=bins, range=(0,25000))\n hist6, _ = np.histogram( myval1 , density=True, bins=bins, range=(0,25000) )\n hist7, _ = np.histogram( myval2 , density=True, bins=bins, range=(0,25000))\n hist8, _ = np.histogram( myval3, density=True, bins=bins, range=(0,25000))\n X_hist.append(np.hstack((hist1,hist2,hist3,hist4,hist5,hist6,hist7,hist8)))\n\n X_hist = np.array(X_hist)\n\n #################### #Import models (run models) #######################\n\n # from sklearn.externals import joblib #joblib is sklearn's pickle\n # sat_to_sensor_model = joblib.load('models/sat-to-sensor-model/sat-to-sensor-model.pkl')\n # sensor_to_power_mod = joblib.load('models/sensor-to-power-model/sensor-to-power-model.pkl')\n\n X_sensor = sat_to_sensor_model.predict(X_hist)\n y_power = sensor_to_power_mod.predict(X_sensor)\n\n return y_power[0]\n\n\n","sub_path":"webapp/solarApp/solarApp_helper_functions.py","file_name":"solarApp_helper_functions.py","file_ext":"py","file_size_in_byte":5915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"169079571","text":"#! /usr/bin/env python\n#! /bin/env python\n# coding=utf-8\n# Source code on GitHub, link \"https://github.com/ssssssbn/cloudflare_ddns\", modified based on \"https://github.com/AmirAzodi/cloudflare_ddns\"\n# place cloudflare_ddns_lib.py, cloudflare_api.py, logger.py, cloudflare-ddns.py and cloudflare-ddns.conf on your server (e.g. /usr/local/bin/ or ~/)\n# run this command:\n# chmod +x /\"PATH_TO_FILE\"/cloudflare-ddns.py\n# open cloudflare-ddns.conf in a text editor and set the necessary parameters.\n# (One domain name, one type, one way to get IPv4/6, email address and api_key are required)\n\n#import pdb;\n\nimport os;\nimport json;\nimport re;\nimport logging;\nimport copy;\nimport time;\n\ntry:\n # For Python 3.0 and later\n from urllib.request import urlopen;\n from urllib.request import Request;\n from urllib.error import URLError;\n from urllib.error import HTTPError;\n # import urllib.parse\nexcept ImportError:\n # Fall back to Python 2's urllib2\n from urllib2 import urlopen;\n from urllib2 import Request;\n from urllib2 import HTTPError;\n from urllib2 import URLError;\n\n\nimport cloudflare_api;\nimport logger;\n\nipv4_regex = '^(\\d{1,3}\\.){4}$';\nipv6_regex = '^(([\\da-fA-F]{0,4}):){2,8}$';\nttl_range = [1, 120, 300, 600, 900, 1800, 3600, 7200, 18000, 43200, 86400];\ntype_support = ['A', 'AAAA', 'CNAME'];\n \n\npublic_ipv4 = None;\ntry_get_ipv4 = False;\npublic_ipv6 = None;\ntry_get_ipv6 = False;\nupdate = False;\ncontent_header = None;\nverify_account = False;\nconfig_file_path = None;\nconfig_file_name = 'cloudflare_ddns.conf';\n__config_file_location__ = None;\nconfig = None;\nlog_file_path = '/tmp/cloudflare_ddns';\nlog_file_name = 'cloudflare_ddns.log';\n\nclass_logger = None;\nlog = None;\n\n#pdb.set_trace();\ndef get_ipv4():\n try:\n if config['get_ipv4_by_command']:\n log.info('* Getting public IPv4 address by \"get_ipv4_by_command\"');\n result = os.popen(config['get_ipv4_by_command']).read().rstrip();\n if not re.match(ipv4_regex, result + '.'):\n log.warning('* The obtained public IPv4 address({0}) by \"get_ipv4_by_command\" is invalid, please check configured \"get_ipv4_by_command\" item'.format(\n result));\n else:\n log.info('* Succeed to get IPv4 address({0}) by \"get_ipv4_by_command\"'.format(\n result));\n return result;\n\n if config['get_ipv4_via_url']:\n log.info('* Getting public IPv4 address by \"get_ipv4_via_url\", it may take a while...');\n result = urlopen(Request(config['get_ipv4_via_url'])).read().rstrip().decode('utf-8');\n if not re.match(ipv4_regex, result + '.'):\n log.warning('* The obtained public IPv4 address({0}) by \"get_ipv4_via_url\" is invalid, please check configured \"get_ipv4_via_url\" item'.format(\n result));\n log.warning('* Unable to get public IPv4, please check configured \"get_ipv4_by_command\" and \"get_ipv4_via_url\" items');\n else:\n log.info('* Succeed to get IPv4 address({0}) by \"get_ipv4_via_url\"'.format(\n result));\n return result;\n except (Exception, URLError) as e:\n if str(e).find('Network is unreachable') != -1:\n log.error('* Ignore this message if this host does not have a public IPv4, otherwise check your network');\n else:\n log.error('* An exception occurred while getting public IPv4 address. Exception: {0}'.format(\n e));\n return None;\n\n#pdb.set_trace();\ndef get_ipv6():\n try:\n if config['get_ipv6_by_command']:\n log.info('* Getting public IPv6 address by \"get_ipv6_by_command\"');\n result = os.popen(config['get_ipv6_by_command']).read().rstrip();\n if not re.match(ipv6_regex, result + ':'):\n log.warning('* The obtained public IPv6 address({0}) by \"get_ipv6_by_command\" is invalid, please check configured \"get_ipv6_by_command\" item'.format(\n result));\n else:\n log.info('* Succeed to get IPv6 address({0}) by \"get_ipv6_by_command\"'.format(\n result));\n return result;\n\n if not got_ipv6 and config['get_ipv6_via_url']:\n log.info('* Getting public IPv6 address by \"get_ipv6_via_url\", it may take a while...');\n result = urlopen(Request(config['get_ipv6_via_url'])).read().rstrip().decode('utf-8');\n if not re.match(ipv6_regex, result + ':'):\n log.warning('* The obtained public IPv6 address({0}) by \"get_ipv6_via_url\" is invalid, please check configured \"get_ipv6_via_url\" item'.format(\n result));\n log.warning('* Unable to get public IPv6, please check configured \"get_ipv6_by_command\" and \"get_ipv6_via_url\" items');\n else:\n log.info('* Succeed to get IPv6 address({0}) by \"get_ipv6_via_url\"'.format(\n result));\n return result;\n except (Exception, URLError) as e:\n if str(e).find('Network is unreachable') != -1:\n log.error('* Ignore this message if this host does not have a public IPv6, otherwise check your network');\n else:\n log.error('* An exception occurred while getting public IPv6 address. Exception: {0}'.format(\n e));\n return None;\n\ndef get_zone_info(domain, root_domain_name, header):\n log.info('* Getting zone information for \"{0}\"'.format(\n root_domain_name));\n try:\n result = cloudflare_api.get_zone(root_domain_name, header);\n if result['success']:\n log.info('* Succeed to get zone information for \"{0}\"'.format(\n root_domain_name));\n if result['result_info']['total_count'] != 1:\n if domain['create_if_root_domain_not_exists']:\n log.info('* No active zone for \"{0}\" found, configuration \"create_if_root_domain_not_exists\" is \"True\", creating automatically '.format(\n root_domain_name));\n try:\n #pdb.set_trace();\n zone_create_json = cloudflare_api.create_zone(root_domain_name, False, 'full', header);\n if zone_create_json['success']:\n log.info('* Succeed to create zone for \"{0}\"'.format(\n root_domain_name));\n return zone_create_json;\n else:\n log.error('Failed to create zone for \"{0}\", skipping the update for this domain. Errors: {1}, messages: {2}'.format(\n root_domain_name, zone_create_json['errors'], zone_create_json['messages']));\n return None;\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while creating zone for \"{0}\", skipping the update for this domain. Exception: {1}'.format(\n root_domain_name, e));\n return None;\n else:\n log.info('* No active zone for \"{0}\" found, configuration \"create_if_root_domain_not_exists\" is \"False\", skipping the update for this domain. Please check configuration and cloudflare settings and try again'.format(\n root_domain_name));\n return None;\n else:\n return result;\n else:\n log.error('* Failed to get zone for \"{0}\", skipping the update for this domain. Errors: {1}, messages: {2}'.format(\n root_domain_name, result['errors'], result['messages']));\n return None;\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while getting zone information for: \"{0}\". Exception: {1}'.format(\n root_domain_name, e));\n return None;\n\ndef run():\n global public_ipv4;\n global try_get_ipv4;\n global public_ipv6;\n global try_get_ipv6;\n global update;\n global content_header;\n global verify_account;\n global config_file_path;\n global config_file_name;\n global __config_file_location__;\n global config;\n global log_file_path;\n global log_file_name;\n \n global class_logger;\n global log;\n\n if not config_file_path:\n config_file_path = os.path.realpath(\n os.path.join(os.getcwd(), os.path.dirname(__file__)))\n __config_file_location__ = os.path.join(config_file_path, config_file_name);\n \n log_file_location = os.path.join(log_file_path, log_file_name);\n if not os.path.exists(log_file_path):\n os.makedirs(log_file_path);\n os.popen('touch {0}'.format(log_file_location));\n elif not os.path.exists(log_file_location):\n os.popen('touch {0}'.format(log_file_location));\n #else:\n # os.popen('cat /dev/null > {0}'.format(log_file_location));\n \n if not class_logger:\n class_logger = logger.Logger('logger', \n logging.DEBUG, \n log_file_path + '/cloudflare_ddns.log', \n 'a', \n '%(asctime)s-%(levelname)s[line:%(lineno)d]: %(message)s', \n 'utf-8', \n True, \n 'D', \n 1,\n 30);\n log = class_logger.logger;\n \n log.info('------------------------------');\n \n try:\n with open(__config_file_location__, 'r') as config_file:\n try:\n config = json.loads(config_file.read());\n except (Exception, ValueError) as e:\n log.critical('* An exception occurred while loading file \"{0}\", please check if the file content conforms to the JSON format, the program exit. Exception: {1}'.format(\n __config_file_location__, e));\n exit(0);\n except Exception as e:\n log.critical('* An exception occurred while opening file \"{0}\", make sure the file exists and you have the permission to read and write it, the program exit. Exception: {1}'.format(\n __config_file_location__, e));\n exit(0);\n \n log_level = None;\n if config['log_level'] not in (0, 1, 2, 3, 4):\n update = True;\n log_level = config['log_level'] = 1;\n else:\n log_level = config['log_level'];\n \n if log_level == 0:\n class_logger.SetLogLevel(logging.DEBUG);\n elif log_level == 1:\n class_logger.SetLogLevel(logging.INFO);\n elif log_level == 2:\n class_logger.SetLogLevel(logging.WARNING);\n elif log_level == 3:\n class_logger.SetLogLevel(logging.ERROR);\n elif log_level == 4:\n class_logger.SetLogLevel(logging.CRITICAL);\n\n if not config['user']['email'] or not config['user']['api_key']:\n log.critical('* Program is unable to continue without Cloudflare authentication credentials');\n exit(0);\n \n content_header = {'X-Auth-Email': config['user']['email'],\n 'X-Auth-Key': config['user']['api_key'],\n 'Content-type': 'application/json'};\n\n\n for domain in config['domains']:\n zone_json = None;\n get_zone = False;\n new_zone = False;\n next_zone = False;\n root_domain_name = domain['root_domain_name'];\n # check to make sure domain name is specified\n if not root_domain_name:\n log.error('* Missing root_domain name, skipping the update this domain, please check configuration');\n continue;\n \n \n # get domain zone id from CloudFlare if missing\n for host in domain['hosts']:\n # check to make sure host name is specified\n # otherwise move on to the next host\n full_domain_name = None;\n if not host['sub_domain_name_prefix']:\n full_domain_name = root_domain_name;\n else:\n full_domain_name = host['sub_domain_name_prefix'] + '.' + root_domain_name;\n \n types = [];\n \n # iterate over the DNS record types\n for record in host['records']:\n type =record['type'];\n content = record['content'];\n ttl = record['ttl'];\n proxied = record['proxied'];\n \n dns_record_json = None;\n need_update = False;\n # select which IP to use based on DNS record type (e.g. A or AAAA)\n #pdb.set_trace()\n if type not in type_support:\n log.error('* Missing or wrong or unsupported DNS record type: \"{0}\", skipping the update for type \"{1}\" of \"{2}\"'.format(\n type, type, full_domain_name));\n continue;\n elif type == 'A':\n global try_get_ipv4;\n if not try_get_ipv4:\n try_get_ipv4 = True;\n public_ipv4 = get_ipv4();\n \n if record['content']:\n if re.match(ipv4_regex, record['content'] + '.'):\n content = record['content'];\n else:\n log.warning('* The content of type \"A\" DNS record of \"{0}\" does not seem to be a valid IPv4 address, skipping the update for type \"A\" DNS record of \"{1}\"'.format(\n full_domain_name, full_domain_name));\n continue;\n elif public_ipv4:\n content = public_ipv4;\n else:\n log.warning('* Unable to set type \"A\" DNS record because no IPv4 address is available, skipping the update for type \"A\" DNS record of \"{0}\"'.format(\n full_domain_name));\n continue;\n elif type == 'AAAA':\n if not try_get_ipv6:\n try_get_ipv6 = True;\n public_ipv6 = get_ipv6();\n \n if record['content']:\n if re.match(ipv6_regex, record['content'] + ':'):\n content = record['content'];\n else:\n log.warning('* The content of type \"AAAA\" DNS record of \"{0}\" does not seem to be a valid IPv6 address, skipping the update for type \"AAAA\" DNS record of \"{1}\"'.format(\n full_domain_name, full_domain_name));\n continue;\n elif public_ipv6:\n content = public_ipv6;\n else:\n log.warning('* Unable to set type \"AAAA\" DNS record because no IPv6 address is available, skipping the update for type \"AAAA\" DNS record of \"{0}\"'.format(\n full_domain_name));\n continue;\n elif type == 'CNAME':\n if record['content']:\n content = record['content'];\n else:\n log.warning('* The content of type \"{0}\" DNS record is empty, but required, using the default content({1}) to update type \"{2}\" DNS record of \"{3}\"'.format(\n type, root_domain_name, type, full_domain_name));\n content = root_domain_name;\n \n if type not in types:\n types.append(type);\n else:\n log.warning('* Type \"{0}\" DNS record repeated, skipping the update for this DNS record'.format(\n type));\n continue;\n \n #pdb.set_trace();\n if ttl not in ttl_range:\n log.warning('* TTL is invalid and must be 1(Auto), 120 2 min), 300(5 min), 600(10 min), 900(15 min), 1800(30 min), 3600(1 hr), 7200(2 hr ), 18000(5 hr), 43200(12 hr), 86400(1 day), using default value(1(Auto))');\n ttl = 1;\n \n # update ip address/ttl if it has changed since last update\n if record['cloudflare']['content'] != content:\n log.info('* The {0} of DNS recorded as type \"{1}\" on Cloudflare is different from the local {2}'.format(\n 'content' if type == 'CNAME' else 'IP address', type, 'content' if type == 'CNAME' else 'IP address'));\n need_update = True;\n if record['cloudflare']['ttl'] != ttl:\n log.info('* The TTL of DNS recorded as type \"{0}\" on Cloudflare is different from the local TTL'.format(\n type));\n need_update = True;\n \n \n if not need_update:\n continue;\n # log.info('* The IP/TTL/content of DNS recorded as type \"{0}\" on Cloudflare is different from the local public IP/TTL/content, updating type \"{1}\" DNS record for \"{2}\"'.format(\n # type, type, full_domain_name));\n #else:\n # log.info('* The IP/TTL/content of DNS recorded as type \"{0}\" on Cloudflare is the same as the local public IP/TTL/content, skipping the update for type \"{1}\" DNS record of \"{2}\"'.format(\n # type, type, full_domain_name));\n # continue;\n \n \n if not verify_account:\n log.info('* verifying user account');\n try:\n user_detail_json = cloudflare_api.get_user_detail(content_header);\n if user_detail_json['success']:\n log.info('* Succeed to verify user account');\n verify_account = True;\n else:\n log.error('* Failed to verify user account, please check user account, the program exit');\n exit(0);\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while verifying user account, the program exit. Exception: {0}'.format(\n e));\n exit(0);\n \n #get domain information from Cloudflare\n if not get_zone:\n zone_json = get_zone_info(domain, root_domain_name, content_header);\n if not zone_json:\n next_zone = True;\n break;\n elif not isinstance(zone_json['result'], list):\n new_zone = True;\n get_zone = True;\n \n \n if not need_update:\n continue;\n \n #get DNS record information from Cloudflare\n log.info('* Getting type \"{0}\" DNS record of \"{1}\"'.format(\n type, full_domain_name));\n try:\n #pdb.set_trace();\n dns_record_json = cloudflare_api.get_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], type, full_domain_name, content_header);\n if dns_record_json['success']:\n log.info('* Succeed to get type \"{0}\" DNS record of \"{1}\"'.format(\n type, full_domain_name));\n else:\n log.warning('* Failed to get type \"{0}\" DNS record of \"{1}\", skipping the update for type \"{2}\" DNS record of \"{3}\"'.format(\n type, full_domain_name, type, full_domain_name));\n continue;\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while getting type \"{0}\" DNS record of \"{1}\", skipping the update for type \"{2}\" DNS record of \"{3}\". Exception: {4}'.format(\n type, full_domain_name, type, full_domain_name, e));\n continue;\n \n \n \n try:\n if dns_record_json['result_info']['total_count'] < 1:\n if host['create_if_the_record_not_exists']:\n log.info('* No type \"{0}\" DNS record of \"{1}\" found, configuration \"create_if_the_record_not_exists\" is \"True\", creating DNS record(type: {2}, name: {3}, content: {4}, ttl: {5}) automatically'.format(\n type, root_domain_name, type, full_domain_name, content, ttl));\n try:\n dns_record_create_json = cloudflare_api.create_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], type, full_domain_name, content, ttl, proxied, content_header);\n if dns_record_create_json['success']:\n update = True;\n #dns_record_json['result'][0]['id'] = dns_record_create_json['result']['id'];\n record['cloudflare']['content'] = content;\n record['cloudflare']['ttl'] = ttl;\n record['cloudflare']['proxied'] = proxied;\n log.info('* Succeed to create DNS record(id: {0}, type: {1}, name: {2}, content: {3}, ttl: {4}, proxied: {5})'.format(\n dns_record_create_json['result']['id'], type, full_domain_name, content, ttl, proxied));\n else:\n log.warning('* Failed to create DNS record(type: {0}, name: {1}, content: {2}, ttl: {3}, proxied: {4}). Errors:{5}, messages:{6}'.format(\n type, full_domain_name, content, ttl, proxied, dns_record_create_json['errors'], dns_record_create_json['messages']));\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while creating DNS record(type: {0}, name: {1}, content: {2}, ttl: {3}, proxied: {4}), skipping the update this DNS record. Exception:{5}'.format(\n type, full_domain_name, content, ttl, proxied, e));\n else:\n log.warning('* No type \"{0}\" DNS record \"{1}\" found, configuration \"create_if_the_record_not_exists\" is \"False\", skipping the update this DNS record. Please check configuration and cloudflare settings and try again'.format(\n type, root_domain_name));\n continue;\n elif dns_record_json['result_info']['total_count'] > 1:\n if host['delete_if_the_same_type_of_record_repeated']:\n log.info('* Type \"{0}\" DNS record of \"{1}\" is not unique, configuration \"delete_if_the_same_type_of_record_repeated\" is \"True\", using the first DNS record and deleting others'.format(\n type, root_domain_name));\n for index in range(dns_record_json['result_info']['total_count']):\n if index == 0:\n log.info('* Keep the first DNS record(id: {0}, type: {1}, name: {2}, content: {3}, ttl: {4}, proxied: {5})'.format(\n dns_record_json['result'][index]['id'], dns_record_json['result'][index]['type'], dns_record_json['result'][index]['name'], dns_record_json['result'][index]['content'], dns_record_json['result'][index]['ttl'], dns_record_json['result'][index]['proxied']));\n continue;\n else:\n log.info('* Deleting DNS record(id: {0}, type: {1}, name: {2}, content: {3}, ttl: {4}, proxied: {5})'.format(\n dns_record_json['result'][index]['id'], dns_record_json['result'][index]['type'], dns_record_json['result'][index]['name'], dns_record_json['result'][index]['content'], dns_record_json['result'][index]['ttl'], dns_record_json['result'][index]['proxied']));\n try:\n dns_record_delete_json = cloudflare_api.delete_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], dns_record_json['result'][index]['id'], content_header);\n if dns_record_delete_json['success']:\n log.info('* Succeed to delete type \"{0}\" DNS record(id: {1})'.format(\n type, dns_record_delete_json['result']['id']));\n else:\n log.warning('* Failed to delete type \"{0}\" DNS record(id: {1}). Errors:{2}, messages:{3}'.format(\n type, dns_record_json['result'][index]['id'], dns_record_delete_json['errors'], dns_record_delete_json['messages']));\n break;\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while deleting type \"{0}\" DNS record(id: {1}), gave up deleting type \"{2}\" DNS record(id: {3}) for \"{4}\". Exception:{5}'.format(\n type, dns_record_json['result'][index]['id'], type, dns_record_json['result'][index]['id'], full_domain_name, e));\n break;\n else:\n log.warning('* Type \"{0}\" DNS record \"{1}\" is not unique, configuration \"delete_if_the_same_type_of_record_repeated\" is \"False\", using the first DNS record'.format(\n type, root_domain_name));\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while handling DNS records, skipping the update for type \"{0}\" DNS record of \"{1}\". Exception: {2}'.format(\n type, full_domain_name, e));\n continue;\n \n log.info('* Updating type \"{0}\" DNS record for \"{1}\"'.format(\n type, full_domain_name));\n try:\n #pdb.set_trace();\n if not record['cloudflare']['content'] and content == dns_record_json['result'][0]['content'] and ttl == dns_record_json['result'][0]['ttl'] and proxied == dns_record_json['result'][0]['proxied']:\n update = True;\n record['cloudflare']['content'] = content;\n record['cloudflare']['ttl'] = ttl;\n record['cloudflare']['proxied'] = proxied;\n log.info('* Succeed to update DNS record(id: {0}, type: {1}, name: {2}, content: {3}, ttl: {4}, proxied: {5})'.format(\n dns_record_json['result'][0]['id'], type, full_domain_name, content, ttl, proxied));\n continue;\n update_res_json = cloudflare_api.update_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], dns_record_json['result'][0]['id'], type, full_domain_name, content, ttl, proxied, content_header);\n if update_res_json['success']:\n update = True;\n record['cloudflare']['content'] = content;\n record['cloudflare']['ttl'] = ttl;\n record['cloudflare']['proxied'] = proxied;\n log.info('* Succeed to update DNS record(id: {0}, type: {1}, name: {2}, content: {3}, ttl: {4}, proxied: {5})'.format(\n update_res_json['result']['id'], type, full_domain_name, content, ttl, proxied));\n else:\n log.warning('* Failed to update type \"{0}\" DNS record(id: {1}, type: {2}, name: {3}). Errors: {4}, messages: {5}'.format(\n type, dns_record_json['result'][0]['id'], type, full_domain_name, update_res_json['errosr'], update_res_json['messages']));\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while updating DNS record(id: {0}, type: {1}, name: {2}). Exception: {3}'.format(\n dns_record_json['result'][0]['id'], type, full_domain_name, e));\n \n \n if next_zone:\n continue;\n \n \n if host['delete_the_other_unused_type_of_record']:\n log.info('* Configuration \"delete_the_other_unused_type_of_record\" is \"True\", deleting other unused type DNS record for \"{0}\" if exists'.format(\n full_domain_name));\n \n if not get_zone:\n zone_json = get_zone_info(domain, root_domain_name, content_header);\n if not zone_json:\n continue;\n elif not isinstance(zone_json['result'], list):\n new_zone = True;\n get_zone = True;\n \n #pdb.set_trace();\n delete_type = copy.deepcopy(type_support);\n for type in types:\n try:\n del delete_type[delete_type.index(type)];\n except Exception:\n pass;\n if len(delete_type) > 0:\n for type in delete_type:\n try:\n type_record_json = cloudflare_api.get_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], type, full_domain_name, content_header);\n if type_record_json['success']:\n for delete_record_json in type_record_json['result']:\n log.info('* Deleting type \"{0}\" DNS record(id: {1})'.format(\n type, delete_record_json['id']));\n try:\n type_record_delete_json = cloudflare_api.delete_dns_record(zone_json['result']['id'] if new_zone else zone_json['result'][0]['id'], delete_record_json['id'], content_header);\n if type_record_delete_json['success']:\n log.info('* Succeed to delete type \"{0}\" DNS record(id: {1})'.format(\n type, delete_record_json['id']));\n else:\n log.warning('* Failed to delete type \"{0}\" DNS record(id: {1}). Errors: {2}, messages: {3}'.format(\n type, delete_record_json['id'], type_record_delete_json['errors'], type_record_delete_json['messages']));\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while deleting type \"{0}\" DNS record(id: {1}), gave up deleting this DNS record. Exception: {2}'.format(\n type, delete_record_json['id'], e));\n else:\n log.warning('* Failed to get type \"{0}\" DNS records for \"{1}\", gave up deleting DNS records'.format(\n type, full_domain_name));\n except (Exception, HTTPError) as e:\n log.error('* An exception occurred while deleting type \"{0}\" DNS records, gave up deleting type \"{1}\" DNS records. Exception: {2}'.format(\n type, type, e));\n \n host['delete_the_other_unused_type_of_record'] = False;\n update = True;\n \n \n \n # if any DNS records were updated, update the config file accordingly\n if update:\n try:\n with open(__config_file_location__, 'w') as config_file:\n json.dump(config, config_file, indent = 1, sort_keys = True);\n except Exception as e:\n log.error('* An exception occurred while writing the configuration to the file \"{0}\". Exception: {1}'.format(\n __config_file_location__, e));\n log.info('* Updates completed. Bye.');\n else:\n log.info('* Nothing to update. Bye.');\n \n public_ipv4 = None;\n try_get_ipv4 = False;\n public_ipv6 = None;\n try_get_ipv6 = False;\n update = False;\n\nif __name__ == '__main__':\n try:\n while True:\n run();\n #pdb.set_trace();\n if config['check_interval']:\n time.sleep(config['check_interval']);\n else:\n break;\n except Exception as e:\n log.error('* An exception occurred while running, the program exit. Exception: {0}'.format(\n e));\n exit(0);\n","sub_path":"cloudflare_ddns.py","file_name":"cloudflare_ddns.py","file_ext":"py","file_size_in_byte":28597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"336650356","text":"import numpy as np\nimport scipy.io, math, sys, time\nfrom matplotlib import pyplot\n\nfrom utils import Utils\nfrom statistics import Statistics\nfrom features import Features\nfrom reader import Reader\nfrom plots import Plots\n\n\n\nclass LogisticRegression:\n\t\"\"\"\n\tSimple Logistic Regression with OneVsAll methods\n\t\"\"\"\n\n\tdef __init__(self, nb_input, nb_output, nb_features):\n\t\t\"\"\"\n\t\t:param nb_input: number of input samples\n\t\t:param nb_output: number of classes\n\t\t:param nb_features: number of features\n\t\t\"\"\"\n\n\t\tself.nb_input = nb_input\n\t\tself.nb_output = nb_output\n\t\tself.nb_features = nb_features\n\n\t\t# Weights:\n\t\tself.weights = np.zeros((nb_features, 1))\n\t\tself.all_weights = np.zeros((nb_output, nb_features))\n\n\t\t# Gradients:\n\t\tself.gradients = np.zeros((nb_features, 1))\n\n\n\n\tdef initialize_weights(self):\n\t\tepsilon = Utils.get_epsilon(self.nb_features, 1)\n\t\tself.weights = np.random.randn(self.nb_features, 1) * 2 * epsilon - epsilon\n\n\n\tdef set_weights(self, w):\n\t\tself.weights = w\n\n\n\tdef get_weights(self):\n\t\treturn self.weights\n\n\n\tdef predict_classes(self, X):\n\t\treturn np.argmax(X.dot(self.all_weights.T), axis=1)\n\n\t\n\tdef predict_binary(self, X):\n\t\treturn Utils.sigmoid(X.dot(self.weights)) >= Utils.sigmoid(0)\n\n\n\tdef predict(self, X):\n\t\tif self.nb_output > 2:\n\t\t\treturn self.predict_classes(X_norm)\n\t\telse:\n\t\t\treturn self.predict_binary(X_norm)\n\n\n\tdef cost(self, X, Y, lbda):\n\n\n\t\tm, n = X.shape\n\t\t\n\t\tZ = Utils.sigmoid(X.dot(self.weights))\n\t\t\n\t\tA = np.log(Z).T.dot(Y)\n\t\tB = np.log(1 - Z).T.dot(1 - Y)\n\t\tJ = -(A+B)/m\n\n\n\t\t# regularization (not for bias):\n\t\tJ += (lbda * np.sum(np.power(self.weights[1:], 2)))/(2.0*m)\n\n\t\t\n\t\t# get gradients\n\t\tgrad = np.zeros(self.weights.shape)\n\t\tgrad[0] = X[:,0].T.dot(Z - Y)/m\n\t\tgrad[1:] = (X[:, 1:].T.dot(Z - Y)/m) + (lbda*self.weights[1:])/m\n\n\t\treturn np.sum(J), grad\n\n\n\tdef update_weights(self, grad, alpha):\n\t\tself.weights = self.weights - alpha*grad\n\n\n\n\t\n\tdef train_binary(self, X, Y, alpha, lbda, precision, nb_iters, verbose=True):\n\n\t\tdef reached_precision(cost, precision):\n\t\t\tif len(cost) < 2:\n\t\t\t\treturn True\n\t\t\treturn abs(cost[-1] - cost[-2]) > precision\n\n\t\tcost_history = []\n\t\ti = 0\n\n\n\n\t\twhile i < nb_iters and reached_precision(cost_history, precision):\n\t\t\tJ, G = self.cost(X, Y, lbda)\n\t\t\tself.update_weights(G, alpha)\n\t\t\tcost_history.append(J)\n\n\t\t\tif verbose:\n\t\t\t\tsys.stdout.write(\"Iteration %4d - Cost %.4f \\r\" % (i+1, J))\n\t\t\t\tsys.stdout.flush()\n\n\t\t\ti += 1\n\n\t\tif verbose:\n\t\t\tprint(\"\\n\"+\"-\"*30)\n\t\treturn cost_history\n\n\n\tdef train_classes(self, X, Y, alpha, lbda, precision, nb_iters, verbose=True):\n\n\t\tm, n = X.shape\n\t\tcost_history = []\n\n\t\tfor k in range(self.nb_output):\n\n\t\t\tself.initialize_weights()\n\t\t\tJ = self.train_binary(X, Y == k, alpha, lbda, precision, nb_iters, verbose)\n\n\t\t\tself.all_weights[k] = self.weights[:,0]\n\t\t\tcost_history.append(J)\n\t\t\t\n\n\t\treturn cost_history\n\n\n\tdef train(self, X, Y, alpha=0.3, lbda=0.5, precision=1e-6, nb_iters=10000, verbose=True):\n\t\tif self.nb_output > 2:\n\t\t\treturn self.train_classes(X, Y, alpha, lbda, precision, nb_iters, verbose)\n\t\telse:\n\t\t\treturn self.train_binary(X, Y, alpha, lbda, precision, nb_iters, verbose)\n\n\n\nif __name__ == '__main__':\n\n\n\t# X, Y = Reader.read_data('dados/xor.txt', ignore_line_number=False)\n\n\t# data = {'X':X, 'Y':Y}\n\t# Reader.save_mat('dados/xor.mat', data)\n\n\tmat = Reader.load_mat('dados/xor.mat')\n\tX_orig, Y = np.matrix(mat['X']), mat['Y']\n\n\t# X, mu, sigma = Features.normalize(X)\n\tPlots.scatterplot(X_orig, Y)\n\tpyplot.show()\n\n\tmax_degree = 60\n\titers, times, accs, alphas = [], [], [], []\n\n\n\tresult_times = open(\"reg_result_times.txt\", \"w\")\n\tresult_times.write(\"alpha tempo\\n\")\n\n\tresult_iters = open(\"reg_result_iters.txt\", \"w\")\n\tresult_iters.write(\"alpha iters\\n\")\n\n\tresult_accs = open(\"reg_result_accs.txt\", \"w\")\n\tresult_accs.write(\"alpha acc\\n\")\n\n\tresult_alphas = open(\"reg_result_alphas.txt\", \"w\")\n\tresult_alphas.write(\"alpha custo\\n\")\n\n\tlalphas = [0.01, 0.03, 0.1, 0.3, 1.0, 3.0, 6.0, 12.0, 24.0, 48.0]\n\n\tfor nb_degree in range(1, max_degree+1):\n\t\tfor k, alpha in enumerate(lalphas):\n\n\t\t\tX = Utils.add_column_with_ones(Features.map(X_orig, degree=nb_degree))\n\n\t\t\tvalidation_split = 0.2\n\t\t\tuse_shuffle = False\n\t\t\tuse_validation = False\n\n\t\t\tnb_input = X.shape[0]\n\t\t\tnb_features = X.shape[1]\n\t\t\tnb_labels = len(set(np.squeeze(np.asarray(Y))))\n\n\t\t\tnb_iters = 50000\n\t\t\tnb_epochs = 1\n\n\t\t\t# alpha\t = 3.0\n\t\t\tlbda\t = 0.0\n\t\t\tmomentum = 0.9\n\t\t\tprecision = 1e-6\n\n\t\t\ttimer \t = time.clock if (sys.platform == 'win32') else time.time\n\n\n\n\t\t\tlg = LogisticRegression(nb_input, nb_labels, nb_features)\n\n\t\t\ta,b,c,d = [],[],[],[]\n\t\t\tfor i in range(nb_epochs):\n\t\t\t\t\n\n\t\t\t\t# print('Epoch %d' % (i+1))\n\t\t\t\t# print(\"-\"*30)\n\n\t\t\t\tsys.stdout.write(\"Degree %2d - Alpha %2.2lf - Epoch %2d \\r\" % (nb_degree, alpha, i+1))\n\t\t\t\tsys.stdout.flush()\n\n\t\t\t\tstart_time = timer()\n\t\t\t\tlg.initialize_weights()\n\t\t\t\tj_history = lg.train(X, Y, alpha, lbda, precision, nb_iters, verbose=False)\t\n\t\t\t\ttotal_time = timer() - start_time\n\t\t\t\t\n\t\t\t\ta.append(len(j_history))\n\t\t\t\tb.append(total_time)\n\t\t\t\tc.append(Statistics.accuracy(lg.predict_binary(X), Y))\n\t\t\t\td = j_history\n\n\t\t\t\t# print('Iterations:\\t %d' % a[-1])\n\t\t\t\t# print('Time:\\t\\t %.2f seconds' % b[-1])\n\t\t\t\t# print('Accuracy:\\t %.2f' % c[-1])\n\n\t\t\t\t# f1, precision, recall, acc = Statistics.f_score(lg.predict_binary(X), Y)\n\t\t\t\t# print('F1:\\t %.2f' % f1)\n\t\t\t\t# print('Prec:\\t %.2f' % precision)\n\t\t\t\t# print('Rec:\\t %.2f' % recall)\n\t\t\t\t# print('Acc:\\t %.2f' % acc)\n\n\t\t\t\t# print('\\n')\n\n\t\t\t\t# if nb_degree == 2:\n\t\t\t\t# \tcolors = \"gbrcmyk\"*2\n\t\t\t\t# \tPlots.lineplot(list(range(len(d))), d, color=colors[k], label='Alpha {}'.format(alpha))\n\t\t\t\t# Plots.draw_boundary(X[:, 1:], lg.get_weights(), degree=nb_degree)\n\n\t\t\tma, da = np.mean(np.array(a)), np.std(np.array(a))\n\t\t\tmb, db = np.mean(np.array(b)), np.std(np.array(b))\n\t\t\tmc, dc = np.mean(np.array(c)), np.std(np.array(c))\n\t\t\tmd, dd = np.mean(np.array(d)), np.std(np.array(d))\n\n\t\t\titers.append(ma)\n\t\t\ttimes.append(mb)\n\t\t\taccs.append(mc)\n\t\t\talphas.append(md)\n\n\t\t\tresult_iters.write(\"%.2f %.4f\\n\" % (alpha, ma))\n\t\t\tresult_times.write(\"%.2f %.4f\\n\" % (alpha, mb))\n\t\t\tresult_accs.write(\"%.2f %.4f\\n\" % (alpha, mc))\n\t\t\tresult_alphas.write(\"%.2f %.4f\\n\" % (alpha, md))\n\n\n\n\t\t\tprint('Degree %d - %d' % (nb_degree, nb_features))\n\t\t\tprint(\"-\"*30)\n\t\t\tprint('Iterations:\\t media:%.2lf \\t desvio:%.2f' % (ma, da))\n\t\t\tprint('Time:\\t\\t media:%.2lf \\t desvio:%.2f seconds' % (mb, db))\n\t\t\tprint('Accuracy:\\t media:%.2lf \\t desvio:%.2f' % (mc, dc))\n\t\t\tprint('\\n')\n\n\n\tpyplot.legend(loc='upper right', numpoints=1, shadow=True, fancybox=True)\n\tpyplot.show()\n\n\tpyplot.xlabel('Taxa de aprendizagem')\n\tpyplot.ylabel('J(W)')\n\tPlots.lineplot(lalphas, alphas, color='g', label='Custo')\n\tpyplot.legend(loc='upper right', numpoints=1, shadow=True, fancybox=True)\n\tpyplot.show()\n\n\tPlots.lineplot(lalphas, times, color='g', label='Times')\n\tpyplot.legend(loc='upper right', numpoints=1, shadow=True, fancybox=True)\n\tpyplot.show()\n\n\tPlots.lineplot(lalphas, iters, color='b', label='Iterations')\n\tpyplot.legend(loc='upper right', numpoints=1, shadow=True, fancybox=True)\n\tpyplot.show()\n\t\n\n","sub_path":"Machine Learning/Trabalho1/reglog.py","file_name":"reglog.py","file_ext":"py","file_size_in_byte":7034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"87043544","text":"#!/usr/bin/env python\n# coding=utf-8\n'''\nAuthor: Wei Luo\nDate: 2020-08-21 18:02:38\nLastEditors: Wei Luo\nLastEditTime: 2020-08-31 13:53:51\nNote: Note\n'''\nfrom .traj_gen_base import TrajGen\nimport numpy as np\nfrom scipy.linalg import block_diag, solve\nimport mosek as mk\nimport mosek.fusion as mkfs\nfrom qpsolvers import solve_qp\nimport sys\nimport casadi as ca\nimport time\n\n\nclass PolyTrajGen(TrajGen):\n def __init__(self, knots_, order_, algo_, dim_, maxContiOrder_):\n \"\"\" Initialize the class of the trajectory generator\"\"\"\n super().__init__(knots_, dim_)\n self.N = order_ # polynomial order\n self.algorithm = algo_\n\n self.M = knots_.shape[0] - 1 # segments which is knots - 1\n self.maxContiOrder = maxContiOrder_\n self.num_variables = (self.N+1) * self.M\n self.inf = np.inf\n self.segState = np.zeros((self.M, 2)) # 0 dim -> how many fixed pins in this segment,\n # muss smaller than the polynomial order+1\n # more fixed pins (higher order) will be ignored.\n # 1 dim -> continuity degree. should be defined by\n # user (maxContiOrder_+1)\n self.polyCoeffSet = np.zeros((self.dim, self.N+1, self.M))\n\n ## math functions\n def scaleMat(self, delT):\n mat_ = np.diag([delT**i for i in range(self.N+1)])\n return mat_\n\n def scaleMatBigInv(self,):\n mat_ = None\n for m in range(self.M):\n matSet_ = self.scaleMat(1/(self.Ts[m+1]-self.Ts[m]))\n if mat_ is None:\n mat_ = matSet_.copy()\n else:\n mat_ = block_diag(mat_, matSet_)\n return mat_\n\n ## functional definition\n def setDerivativeObj(self, weights):\n if weights.shape[0] > self.N:\n print(\"Order of derivative objective > order of poly. Higher terms will be ignored.\")\n self.weight_mask = weights[:self.N]\n else:\n self.weight_mask = weights\n\n def addPin(self, pin_):\n t_ = pin_['t']\n X_ = pin_['X']\n super().addPin(pin_)\n m, _ = self.findSegInteval(t_)\n if len(X_.shape) == 2: # 2 dimension ==> loose pin\n if m in self.loosePinSet.keys():\n self.loosePinSet[m].append(pin_)\n else:\n self.loosePinSet[m] = [pin_]\n elif len(X_.shape) == 1: # vector ==> fix pin\n assert (t_==self.Ts[m] or t_==self.Ts[-1]), 'Fix pin should be imposed only knots'\n if self.segState[m, 0] <= self.N+1:\n if m in self.fixPinSet.keys():\n self.fixPinSet[m].append(pin_)\n self.fixPinOrder[m].append(pin_['d'])\n else:\n self.fixPinSet[m] = [pin_]\n self.fixPinOrder[m] = [pin_['d']]\n self.segState[m, 0] += 1\n\n else:\n print('FixPin exceed the dof of this segment. Pin ignored')\n else:\n print('Dim of pin value is invalid')\n\n\n def nthCeoff(self, n, d):\n \"\"\" Returns the nth order ceoffs (n=0...N) of time vector of d-th\n derivative.\n\n Args:\n n(int): target order\n d(int): order derivative\n\n Returns:\n val_: n-th ceoffs\n \"\"\"\n if d == 0:\n val_ = 1\n else:\n accumProd_ = np.cumprod(np.arange(n, n-d, -1))\n val_ = accumProd_[-1]*(n>=d)\n return val_\n\n def IntDerSquard(self, d):\n \"\"\"\n {x^(d)(t)}^2 = (tVec(t,d)'*Dp)'*(tVec(t,d)'*Dp)\n\n Args:\n d(int): order derivative\n\n Returns:\n mat_: matrix of the cost function\n \"\"\"\n mat_ = np.zeros((self.N+1, self.N+1))\n if d > self.N:\n print(\"Order of derivative > poly order, return zeros-matrix \\n\")\n for i in range(d, self.N+1):\n for j in range(d, self.N+1):\n # if i+j-2*d+1 > 0:\n mat_[i,j] = self.nthCeoff(i, d) * self.nthCeoff(j, d) / (i+j-2*d+1)\n return mat_\n\n def findSegInteval(self, t_):\n idx_ = np.where(self.Ts<=t_)[0]\n if idx_.shape[0]>0:\n m_ = np.max(idx_)\n if m_ >= self.M:\n if t_ != self.Ts[-1]:\n print('Eval of t : geq TM. eval target = last segment')\n m_ = self.M-1\n else:\n print('Eval of t : leq T0. eval target = 1st segment')\n m_ = 0\n tau_ = (t_-self.Ts[m_])/(self.Ts[m_+1]-self.Ts[m_])\n return m_, tau_\n\n def tVec(self, t_, d_):\n # time vector evaluated at time t with d-th order derivative.\n vec_ = np.zeros((self.N+1, 1))\n for i in range(d_, self.N+1):\n vec_[i] = self.nthCeoff(i, d_)*t_**(i-d_)\n return vec_\n\n def fixPinMatSet(self, pin):\n t_ = pin['t']\n X_ = pin['X']\n d_ = pin['d']\n m_, tau_ = self.findSegInteval(t_)\n idxStart_ = m_*(self.N+1)\n idxEnd_ = (m_+1)*(self.N+1)\n dTm_ = self.Ts[m_+1] - self.Ts[m_]\n aeqSet_ = np.zeros((self.dim, self.num_variables))\n beqSet_ = np.zeros((self.dim, 1))\n for dd in range(self.dim):\n aeqSet_[dd, idxStart_:idxEnd_] = self.tVec(tau_, d_).flatten()/dTm_**d_#\n beqSet_[dd] = X_[dd]\n return aeqSet_, beqSet_\n\n def contiMat(self, m_, dmax):\n \"\"\"\n ensure in dmax derivative degree the curve should be continued.\n from 0 to dmax derivative\n Args:\n m_: index of the segment <= M-1\n dmax: max conti-degree\n \"\"\"\n dmax_ = int(dmax)\n aeq_ = np.zeros((dmax_+1, self.num_variables))\n beq_ = np.zeros((dmax_+1, 1)) # different of the eq should be zero\n idxStart_ = m_*(self.N+1)\n idxEnd_ = (m_+2)*(self.N+1) # end of the next segment\n dTm1_ = self.Ts[m_+1] - self.Ts[m_]\n dTm2_ = self.Ts[m_+2] - self.Ts[m_+1]\n for d in range(dmax_+1):\n # the end of the first segment should be the same as the begin of the next segment at each derivative\n aeq_[d, idxStart_:idxEnd_] = np.concatenate((self.tVec(1, d)/dTm1_**d, - self.tVec(0, d)/dTm2_**d), axis=0).flatten() #\n\n return aeq_, beq_\n\n def loosePinMatSet(self, pin_):\n aSet_ = np.zeros((self.dim, 2, self.num_variables))\n bSet_ = np.zeros((self.dim, 2, 1))\n t_ = pin_['t']\n X_ = pin_['X']\n d_ = pin_['d']\n m_, tau_ = self.findSegInteval(t_)\n dTm_ = self.Ts[m_+1] - self.Ts[m_]\n idxStart_ = m_*(self.N+1)\n idxEnd_ = (m_+1)*(self.N+1)\n for dd in range(self.dim):\n aSet_[dd, :, idxStart_:idxEnd_] = np.array([self.tVec(tau_, d_)/dTm_**d_,-self.tVec(tau_, d_)/dTm_**d_]).reshape(2, -1) #\n bSet_[dd, :] = np.array([X_[dd, 1], -X_[dd, 0]]).reshape(2, -1)\n return aSet_, bSet_\n\n\n def getQPset(self,):\n # objective\n QSet = np.zeros((self.dim, self.num_variables, self.num_variables))\n for dd in range(self.dim):\n Q_ = np.zeros((self.num_variables, self.num_variables))\n\n for d in range(1, self.weight_mask.shape[0]+1):\n if self.weight_mask[d-1] > 0:\n Qd_ = None\n for m in range(self.M):\n dT_ = self.Ts[m+1] - self.Ts[m]\n Q_m_ = self.IntDerSquard(d)/dT_**(2*d-1)\n if Qd_ is None:\n Qd_ = Q_m_.copy()\n else:\n Qd_ = block_diag(Qd_, Q_m_)\n Q_ = Q_ + self.weight_mask[d-1]*Qd_\n QSet[dd] = Q_\n\n # constraint\n AeqSet = None\n ASet = None\n BSet = None\n BeqSet = None\n\n for m in range(self.M): # segments\n ## fix pin\n if m in self.fixPinSet.keys():\n for pin in self.fixPinSet[m]:\n aeqSet, beqSet = self.fixPinMatSet(pin)\n if AeqSet is None:\n AeqSet = aeqSet.reshape(self.dim, -1, self.num_variables)\n BeqSet = beqSet.reshape(self.dim, -1, 1)\n else:\n AeqSet = np.concatenate((AeqSet, aeqSet.reshape(self.dim, -1, self.num_variables)), axis=1)\n BeqSet = np.concatenate((BeqSet, beqSet.reshape(self.dim, -1, 1)), axis=1)\n\n ## continuity\n if m < self.M-1:\n contiDof_ = min(self.maxContiOrder+1, self.N+1-self.segState[m, 0])\n self.segState[m, 1] = contiDof_\n if contiDof_ != self.maxContiOrder+1:\n print('Connecting segment ({0},{1}) : lacks {2} dof for imposed {3} th continuity'.format(m, m+1, self.maxContiOrder-contiDof_, self.maxContiOrder))\n if contiDof_ >0:\n aeq, beq = self.contiMat(m, contiDof_-1)\n AeqSet = np.concatenate((AeqSet, aeq.reshape(1, -1, self.num_variables).repeat(self.dim, axis=0)), axis=1)\n BeqSet = np.concatenate((BeqSet, beq.reshape(1, -1, 1).repeat(self.dim, axis=0)), axis=1)\n else:\n pass # not pin in this interval\n\n ## loose pin\n if m in self.loosePinSet.keys():\n for pin in self.loosePinSet[m]:\n aSet, bSet = self.loosePinMatSet(pin)\n if ASet is None:\n ASet = aSet\n BSet = bSet\n else:\n ASet = np.concatenate((ASet, aSet), axis=1)\n BSet = np.concatenate((BSet, bSet), axis=1)\n\n\n return QSet, ASet, BSet, AeqSet, BeqSet\n\n def coeff2endDerivatives(self, Aeq_):\n assert Aeq_.shape[1] <= self.num_variables, 'Pin + continuity constraints are already full. No dof for optim.'\n mapMat_ = Aeq_.copy()\n for m in range(self.M):\n freePinOrder_ = np.setdiff1d(np.arange(self.N+1), self.fixPinOrder[m]) # free derivative (not defined by fixed pin)\n dof_ = self.N+1 - np.sum(self.segState[m])\n freeOrder = freePinOrder_[:int(dof_)]\n for order in freeOrder:\n virtualPin_ = {'t':self.Ts[m], 'X':np.zeros((self.dim, 1)), 'd':order}\n # print('virtual Pin {}'.format(virtualPin_))\n aeqSet_, _ = self.fixPinMatSet(virtualPin_)\n aeq_ = aeqSet_[0] # only one dim is taken.\n mapMat_ = np.concatenate((mapMat_, aeq_.reshape(-1, self.num_variables)), axis=0)\n return mapMat_\n\n def mapQP(self, QSet_, ASet_, BSet_, AeqSet_, BeqSet_):\n Afp_ = self.coeff2endDerivatives(AeqSet_[0]) # sicne all Aeq in each dim are the same\n AfpInv_ = np.linalg.inv(Afp_)\n Nf_ = int(AeqSet_[0].shape[0])\n Qtemp_ = np.dot(np.dot(AfpInv_.T, QSet_[0]), AfpInv_)\n # Qff_ = Qtemp_[:Nf_, :Nf_]\n Qfp_ = Qtemp_[:Nf_, Nf_:]\n Qpf_ = Qtemp_[Nf_:, :Nf_]\n Qpp_ = Qtemp_[Nf_:, Nf_:]\n QSet = np.zeros((self.dim, self.num_variables-Nf_, self.num_variables-Nf_))\n HSet = np.zeros((self.dim, self.num_variables-Nf_))\n # check ASet ?\n if ASet_ is not None:\n ASet = np.zeros((self.dim, ASet_.shape[1], self.num_variables-Nf_))\n BSet = BSet_.copy()\n dp_ = None\n for dd in range(self.dim):\n df_ = BeqSet_[dd]\n QSet[dd] = 2*Qpp_\n HSet[dd] = np.dot(df_.T, (Qfp_+Qpf_.T))\n A_ = np.dot(ASet_[dd], AfpInv_)\n ASet[dd] = A_[:, Nf_:]\n BSet[dd] = BSet_[dd] - np.dot(A_[:, :Nf_], df_)\n else:\n ASet = None\n BSet = None\n # directly solving the problem without making an optimization problem\n dp_ = np.zeros((self.dim, self.num_variables-Nf_))\n for dd in range(self.dim):\n df_ = BeqSet_[dd]\n dp_[dd] = np.dot(np.dot(-np.linalg.inv(Qpp_), Qfp_.T), df_).flatten()\n\n return QSet, HSet, ASet, BSet, dp_\n\n def qp_mk_solver(self, P, q=None, G=None, h=None, A=None, b=None, lb=None, ub=None):\n '''\n description:\n using MOSEK to solve a qp problem\n param {type}\n return {type}\n '''\n num_x = P.shape[0]\n num_c = 0\n\n bound_key_cons = []\n bound_low_cons = []\n bound_up_cons = []\n\n A_sum = None\n xx = None\n # print('solving using mosek')\n\n ## only for print optimizer states\n # def streamprinter(text):\n # sys.stdout.write(text + '\\n')\n # sys.stdout.flush()\n # prepare data\n num_x = P.shape[0]\n if lb is None and ub is None:\n bound_low_x = [-self.inf]*num_x\n bound_up_x = [self.inf]*num_x\n bound_key_x = [mk.boundkey.fr]*num_x\n elif lb is None and ub is not None:\n bound_key_x = [mk.boundkey.up]*num_x\n elif lb is not None and ub is None:\n bound_key_x = [mk.boundkey.lo]*num_x\n else:\n bound_key_x = [mk.boundkey.ra]*num_x\n if G is not None:\n num_c += G.shape[0]\n bound_key_cons = bound_key_cons + [mk.boundkey.up]*G.shape[0]\n bound_low_cons = bound_low_cons + [-np.inf]*G.shape[0]\n bound_up_cons = bound_up_cons + h.flatten().tolist()\n if A_sum is None:\n A_sum = G\n else:\n A_sum = np.concatenate((A_sum, G))\n if A is not None:\n num_c += A.shape[0]\n bound_key_cons = bound_key_cons + [mk.boundkey.fx]*A.shape[0]\n bound_low_cons = bound_low_cons + b.flatten().tolist()\n bound_up_cons = bound_up_cons + b.flatten().tolist()\n if A_sum is None:\n A_sum = A\n else:\n A_sum = np.concatenate((A_sum, A))\n\n with mk.Env() as env:\n with env.Task(0, 0) as task:\n # task.set_Stream(mk.streamtype.log, streamprinter) # for print solver information\n task.appendvars(num_x)\n for i in range(num_x):\n task.putvarbound(i, bound_key_x[i], bound_low_x[i], bound_up_x[i])\n if q is not None:\n for i in range(num_x):\n task.putcj(i, q[i]) # add q vector\n for i in range(num_x):\n for j in range(num_x):\n if j <= i: # only the lower triangle matrix needs to be defined\n task.putqobjij(i, j, P[i, j])\n\n task.appendcons(num_c)\n for i in range(num_c):\n task.putconbound(i, bound_key_cons[i], bound_low_cons[i], bound_up_cons[i])\n for i in range(num_c):\n for j in range(num_x):\n task.putaij(i, j, A_sum[i, j])\n # task.putaij(i, j, A[i, j])\n task.putobjsense(mk.objsense.minimize)\n task.optimize()\n # task.solutionsummary(mk.streamtype.msg)\n prosta = task.getprosta(mk.soltype.itr)\n solsta = task.getsolsta(mk.soltype.itr)\n xx = [0.]*num_x\n task.getxx(mk.soltype.itr, xx)\n if solsta == mk.solsta.optimal:\n print(\"Optimal solution found.\")\n elif solsta == mk.solsta.dual_infeas_cer:\n print(\"Primal or dual infeasibility.\\n\")\n elif solsta == mk.solsta.prim_infeas_cer:\n print(\"Primal or dual infeasibility.\\n\")\n elif mk.solsta.unknown:\n print(\"Unknown solution status\")\n else:\n print(\"Other solution status\")\n\n return xx\n\n def qp_mk_fusion_solver(self, P, q=None, G=None, h=None, A=None, b=None, lb=None, ub=None):\n '''\n description:\n param {type}\n return {type}\n '''\n num_x = P.shape[0]\n num_c_eq = 0\n xx = None\n print('solving using mosek fusion')\n\n ## only for print optimizer states\n # def streamprinter(text):\n # sys.stdout.write(text + '\\n')\n # sys.stdout.flush()\n # prepare data\n if G is not None:\n num_c_ieq = G.shape[0]\n\n if A is not None:\n num_c_eq = A.shape[0]\n\n with mkfs.Model('test') as M:\n x = M.variable('x', num_x, mkfs.Domain.unbounded())\n t_0 = M.variable('t0', 1, mkfs.Domain.unbounded())\n # F_chol, d, _ = ldl(P, lower=True)\n try:\n F_chol = np.linalg.cholesky(P)\n except:\n F_chol =np.linalg.cholesky(P+np.diag(np.ones(P.shape[0]))*1e-7)\n # result of the cholesky decomposition\n F_ = M.parameter('F', [num_x, num_x])\n # set up value of the mksf parameter\n F_.setValue(F_chol.T)\n quad_cost = mkfs.Expr.vstack(t_0, mkfs.Expr.mul(F_, x))\n M.constraint(\"lc\", quad_cost, mkfs.Domain.inQCone())\n\n if A is not None:\n A_ = M.parameter('A', [num_c_eq, num_x])\n b_ = M.parameter('b', num_c_eq)\n A_.setValue(A)\n b_.setValue(b.flatten())\n M.constraint(mkfs.Expr.sub(mkfs.Expr.mul(A_, x), b_), mkfs.Domain.equalsTo(0.0))\n if G is not None:\n A_ieq_ = M.parameter('A_ieq', [num_c_ieq, num_x])\n b_ieq_ = M.parameter('b_ieq', num_c_ieq)\n A_ieq_.setValue(G)\n b_ieq_.setValue(h.flatten())\n M.constraint(mkfs.Expr.sub(mkfs.Expr.mul(A_ieq_, x), b_ieq_), mkfs.Domain.lessThan(0.0))\n\n M.objective('obj', mkfs.ObjectiveSense.Minimize, t_0)\n t1 = time.time()\n M.solve()\n sol_x = x.level()\n return sol_x\n\n def solve(self,):\n self.isSolved = True\n # prepare QP\n QSet, ASet, BSet, AeqSet, BeqSet = self.getQPset()\n\n if self.algorithm == 'end-derivative':# and ASet is not None:\n mapMat = self.coeff2endDerivatives(AeqSet[0])\n QSet, HSet, ASet, BSet, dp_e = self.mapQP(QSet, ASet, BSet, AeqSet, BeqSet)\n elif self.algorithm == 'poly-coeff': # or ASet is None:\n pass\n\n for dd in range(self.dim):\n print('soving {}th dimension ...'.format(dd))\n if self.algorithm == 'poly-coeff': # or ASet is None:\n try:\n if ASet is not None:\n t1 = time.time()\n result = solve_qp(P=QSet[dd], q=np.zeros((QSet[dd].shape[0])), G=ASet[dd],\n h=BSet[dd], A=AeqSet[dd], b=BeqSet[dd], solver='cvxopt')\n print(\"solve qp time {}\".format(time.time() - t1))\n t2 = time.time()\n result = self.qp_mk_solver(P=QSet[dd], G=ASet[dd], h=BSet[dd], A=AeqSet[dd], b=BeqSet[dd])\n print(\"mosek using api {0}\".format(time.time() - t2))\n t3 = time.time()\n result = self.qp_mk_fusion_solver(P=QSet[dd], G=ASet[dd], h=BSet[dd], A=AeqSet[dd], b=BeqSet[dd])\n print(\"mosek using fusion {0}\".format(time.time() - t3))\n # print(\"result qpsolver {0} \\n and result mosek {1}\".format(result, result_test))\n else:\n if dd == 3:\n print(BeqSet[dd].shape)\n # print(QSet[dd].shape)\n # t1 = time.time()\n result = solve_qp(P=QSet[dd], q=np.zeros((QSet[dd].shape[0])), A=AeqSet[dd], b=BeqSet[dd], solver='cvxopt')\n # print(time.time() - t1)\n # t2 = time.time()\n result = self.qp_mk_solver(P=QSet[dd], A=AeqSet[dd], b=BeqSet[dd])\n result = self.qp_mk_fusion_solver(P=QSet[dd], A=AeqSet[dd], b=BeqSet[dd])\n print(result)\n # print(\"saving\")\n # np.savetxt(\"P_\"+str(dd)+\".csv\", QSet[dd], delimiter=\",\")\n # np.savetxt(\"A_\"+str(dd)+\".csv\", AeqSet[dd], delimiter=\",\")\n # np.savetxt(\"B_\"+str(dd)+\".csv\", BeqSet[dd], delimiter=\",\")\n # np.save(\"P_\"+str(dd)+\".npy\", QSet[dd])\n # np.save(\"A_\"+str(dd)+\".npy\", AeqSet[dd])\n # np.save(\"B_\"+str(dd)+\".npy\", BeqSet[dd])\n # print(time.time()-t2)\n Phat_ = result\n if Phat_ is not None:\n flag_ = True\n else:\n flag_ = False\n except:\n Phat_ = None\n flag_ = False\n else: # using end-derivative method\n if ASet is not None:\n # result = solve_qp(P=QSet[dd], q=HSet[dd], G=ASet[dd],\n # h=BSet[dd], solver='cvxopt')\n # dP_ = result.reshape(-1, 1)\n t1 = time.time()\n result = self.qp_mk_solver(P=QSet[dd], q=HSet[dd], G=ASet[dd], h=BSet[dd])\n print(time.time()-t1)\n dP_ = np.array(result).reshape(-1, 1)\n dF_ = BeqSet[dd]\n Phat_ = solve(mapMat, np.concatenate((dF_, dP_)))\n\n flag_ = True\n else:\n # without considering the optimization problem, get the result directly\n dP_ = dp_e.copy()\n dF_ = BeqSet[dd]\n Phat_ = solve(mapMat, np.concatenate((dF_, dP_[dd].reshape(-1, 1))))\n flag_ = True\n # ## ipopt version [this is an alternative choice to solve the opt problem, however it is slower than the sigle qp problem]\n # t3 = time.time()\n # x_sym = ca.SX.sym('x', QSet[0].shape[0])\n # opts_setting = {'ipopt.max_iter':100, 'ipopt.print_level':0, 'print_time':0, 'ipopt.acceptable_tol':1e-8, 'ipopt.acceptable_obj_change_tol':1e-6}\n # obj = 0.5* ca.mtimes([x_sym.T, QSet[dd], x_sym]) + ca.mtimes([HSet[dd].reshape(1, -1), x_sym])\n # Ax_sym = ca.mtimes([ASet[dd], x_sym])\n # nlp_prob = {'f': obj, 'x': x_sym, 'g':Ax_sym}\n # solver = ca.nlpsol('solver', 'ipopt', nlp_prob, opts_setting)\n # try:\n # result = solver(ubg=BSet[dd],)\n # dP_ = result['x']\n # # print(dP_)\n # dF_ = BeqSet[dd]\n # Phat_ = solve(mapMat, np.concatenate((dF_, dP_)))\n # flag_ = True\n # except:\n # dP_ = None\n # flag_ = False\n # print(time.time() - t3)\n # except:\n # dP_ = None\n # flag_ = False\n # else:\n # # without considering the optimization problem, get the result directly\n # dP_ = dp_e.copy()\n # dF_ = BeqSet[dd]\n # Phat_ = solve(mapMat, np.concatenate((dF_, dP_[dd].reshape(-1, 1))))\n # flag_ = True\n if flag_:\n print(\"success !\")\n # print('phat shape: {}'.format(Phat_.shape))\n P_ = np.dot(self.scaleMatBigInv(), Phat_)\n self.polyCoeffSet[dd] = P_.reshape(-1, self.N+1).T\n # print('for dd {0}, Phat {1}, P_{2}, result {3}'.format(dd, Phat_, P_, self.polyCoeffSet[dd]))\n print(\"done\")\n\n def eval(self, t_, d_):\n val_ = np.zeros((self.dim, t_.shape[0]))\n for dd in range(self.dim):\n for idx in range(t_.shape[0]):\n t_i = t_[idx]\n if t_i < self.Ts[0] or t_i > self.Ts[-1]:\n print(\"WARNING: Eval of t: out of bound. Extrapolation\\n\")\n m, _ = self.findSegInteval(t_i)\n # dTm = self.Ts[m+1] - self.Ts[m]\n val_[dd, idx] = np.dot(self.tVec(t_i-self.Ts[m], d_).T, self.polyCoeffSet[dd, :, m])\n\n return val_","sub_path":"python/scripts/traj_gen/poly_trajectory_mosek.py","file_name":"poly_trajectory_mosek.py","file_ext":"py","file_size_in_byte":24549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"605708707","text":"#!/usr/bin/env python\nfrom general import *\n\nclass Event:\n def __init__(self, system, type):\n self.system = system\n self.type = type\n\nclass Event_handler:\n def __init__(self, system):\n self.system = system\n self.events = []\n self.event_create_functions = {\n EVENT_CAST : self.event_cast,\n EVENT_REMOVE : self.event_remove,\n EVENT_DEATH : self.event_death,\n EVENT_DAMAGE : self.event_damage,\n EVENT_COLLISION : self.event_collision,\n EVENT_COLLISION_DURATION: self.event_collision_duration\n }\n self.event_process_functions = {\n EVENT_CAST : self.do_cast,\n EVENT_REMOVE : self.do_remove,\n EVENT_DEATH : self.do_death,\n EVENT_DAMAGE : self.do_damage,\n EVENT_COLLISION : self.do_collision,\n EVENT_COLLISION_DURATION: self.do_collision_duration\n }\n\n def on_update(self):\n for event in self.events:\n self.do_event(event)\n self.events.remove(event)\n\n def do_event(self, event):\n characters = self.system.characters.copy()\n for i in characters:\n character = characters[i]\n for j in character.components:\n component = character.components[j]\n if event.type in component.event_before_functions:\n for k in range(EVENT_BEFORE_PROCEDURE[event.type]):\n if k in component.event_before_functions[event.type]:\n if not (component.event_functions[event.type][k](event)):\n return False\n if not self.event_process_functions[event.type](event):\n return False\n for i in characters:\n character = characters[i]\n for j in character.components:\n component = character.components[j]\n if event.type in component.event_after_functions:\n for k in range(EVENT_AFTER_PROCEDURE[event.type]):\n if k in component.event_after_functions[event.type]:\n if not (component.event_after_functions[event.type][k](event)):\n return False\n return True\n\n\n # 01 CAST\n def event_cast(self, ability, aim_targets):\n castable = ability.components[COMPONENT_CASTABLE]\n if (not castable.castable(aim_targets)):\n return False\n\n event = Event(self.system, EVENT_CAST)\n event.ability = ability\n event.aim_targets = aim_targets\n self.events.append(event)\n return True\n\n def do_cast(self, event):\n castable = event.ability.components[COMPONENT_CASTABLE]\n castable.cool_down_left = castable.value_table.value_total(ATTRIBUTE_COOLDOWN)\n return True\n\n # 02 REMOVE\n def event_remove(self, character):\n if not (character.id in self.system.characters):\n return False\n character.on_remove()\n event = Event(self.system, EVENT_REMOVE)\n event.character = character\n self.events.append(event)\n return True\n\n def do_remove(self, event):\n character = event.character\n if not (character.id in self.system.characters):\n return False\n character.on_remove()\n return True\n\n # 03 DEATH\n def event_death(self, character):\n if not (character.id in self.system.characters):\n return False\n\n event = Event(self.system, EVENT_DEATH)\n event.character = character\n self.events.append(event)\n return True\n\n def do_death(self, event):\n return True\n\n # 04 DAMAGE\n def event_damage(self, attacker, source, target, damage, type, subtype):\n event = Event(self.system, EVENT_DAMAGE)\n event.attacker = attacker\n event.source = source\n event.target = target\n event.damage = damage\n event.damage_type = type\n event.damage_subtype = subtype\n\n self.events.append(event)\n return True\n\n def do_damage(self, event):\n if (COMPONENT_LIFE in event.target.components):\n event.target.components[COMPONENT_LIFE].on_damage(event.damage)\n return True\n return False\n\n # 05 COLLISION\n def event_collision(self, c1, c2):\n event = Event(self.system, EVENT_COLLISION)\n event.c1 = c1\n event.c2 = c2\n\n self.events.append(event)\n return True\n\n def do_collision(self, event):\n return True\n\n # 05 COLLISION_DURATION\n def event_collision_duration(self, c1, c2, delta_time):\n event = Event(self.system, EVENT_COLLISION_DURATION)\n event.c1 = c1\n event.c2 = c2\n event.delta_time = delta_time\n\n self.events.append(event)\n return True\n\n def do_collision_duration(self, event):\n return True","sub_path":"game_project/server/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":5021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359474561","text":"\n# coding=utf8\n\n# представьте, что следующий словарь - база данных\n# в дальнейших комментах под базой будет подразумеваться этот список\n\n\ndb = [\n {'id': 1, 'name': 'Chuck Norris', 'rate': 2},\n {'id': 2, 'name': 'Bruce Lee', 'rate': 1},\n {'id': 3, 'name': 'Jackie Chan', 'rate': 3},\n]\n\n\nclass EntityMeta(type):\n def __new__(mcs, name, parents, props):\n for key, value in props.items():\n if isinstance(value, Field):\n value.name = key\n value.__tablename__ = props['__tablename__']\n\n return super(EntityMeta, mcs).__new__(mcs, name, parents, props)\n\n\nclass Entity:\n def __init__(self, **kwargs):\n setattr(self, '__data__', {})\n if 'id' not in kwargs:\n self.id = self.__next_id()\n self.__save(kwargs)\n for key, value in kwargs.items():\n setattr(self, key, value)\n\n def __save(self, props):\n d = {'id': self.id}\n for key, value in props.items():\n d[key] = value\n db.append(d)\n\n @classmethod\n def __next_id(cls):\n return max([record['id'] for record in db]) + 1\n\n @staticmethod\n def get(id):\n for record in db:\n if record['id'] == id:\n return User(**record)\n return None\n\n\nclass Field:\n def __init__(self):\n self.name = None\n self.__tablename__ = None\n\n def __get__(self, instance, owner):\n if instance:\n return instance.__data__[self.name]\n else:\n return self\n\n def __eq__(self, other):\n return '\"%s\".\"%s\" = \\'%s\\'' % (self.__tablename__, self.name, str(other))\n\n\nclass TextField(Field):\n def __set__(self, instance, value):\n instance.__data__[self.name] = str(value)\n\n\nclass IntegerField(Field):\n def __set__(self, instance, value):\n try:\n instance.__data__[self.name] = int(value)\n except ValueError:\n raise ValueError('Field \"%s\" should be an integer.' % self.name)\n\n# Д��лаем мини-модель ORM, нужно заставить работать следующий кусок кода.\n# Для этого реализуйте объявленные выше классы, а также, если необходимо,\n# базовые и метаклассы.\n\n\nclass User(Entity, metaclass=EntityMeta):\n __tablename__ = 'user'\n name = TextField()\n rate = IntegerField()\n # если угодно, можно заменить TextField на Field(Text), Field.Text и т.п.\n\nu = User.get(2) # u должен присвоиться объект типа User\n # с аттрибутами id=2, name='Bruce Lee', rate=1\n\nprint(u.name) # вернет строку 'Bruce Lee'\n\nu2 = User(name='Arni', rate=4) # В \"базу\" должен записаться новый dict\n # {'id': 4, 'name': 'Arni', 'rate': 4},\n # переменной u2 должен присвоиться объект\n # типа User c аттрибутами name='Arni', rate=4\n\nprint(u2.rate) # Должно вернуть 4 (int(4))\n\nprint(db)\n\nprint(User.name == 'Duncan MacLeod') # Выражение должно вернуть SQL statement\n # (просто строку):\n # \"user\".\"name\" = 'Duncan MacLeod'\n","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"184078502","text":"# Copyright 2022 Huawei Technologies Co., Ltd\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\"\"\"\nTesting AdjustSaturation op in DE\n\"\"\"\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport PIL\nfrom PIL import Image, ImageEnhance\n\nimport mindspore.dataset as ds\nimport mindspore.dataset.transforms.transforms\nimport mindspore.dataset.vision as vision\nfrom mindspore import log as logger\n\n\nDATA_DIR = \"../data/dataset/testImageNetData/train/\"\nMNIST_DATA_DIR = \"../data/dataset/testMnistData\"\n\nDATA_DIR_2 = [\"../data/dataset/test_tf_file_3_images/train-0000-of-0001.data\"]\nSCHEMA_DIR = \"../data/dataset/test_tf_file_3_images/datasetSchema.json\"\n\nIMAGE_FILE = \"../data/dataset/apple.jpg\"\n\n\ndef generate_numpy_random_rgb(shape):\n \"\"\"\n Only generate floating points that are fractions like n / 256, since they\n are RGB pixels. Some low-precision floating point types in this test can't\n handle arbitrary precision floating points well.\n \"\"\"\n return np.random.randint(0, 256, shape) / 255.\n\n\ndef test_adjust_saturation_eager():\n \"\"\"\n Feature: AdjustSaturation op\n Description: Test eager support for AdjustSaturation C implementation\n Expectation: Output is the same as expected output\n \"\"\"\n # Eager 3-channel\n rgb_flat = generate_numpy_random_rgb((64, 3)).astype(np.uint8)\n img_in = rgb_flat.reshape((8, 8, 3))\n img_pil = Image.fromarray(img_in)\n\n adjustsaturation_op = vision.AdjustSaturation(0.0)\n img_out = adjustsaturation_op(img_in)\n pil_out = ImageEnhance.Color(img_pil).enhance(0)\n pil_out = np.array(pil_out)\n assert_allclose(pil_out.flatten(),\n img_out.flatten(),\n rtol=1e-5,\n atol=0)\n\n img_in2 = PIL.Image.open(\"../data/dataset/apple.jpg\").convert(\"RGB\")\n\n adjustsaturation_op2 = vision.AdjustSaturation(1.0)\n img_out2 = adjustsaturation_op2(img_in2)\n img_out2 = np.array(img_out2)\n pil_out2 = ImageEnhance.Color(img_in2).enhance(1)\n pil_out2 = np.array(pil_out2)\n assert_allclose(pil_out2.flatten(),\n img_out2.flatten(),\n rtol=1e-5,\n atol=0)\n\n\ndef test_adjust_saturation_invalid_saturationfactor_param():\n \"\"\"\n Feature: AdjustSaturation op\n Description: Test AdjustSaturation Cpp implementation with invalid ignore parameter\n Expectation: Correct error is raised as expected\n \"\"\"\n logger.info(\"Test AdjustSaturationC implementation with invalid ignore parameter\")\n try:\n data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)\n data_set = data_set.map(\n operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],\n input_columns=[\"image\"])\n # invalid alpha\n data_set = data_set.map(operations=vision.AdjustSaturation(saturation_factor=-10.0),\n input_columns=\"image\")\n except ValueError as error:\n logger.info(\"Got an exception in AdjustSaturation: {}\".format(str(error)))\n assert \"Input is not within the required interval of \" in str(error)\n try:\n data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)\n data_set = data_set.map(\n operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],\n input_columns=[\"image\"])\n # invalid alpha\n data_set = data_set.map(operations=vision.AdjustSaturation(saturation_factor=[1.0, 2.0]),\n input_columns=\"image\")\n except TypeError as error:\n logger.info(\"Got an exception in AdjustSaturation: {}\".format(str(error)))\n assert \"is not of type [, ], but got\" in str(error)\n\n\ndef test_adjust_saturation_pipeline():\n \"\"\"\n Feature: AdjustSaturation op\n Description: Test AdjustSaturation Cpp implementation Pipeline\n Expectation: Output is the same as expected output\n \"\"\"\n # First dataset\n transforms1 = [vision.Decode(), vision.Resize([64, 64])]\n transforms1 = mindspore.dataset.transforms.transforms.Compose(\n transforms1)\n ds1 = ds.TFRecordDataset(DATA_DIR_2,\n SCHEMA_DIR,\n columns_list=[\"image\"],\n shuffle=False)\n ds1 = ds1.map(operations=transforms1, input_columns=[\"image\"])\n\n # Second dataset\n transforms2 = [\n vision.Decode(),\n vision.Resize([64, 64]),\n vision.AdjustSaturation(1.0)\n ]\n transform2 = mindspore.dataset.transforms.transforms.Compose(\n transforms2)\n ds2 = ds.TFRecordDataset(DATA_DIR_2,\n SCHEMA_DIR,\n columns_list=[\"image\"],\n shuffle=False)\n ds2 = ds2.map(operations=transform2, input_columns=[\"image\"])\n\n num_iter = 0\n for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),\n ds2.create_dict_iterator(num_epochs=1)):\n num_iter += 1\n ori_img = data1[\"image\"].asnumpy()\n cvt_img = data2[\"image\"].asnumpy()\n assert_allclose(ori_img.flatten(),\n cvt_img.flatten(),\n rtol=1e-5,\n atol=0)\n assert ori_img.shape == cvt_img.shape\n\n\nif __name__ == \"__main__\":\n test_adjust_saturation_eager()\n test_adjust_saturation_invalid_saturationfactor_param()\n test_adjust_saturation_pipeline()\n","sub_path":"tests/ut/python/dataset/test_adjust_saturation.py","file_name":"test_adjust_saturation.py","file_ext":"py","file_size_in_byte":6065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"401453693","text":"import testing_base as base\r\nimport testing_logger as logger\r\nfrom pymodbus.pdu import ExceptionResponse\r\n\r\n@base.testClass('wpt')\r\nclass Ping_Test(base.testingBase):\r\n '''\r\nThis test simply pings the target over modbus, according\r\nto the command parameters.\r\nUses: -i, -s, -p, -w, -l, -f, -t\r\n'''\r\n\r\n def __init__(self, cfg):\r\n self.log = logger.get_logger('Ping_Test')\r\n self.log.info(\"Look at the modbus watchdog to check for false triggers\")\r\n self.config = cfg\r\n self.ping = 0\r\n base.testingBase.__init__(self, cfg)\r\n\r\n def init_test(self):\r\n self.log.info(\"setup target state\")\r\n self.log.info(\"sleep time between iterations: %.3f\"%(self.config.sleep))\r\n self.log.info(\"iterations between pings: %d\"%(self.config.ping))\r\n\r\n def iterate_test(self):\r\n # read bulk data\r\n regs = self.client.client.read_holding_registers(0, 10)\r\n if ExceptionResponse is type(regs):\r\n self.log.error(\"read configurations from target failed: %s\"%(str(regs)))\r\n","sub_path":"testing/tests/wd_ping_test.py","file_name":"wd_ping_test.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"369738182","text":"# -*- coding: utf-8 -*-\n\nfrom pvfactors import PVFactorsError\nfrom pvfactors.pvcore import LinePVArray, Y_GROUND\nfrom shapely.geometry import LineString, Point\nfrom shapely.affinity import affine_transform\nimport numpy as np\n\n\nclass PVRowBase(object):\n \"\"\"\n ``PVRowBase`` exists for future developments of the model. It is the\n base class for PV Rows that will contain all the boiler plate code\n shared by sub classes like :class:`PVRowLine`, or for instance\n ``PVRowRoof``.\n \"\"\"\n\n def __init__(self):\n\n self.front = None\n self.back = None\n self.shadow = None\n self.shadow_line_index = None\n self.lines = []\n self.highest_point = None # for shading purposes\n self.lowest_point = None # for shading purposes\n self.left_point = None # for shading purposes\n self.right_point = None # for shading purposes\n self.director_vector = None # for shading purposes\n self.normal_vector = None # for shading purposes\n # Complete line will have the full PVRow linestring with possibly\n # multiple points on it, but still one linestring only\n self.complete_linestring = None # for obstruction purposes\n\n def create_lines(self, *args, **kwargs):\n raise NotImplementedError\n\n def get_shadow_bounds(self, *args, **kwargs):\n raise NotImplementedError\n\n def translate_2d_lines(self, x_off, y_off):\n \"\"\"\n Translate all the :class:`pvfactors.components.LinePVArray` objects in\n the line attribute in the x and y directions.\n /!\\ method is unfinished\n\n :param float x_off: translation in the x direction\n :param float y_off: translation in the y direction\n :return: None\n \"\"\"\n matrix_2d_translation = [1, 1, 1, 1, x_off, y_off]\n for line in self.lines:\n line['shapeline'] = affine_transform(line['shapeline'],\n matrix_2d_translation)\n # and update lines in line_registry\n\n\nclass PVRowLine(PVRowBase):\n \"\"\"\n ``PVRowLine`` is sub-classed from :class:`PVRowBase`, and its core is\n made of :class:`pvfactors.components.LinePVArray` objects. It is a\n container that has methods and attributes relative to PV Rows and their\n shadows.\n ``PVRowLine`` can only create PV rows that have the shape of\n straight lines. So it won't be able to create shapes like dual-tilt\n systems for instance.\n\n :param line_registry: line registry passed by :class:`pvarray.Array`\n object\n :type line_registry: :class:`pvcore.Registry`\n :param float x_center: x coordinate of center of the line [m]\n :param float y_center: y coordinate of center of the line [m]\n :param int index: PV row index, used to distinguish different PV rows\n :param float array_tilt: tilt of the PV row, same as the whole array a\n priori [deg]\n :param float pvrow_width: width of the PV row, which is the length of\n the PV row line [m]\n \"\"\"\n\n def __init__(self, line_registry, x_center, y_center, index, array_tilt,\n pvrow_width):\n super(PVRowLine, self).__init__()\n self.width = pvrow_width\n self.tilt = array_tilt\n self.index = index\n self.x_center = x_center\n self.y_center = y_center\n (self.lines, self.highest_point, self.lowest_point, self.right_point,\n self.left_point, self.director_vector, self.normal_vector) = (\n self.create_lines(self.tilt, index))\n self.line_registry_indices = line_registry.pvgeometry.add(self.lines)\n # Complete line will have the full pvrow linestring with possibly\n # multiple points on it, but still one linestring only\n self.complete_linestring = self.lines[0]['geometry']\n self.cut_points = []\n\n def create_lines(self, tilt, index):\n \"\"\"\n Create the :class:`pvcore.LinePVArray` objects that the PV row\n is made out of, based on the inputted geometrical parameters.\n\n :param float tilt: tilt angle of the PV row [deg]\n :param int index: PV row index, used to distinguish different PV rows\n :return: [line_pvarray], highest_point, lowest_point,\n right_point, left_point, director_vector, normal_vector // which\n are: list of :class:`pvcore.LinePVArray`;\n :class:`shapely.Point` of the line with biggest y coordinate;\n :class:`shapely.Point` of the line with smallest y coordinate;\n :class:`shapely.Point` of the line with biggest x coordinate;\n :class:`shapely.Point` of the line with smallest x coordinate;\n list of 2 coordinates for director vector of the line; list of\n 2 coordinates for normal vector of the line\n \"\"\"\n tilt_rad = np.radians(tilt)\n\n # Create the three trackers\n radius = self.width / 2.\n x1 = radius * np.cos(tilt_rad + np.pi) + self.x_center\n y1 = radius * np.sin(tilt_rad + np.pi) + self.y_center\n x2 = radius * np.cos(tilt_rad) + self.x_center\n y2 = radius * np.sin(tilt_rad) + self.y_center\n\n highest_point = Point(x2, y2) if y2 >= y1 else Point(x1, y1)\n lowest_point = Point(x1, y1) if y2 >= y1 else Point(x2, y2)\n right_point = Point(x2, y2) if x2 >= x1 else Point(x1, y1)\n left_point = Point(x1, y1) if x2 >= x1 else Point(x2, y2)\n\n # making sure director_vector[0] >= 0\n director_vector = [x2 - x1, y2 - y1]\n # making sure normal_vector[1] >= 0\n normal_vector = [- director_vector[1], director_vector[0]]\n geometry = LineString([(x1, y1), (x2, y2)])\n line_pvarray = LinePVArray(geometry=geometry, line_type='pvrow',\n shaded=False, pvrow_index=index)\n return ([line_pvarray], highest_point, lowest_point,\n right_point, left_point, director_vector, normal_vector)\n\n def get_shadow_bounds(self, solar_2d_vector):\n \"\"\"\n Calculate the x coordinates of the boundary points of the shadow lines\n on the ground, assuming Y_GROUND is the y coordinate of the ground.\n Note: this shadow construction is more or less ignored when direct\n shading happens between rows, leading to one continous shadows formed by\n all the PV rows in the array.\n\n :param list solar_2d_vector: projection of solar vector into the 2D\n plane of the array geometry\n :return: x1_shadow, x2_shadow // ``float`` smallest x coordinate of\n shadow, ``float`` largest x coordinate of shadow\n \"\"\"\n list_x_values = []\n for line in self.lines:\n geometry = line['geometry']\n b1 = geometry.boundary[0]\n b2 = geometry.boundary[1]\n shadow_intercept_1 = - (solar_2d_vector[1] * b1.x\n + solar_2d_vector[0] * b1.y)\n shadow_intercept_2 = - (solar_2d_vector[1] * b2.x\n + solar_2d_vector[0] * b2.y)\n x1_shadow = - ((shadow_intercept_1\n + solar_2d_vector[0] * Y_GROUND)\n / solar_2d_vector[1])\n x2_shadow = - ((shadow_intercept_2\n + solar_2d_vector[0] * Y_GROUND)\n / solar_2d_vector[1])\n list_x_values.append(x1_shadow)\n list_x_values.append(x2_shadow)\n x1_shadow = min(list_x_values)\n x2_shadow = max(list_x_values)\n return x1_shadow, x2_shadow\n\n def is_front_side_illuminated(self, solar_2d_vector):\n \"\"\"\n Find out if the direct sun light is incident on the front or back\n surface of the PV rows\n\n :param list solar_2d_vector: projection of solar vector into the 2D\n plane of the array geometry\n :return: ``bool``, True if front surface is illuminated\n \"\"\"\n # Only 1 normal vector here\n dot_product = np.dot(solar_2d_vector, self.normal_vector)\n return dot_product <= 0\n\n @property\n def facing(self):\n \"\"\"\n This property is mainly used to calculate the view_matrix\n :return: direction that the pvrow front surfaces are facing\n \"\"\"\n if self.tilt == 0.:\n direction = 'up'\n elif self.tilt > 0:\n direction = 'left'\n elif self.tilt < 0:\n direction = 'right'\n else:\n raise PVFactorsError(\"Unknown facing condition for pvrow\")\n return direction\n\n def calculate_cut_points(self, n_segments):\n \"\"\"\n Calculate the points of the PV row geometry on which the PV line will be\n cut and discretized. The list of cut points is saved into the object.\n\n :param int n_segments: number of segments wanted for the discretization\n :return: None\n \"\"\"\n fractions = np.linspace(0., 1., num=n_segments + 1)[1:-1]\n list_points = [self.complete_linestring.interpolate(fraction,\n normalized=True)\n for fraction in fractions]\n self.cut_points = list_points\n","sub_path":"pvfactors/pvrow.py","file_name":"pvrow.py","file_ext":"py","file_size_in_byte":9200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57315705","text":"from . import speakandlisten\nimport wikipedia\n\ndef wiki():\n #taking the input for what to know about\n wikiknowabout = speakandlisten.takeCommand()\n #getting the search results\n wiki_result = wikipedia.summary(wikiknowabout, sentences = 3 or 4)\n print(wiki_result)\n speakandlisten.speak(wiki_result)","sub_path":"Abilities/get_wiki.py","file_name":"get_wiki.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"101136742","text":"#栈\n\nclass Node(object):\n def __init__(self, elem):\n #节点数据\n self.elem = elem\n #节点指针\n self.next = None\n\nclass Link(object):\n def __init__(self, node=None):\n #初始化头指针指向None\n self.head = node\n\n #入栈\n def into(self, item):\n #创建新节点\n node = Node(item)\n #新节点指针指向上一个节点\n node.next = self.head\n #头指针指向新插入的节点\n self.head = node\n\n #出栈\n def out(self):\n #头指针指��下一个节点\n node=self.head\n self.head=node.next\n #上一个栈的指针指向none\n node.next=None\n #返回出栈数据\n return node.elem\n\n #遍历\n def select(self,item):\n # 头指针\n cur = self.head\n while cur != None:\n if cur.elem==item:\n return cur.elem\n break\n cur = cur.next\n\nif __name__=='__main__':\n item=Link()\n item.into(1)\n item.into(2)\n item.into(3)\n print(item.out())\n print(item.select(1))\n","sub_path":"数据结构/MyStack.py","file_name":"MyStack.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"245905067","text":"# -*- coding: utf-8 -*-\n\"\"\"\n**************************************************************************\n* E-Yantra Robotics Competition\n* ================================\n* This software is intended to check version compatiability of open source software\n* Theme: ANT BOT\n* MODULE: Task1.1\n* Filename: Task1.1.py\n* Version: 1.0.0 \n* Date: October 31, 2018\n* \n* Author: e-Yantra Project, Department of Computer Science\n* and Engineering, Indian Institute of Technology Bombay.\n* \n* Software released under Creative Commons CC BY-NC-SA\n*\n* For legal information refer to:\n* http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode \n* \n*\n* This software is made available on an “AS IS WHERE IS BASIS”. \n* Licensee/end user indemnifies and will keep e-Yantra indemnified from\n* any and all claim(s) that emanate from the use of the Software or \n* breach of the terms of this agreement.\n* \n* e-Yantra - An MHRD project under National Mission on Education using \n* ICT(NMEICT)\n*\n**************************************************************************\n\"\"\"\n\n\"\"\"\nArUco ID Dictionaries: 4X4 = 4-bit pixel, 4X4_50 = 50 combinations of a 4-bit pixel image\nList of Dictionaries in OpenCV's ArUco library:\nDICT_4X4_50\t \nDICT_4X4_100\t \nDICT_4X4_250\t \nDICT_4X4_1000\t \nDICT_5X5_50\t \nDICT_5X5_100\t \nDICT_5X5_250\t \nDICT_5X5_1000\t \nDICT_6X6_50\t \nDICT_6X6_100\t \nDICT_6X6_250\t \nDICT_6X6_1000\t \nDICT_7X7_50\t \nDICT_7X7_100\t \nDICT_7X7_250\t \nDICT_7X7_1000\t \nDICT_ARUCO_ORIGINAL\n\nReference: http://hackage.haskell.org/package/opencv-extra-0.2.0.1/docs/OpenCV-Extra-ArUco.html\n\"\"\"\n\nimport numpy\nimport cv2\nimport cv2.aruco as aruco\n\nimport os\nfrom pathlib import Path\n\nmappingName = {}\n\ndef aruco_gen(id_aruco, num_pixels):\n\n # Setting up n and C according to the id_aruco \t\t\n\n if id_aruco == 8 or id_aruco == 27:\n aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)\n\n elif id_aruco == 92 or id_aruco == 4:\n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_250)\n\n\n\n # ----------Building the Marker----------\n \t \n img = aruco.drawMarker(aruco_dict, id_aruco,num_pixels)\n\n tempImg = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n \n finalImg = cv2.copyMakeBorder(tempImg,25,25,25,25,cv2.BORDER_CONSTANT,value = (255,255,255))\n\n\n # -------------- Adding Red label to RGB File ------------\n\n title = 'ArUco ID = '+ str(id_aruco)\n\n fontStyle = cv2.FONT_HERSHEY_SIMPLEX\n\n cv2.putText(finalImg, title , (170, 17), fontStyle, 0.6, (0, 0, 255), 1, cv2.LINE_AA)\n\n # ------------- Creating the .jpg file from RGB file -----------\n\n fileName = '../Images/ArUco'+ str(id_aruco) +'.jpg'\n\n tempName = 'Aruco'+str(id_aruco)+'.jpg'\n global mappingName\n mappingName[tempName] = 1\n\n cv2.imwrite(fileName,finalImg)\n\n # ------------ Display RGB Image -----\n\n cv2.imshow('frame',finalImg)\n\n\n cv2.waitKey(0)\n\n cv2.destroyAllWindows()\n\n\ndef generalised_version(id_aruco, n, C, num_pixels):\n\n global c\n\n if n == 'ARUCO' and C == 'ORIGINAL':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)\n\n elif n == '4':\n if C == '50':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_50)\n \n elif C == '100':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_100)\n \n elif C == '250':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_250)\n\n elif C == '1000':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_4X4_1000)\n\n elif n == '5':\n if C == '50':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_50)\n \n elif C == '100':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_100)\n \n elif C == '250':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_250)\n\n elif C == '1000':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_1000)\n\n elif n == '6':\n if C == '50':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_50)\n \n elif C == '100':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_100)\n \n elif C == '250':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250)\n\n elif C == '1000':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_1000)\n\n elif n == '7':\n if C == '50':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_7X7_50)\n \n elif C == '100':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_7X7_100)\n \n elif C == '250':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_7X7_250)\n\n elif C == '1000':\n aruco_dict = aruco.Dictionary_get(aruco.DICT_7X7_1000)\n \n # ----------Building the Marker----------\n \t \n img = aruco.drawMarker(aruco_dict, id_aruco,num_pixels)\n\n tempImg = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n \n finalImg = cv2.copyMakeBorder(tempImg,25,25,25,25,cv2.BORDER_CONSTANT,value = (255,255,255))\n\n # -------------- Adding Red label to RGB File ------------\n\n title = 'ArUco ID = '+ str(id_aruco)\n\n fontStyle = cv2.FONT_HERSHEY_SIMPLEX\n\n cv2.putText(finalImg, title , (170, 17), fontStyle, 0.6, (0, 0, 255), 1, cv2.LINE_AA)\n\n # ------------- Creating the .jpg file from RGB file -----------\n\n fileName = '../Images/ArUco'+ str(id_aruco) +'.jpg'\n\n global mappingName\n tempName = 'Aruco'+str(id_aruco)+'.jpg'\n \n if tempName in mappingName:\n fileName = '../Images/Aruco'+str(id_aruco)+'_'+str(mappingName[tempName])+'.jpg'\n print('\\n\"' +tempName+'\"','already present. New name is : '+'\"'+ 'Aruco'+str(id_aruco) + '_' + str(mappingName[tempName]) + '.jpg' + '\"')\n mappingName[tempName] +=1\n\n else:\n mappingName[tempName] = 1\n\n cv2.imwrite(fileName,finalImg)\n\n # ------------ Display RGB Image -----\n\n cv2.imshow('frame',finalImg)\n\n cv2.waitKey(0)\n\n cv2.destroyAllWindows()\n \n\nif __name__ == \"__main__\": \n aruco_gen(8, 400)\n aruco_gen(27, 400)\n aruco_gen(92, 400)\n aruco_gen(4, 400)\n\n print(\"\\n\\n*** THE 4 ARUCO MARKERS HAVE BEEN GENERATED AS SPECIFIED IN TABLE 1 of Task1.1.pdf ***\\n\")\n\n print(\"*** Now the Generalised Code is running ***\\n\")\n\n choice = input(\"\\nPress 0 to EXIT or ANY KEY to CONTINUE : \")\n\n while choice != '0':\n\n id_aruco = input(\"\\nEnter ArUco ID : \")\n n = input(\"Enter n (bits/'ARUCO'): \")\n C = input(\"Enter C (combination/'ORIGINAL') : \")\n\n if (not id_aruco.isdecimal()) or (int(id_aruco) > 1023):\n print(\"Exception : Invalid value of ArUco ID.\")\n\n elif(n == 'ARUCO'):\n if(C == 'ORIGINAL'):\n generalised_version(int(id_aruco),n,C,400)\n else:\n print(\"Exception : Invalid Value of C (combination/'ORIGINAL').\")\n\n elif not n.isdecimal() or (int(n)>7) or (int(n)<4):\n print(\"Exception : Invalid value of n (bits/'ARUCO').\")\n\n elif not C.isdecimal() or ((int(C) != 50) and (int(C) != 100) and (int(C) != 250) and (int(C) != 1000)):\n print(\"Exception : Invalid value of C (comnination).\")\n\n \n elif int(id_aruco) >= int(C):\n print(\"Exception : Aruco ID exceeding C (combination).\")\n\n elif(int(id_aruco) < int(C)):\n generalised_version(int(id_aruco),n,C,400)\n \n else:\n print(\"Exception : Invalid Dictionary.\")\n \n\n #------ Choose to Exit program or not.------\n\n choice = input(\"\\nPress 0 to EXIT or ANY KEY to CONTINUE : \")\n \n \n","sub_path":"Task 1/Task1.1/2121_Task1.1/Code/2121_Task1.1.py","file_name":"2121_Task1.1.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"258494310","text":"\"\"\"\n版本 2月 23日 00：50 用来发送email的程序\n维护：张天翊\n\"\"\"\n\nimport os\nimport sys\n\nsys.path.append(os.path.dirname(os.path.abspath(__file__)))\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom general import notify\nimport time\n\ntime.sleep(1)\nprint(\"print file path please:\")\nfile_path = input()\n\nif os.path.exists(file_path):\n notify.add_file(file_path)\n\n print(\"write the target email path please: use blankspace for the dafult path \")\n email_path = input()\n\n if len(email_path) < 5:\n print(\"send mail to defult list\")\n notify.send_log()\n else:\n print(\"send mail to:\", email_path)\n notify.send_log(email_path)\n\nelse:\n print(\"file path is not exist!!!!\")\n\n\n","sub_path":"send_file_by_email.py","file_name":"send_file_by_email.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"604894250","text":"import re\n\nfrom flask import render_template, request, redirect, flash, url_for\n\nfrom main import env, app\nfrom views.forms import EmployeeForm, DeleteByIdForm\n\nEMAIL_PATTERN = '\\w*[@]\\w*[.]\\w{2,3}'\nPHONE_NUM_PATTERN = '[+]{1}[3]{1}[8]{1}[0]{1}[0-9]{9}'\n\n\n@app.route('/create_employee', methods=['GET', 'POST'])\ndef create_employee():\n form = EmployeeForm(request.form)\n departments_list = [(department.id, department.name, 'department') for department in env.departments]\n units_list = [(unit.id, unit.name, 'unit') for unit in env.units]\n form.subdivision_data.choices = departments_list + units_list\n form.vacancy_data.choices = [(vacancy.id, vacancy.name) for vacancy in env.vacancies]\n if request.method == 'POST':\n print('/create employee', form.data)\n if request.form['first_name'] == '' or request.form['last_name'] == '':\n flash('First and last names should be mentioned!', category='danger')\n elif request.form['subdivision_data'] == '' or request.form['vacancy_data'] == '':\n flash('Subdivision and vacancy information should be mentioned!', category='danger')\n elif re.match(EMAIL_PATTERN, request.form['email']) is None:\n flash('Incorrect email format!', category='danger')\n elif re.match(PHONE_NUM_PATTERN, request.form['phone_number']) is None:\n flash('Incorrect phone number format!', category='danger')\n else:\n env.create_employee(object_type='employee', **form.data)\n flash('Employee successfully created!', category='success')\n return redirect(url_for('index'))\n return render_template('create_employee.html', form=form)\n\n\n@app.route('/update_employee', methods=['GET', 'POST'])\ndef update_employee():\n form = EmployeeForm(request.form)\n form.id.choices = [(employee.id, employee.name) for employee in env.employees]\n departments_list = [(department.id, department.name, 'department') for department in env.departments]\n units_list = [(unit.id, unit.name, 'unit') for unit in env.units]\n form.subdivision_data.choices = departments_list + units_list\n form.vacancy_data.choices = [(vacancy.id, vacancy.name) for vacancy in env.vacancies]\n if request.method == 'POST':\n print('/update employee', form.data)\n if request.form['id'] == 'None':\n flash('There is no existing employee!', category='warning')\n elif request.form['email'] and re.match(EMAIL_PATTERN, request.form['email']) is None:\n flash('Incorrect email format!', category='danger')\n elif request.form['phone_number'] and re.match(PHONE_NUM_PATTERN, request.form['phone_number']) is None:\n flash('Incorrect phone number format!', category='danger')\n else:\n env.update_employee(object_type='employee', **form.data)\n flash('Employee successfully updated!', category='success')\n return redirect(url_for('index'))\n return render_template('update_employee.html', form=form)\n\n\n@app.route('/delete_employee', methods=['GET', 'POST'])\ndef delete_employee():\n form = DeleteByIdForm(request.form)\n form.id.choices = [(employee.id, employee.name) for employee in env.employees]\n if request.method == 'POST':\n if request.form['id'] == 'None':\n flash('There is no employees to delete!', category='warning')\n else:\n env.delete_object(object_type='employee', **form.data)\n flash('Employee successfully deleted!', category='success')\n return redirect(url_for('index'))\n return render_template('delete_employee.html', form=form)\n\n\n@app.route('/show_employee')\ndef show_employee():\n return render_template('show_employee.html', employees=env.employees)\n\n\n@app.route('/employee/')\ndef employee_details(id):\n for employee in env.employees:\n if employee.id == int(id):\n return render_template('details.html', employee=employee)\n return render_template('index.html')\n","sub_path":"views/employee_view.py","file_name":"employee_view.py","file_ext":"py","file_size_in_byte":3988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"260549821","text":"# -*- coding: utf-8 -*-\nimport sys\nimport uuid\nimport scrapy\nfrom scrapy.selector import Selector\n\nfrom crawldatapro.items import CrawldataproItem\n\n\nclass SmzdmspiSpider(scrapy.Spider):\n name = 'smzdmspi'\n allowed_domains = ['smzdm.com']\n start_urls = ['http://smzdm.com/']\n\n def get_comment_page(self,pstr):\n m = int(pstr)\n m_result = int(0)\n if m == 0:\n pass\n else:\n r_rem = 16 % 30\n r_mod = 16 // 30\n if r_rem > 0:\n r_mod = r_mod + 1\n m_result = r_mod\n return m_result\n\n # 爬虫启动时，引擎自动调用该方法，并且只会被调用一次，用于生成初始的请求对象（Request）。\n # start_requests()方法读取start_urls列表中的URL并生成Request对象，发送给引擎。\n # 引擎再指挥其他组件向网站服务器发送请求，下载网页\n def start_requests(self):\n for i in range(1, 2):\n url = f'https://www.smzdm.com/fenlei/qipaoshui/p{i}'\n yield scrapy.Request(url=url, callback=self.parse_products)\n # url 请求访问的网址\n # callback 回调函数，引擎回将下载好的页面(Response对象)发给该方法，执行数据解析\n # 这里可以使用callback指定新的函数，不是用parse作为默认的回调参数\n\n # 解析函数\n def parse_products(self, response):\n # 打印网页的url\n # print(f'开始处理：{response.url}')\n # 打印网页的内容\n # print(response.text)\n bandlist = []\n if response.url == 'https://www.smzdm.com/fenlei/qipaoshui/':\n bands = Selector(response=response).xpath('/html/body/div[1]/div/div[3]/div[2]/div[1]/div[1]/div[2]/div[2]/div[2]/div/a')\n for band in bands:\n m = str(band.xpath('./text()').extract_first()).strip('\\n').strip()\n bandlist.append(m)\n # print(m)\n\n products = Selector(response=response).xpath('//li[@class=\"feed-row-wide\"]')\n if products:\n for product in products:\n title = product.xpath('./div/div[2]/h5/a/text()')\n link = product.xpath('./div/div[2]/h5/a/@href')\n zhi = product.xpath('//div/div[2]/div[4]/div[1]/span/a[1]/span[1]/span/text() | //div/div[2]/div[2]/div[1]/span[1]/span[1]/span[1]/span/text()')\n buzhi = product.xpath('//div/div[2]/div[4]/div[1]/span/a[2]/span[1]/span/text() | //div/div[2]/div[2]/div[1]/span[1]/span[2]/span[1]/span/text()')\n collectcount = product.xpath('//div/div[2]/div[4]/div[1]/a[1]/span/text()')\n commentcount = product.xpath('//div/div[2]/div[4]/div[1]/a[2]/span/text()')\n platform = product.xpath('//div/div[2]/div[4]/div[2]/span/a/text()')\n publish = product.xpath('//div/div[2]/div[4]/div[2]/span/text()')\n\n productitem = CrawldataproItem()\n productitem['title'] = str(title.extract_first()).strip('\\n').strip()\n productitem['link'] = str(link.extract_first()).strip('\\n').strip()\n productitem['zhi'] = str(zhi.extract_first()).strip('\\n').strip()\n productitem['buzhi'] = str(buzhi.extract_first()).strip('\\n').strip()\n productitem['collectcount'] = str(collectcount.extract_first()).strip('\\n').strip()\n productitem['commentcount'] = str(commentcount.extract_first()).strip('\\n').strip()\n productitem['platform'] = str(platform.extract_first()).strip('\\n').strip()\n productitem['publish'] = str(publish.extract_first()).strip('\\n').strip()\n productitem['uid'] = str(uuid.uuid1())\n productitem['bands'] = bandlist\n\n print(f\"产品内容：{productitem['title']}-{productitem['link']}-{ productitem['zhi']}-{productitem['buzhi']}-{productitem['collectcount']}-{productitem['commentcount']}-{productitem['platform']}-{productitem['publish']}\")\n\n # 实现评论翻页功能（思路：每页最多30个评论，根据评论数推断评论页数）\n page_count = self.get_comment_page(productitem['commentcount'])\n if page_count > 0:\n for i in range(1, page_count+1):\n url = f'{link.extract()[0]}/p{i}'\n yield scrapy.Request(url=url, meta={'item': productitem}, callback=self.parse_details)\n else:\n yield productitem\n\n # 解析具体页面\n def parse_details(self, response):\n print('-----------------------开始评论详情---------------------------------------')\n print(response.url)\n details = Selector(response=response).xpath('//li[@class=\"comment_list\"]')\n productitem = response.meta['item']\n mlist = []\n if details:\n for detail in details:\n publisher = str(detail.xpath('./div[2]/div[1]/a/span/text()').extract_first().strip('\\n').strip())\n datePublished = str(detail.xpath('./div[2]/div[1]/div[1]/meta/@content').extract_first().strip('\\n').strip())\n timePublished = str(detail.xpath('./div[2]/div[1]/div[1]/text()').extract_first().strip('\\n').strip())\n comment = str(detail.xpath('./div[2]/div[3]/div[1]/p/span/text() | ./div[2]/div[2]/div[1]/p/span/text()').extract_first().strip('\\n').strip())\n print(f'评论内容：{publisher}-{datePublished}-{timePublished}-{comment}')\n dict = {'uid':productitem['uid'],'publisher':publisher,'datePublished': datePublished,'timePublished':timePublished, 'comment':comment}\n mlist.append(dict)\n productitem['comments'] = mlist\n yield productitem\n\n","sub_path":"week10/homework/crawldatapro/spiders/smzdmspi.py","file_name":"smzdmspi.py","file_ext":"py","file_size_in_byte":5812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"645796229","text":"'''\nvae.py\ncontains the setup for autoencoders.\n\ncreated by shadySource\n\nTHE UNLICENSE\n'''\nimport tensorflow as tf\nfrom tensorflow.python.keras.models import Model\nfrom tensorflow.python.keras import backend as K\n\nclass AutoEncoder(object):\n def __init__(self, encoderArchitecture, \n decoderArchitecture):\n\n self.encoder = encoderArchitecture.model\n self.decoder = decoderArchitecture.model\n\n self.ae = Model(self.encoder.inputs, self.decoder(self.encoder.outputs))\n\ndef test():\n import os\n import numpy as np\n from PIL import Image\n from tensorflow.python.keras.preprocessing.image import load_img\n\n import models\n\n inputShape = (256, 256, 3)\n batchSize = 20\n latentSize = 100\n\n img = load_img(os.path.join('..','images', 'img.jpg'), target_size=inputShape[:-1])\n img.show()\n\n img = np.array(img, dtype=np.float32) / 255 - 0.5\n img = np.array([img]*batchSize) # make fake batches to improve GPU utilization\n\n # This is how you build the autoencoder\n encoder = models.BetaEncoder(inputShape, batchSize, latentSize, 'bvae', beta=69, capacity=15, randomSample=True)\n decoder = models.BetaDecoder(inputShape, batchSize, latentSize)\n bvae = AutoEncoder(encoder, decoder)\n\n bvae.ae.compile(optimizer='adam', loss='mean_absolute_error')\n while True:\n bvae.ae.fit(img, img,\n epochs=100,\n batch_size=batchSize)\n \n # example retrieving the latent vector\n latentVec = bvae.encoder.predict(img)[0]\n print(latentVec)\n\n pred = bvae.ae.predict(img) # get the reconstructed image\n pred[pred > 0.5] = 0.5 # clean it up a bit\n pred[pred < -0.5] = -0.5\n pred = np.uint8((pred + 0.5)* 255) # convert to regular image values\n\n pred = Image.fromarray(pred[0])\n pred.show() # display popup\n\ndef test2():\n import os\n import numpy as np\n from PIL import Image\n from tensorflow.python.keras.preprocessing.image import load_img\n import cv2\n import models\n import random\n\n inputShape = (32, 32, 3)\n batchSize = 32\n latentSize = 128\n episodes = 1000\n verbose = 1\n\n loadFolder = 'imageNet1'\n loadFile = 'PtBetaEncoder-32px-128l-1000e'\n load = False\n saveFolder = 'imageNet2'\n saveFile = 'PtBetaEncoder-32px-128l-1000e'\n save = True\n # C:\\Users\\slani\\Documents\\GitHub\\montazuma\\dataset\\0000001.png\n # C:\\Users\\slani\\Documents\\GitHub\\montazuma\\dataset\\1281149.png\n dataPath = os.path.join('..', '..', 'dataset', 'train_32x32')\n saveFolderPath = os.path.join('..', 'save', saveFolder)\n loadPath = os.path.join('..', 'save', loadFolder, loadFile + '.h5')\n savePath = os.path.join(saveFolderPath, saveFile + '.h5')\n\n if not os.path.exists(saveFolderPath):\n os.makedirs(saveFolderPath)\n\n # This is how you build the autoencoder\n encoder = models.BetaEncoder(inputShape, batchSize, latentSize, 'bvae', beta=128, capacity=15, randomSample=True)\n decoder = models.BetaDecoder(inputShape, batchSize, latentSize)\n bvae = AutoEncoder(encoder, decoder)\n\n bvae.ae.compile(optimizer='adam', loss='mean_absolute_error')\n\n for epoch in range(episodes):\n imgs = []\n for _batch in range(batchSize):\n imageNum = str(random.randrange(1, 1281150)).zfill(7)\n img = load_img(os.path.join(dataPath, imageNum+\".png\"), target_size=inputShape[:-1])\n imgs.append(np.array(img, dtype=np.uint8))\n\n \n if verbose == 1:\n batch_view = np.array(imgs, dtype=np.uint8)\n print(\"batch.shape\", batch_view.shape)\n visualize_batch = np.concatenate((*batch_view,), axis=1)\n visualize_batch = cv2.cvtColor(visualize_batch, cv2.COLOR_RGB2BGR)\n cv2.imshow(\"batch\", visualize_batch)\n cv2.waitKey(1)\n \n batch = np.array(imgs, dtype=np.float32)\n batch = batch / 255 - 0.5\n\n bvae.ae.fit(batch, batch,\n epochs=100,\n batch_size=batchSize)\n \n # example retrieving the latent vector\n if verbose == 1:\n latentVec = bvae.encoder.predict(batch)[0]\n print(latentVec)\n\n print(\"episode: {}/{}\".format(epoch+1, episodes))\n if save:\n bvae.ae.save_weights(savePath)\n\n if verbose == 1:\n pred = bvae.ae.predict(batch) # get the reconstructed image\n print(\"pred.shape\", pred.shape)\n pred[pred > 0.5] = 0.5 # clean it up a bit\n pred[pred < -0.5] = -0.5\n pred = np.uint8((pred + 0.5)* 255) # convert to regular image values\n\n visualize_pred = np.concatenate((*pred,), axis=1)\n visualize_pred = cv2.cvtColor(visualize_pred, cv2.COLOR_RGB2BGR)\n visualize_both = np.concatenate((visualize_batch, visualize_pred), axis=0)\n\n cv2.imwrite(os.path.join(saveFolderPath, \"sample_{}.png\".format(str(epoch).zfill(7))), visualize_both)\n cv2.imshow(\"prediction\", visualize_both)\n cv2.waitKey(1)\n\n # pred = Image.fromarray(pred[0])\n # pred.show() # display popup\n\nif __name__ == \"__main__\":\n test2()\n","sub_path":"bvae/ae.py","file_name":"ae.py","file_ext":"py","file_size_in_byte":5210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"584749818","text":"#!/usr/bin/env python3\n\nfrom matplotlib import pylab\nimport math\nimport random\n\nglobal w_hid_units\nglobal a_hid_units\nglobal o_hid_units\n\nglobal w_out\nglobal a_out\nglobal o_out\n\nglobal delta_out\nglobal ny\n\ndef f(x):\n return -4*math.cos(x/3.0) + math.sin( 15 / (abs(0.5*x+2) +1)) + 0.2*x\n\ndef backprop_out(y):\n global w_hid_units\n global a_hid_units\n global o_hid_units\n \n global w_out\n global a_out\n global o_out\n \n global delta_out\n global ny\n\n delta_out = fermiAbl(a_out)*(o_out-y)\n for i in range(11):\n w_out[i] += -ny*o_hid_units[i]*delta_out\n\ndef fermi_function(x):\n return 1/(math.exp(-x)+1)\n\ndef fermiAbl(x):\n return fermi_function(x)*(1-fermi_function(x))\n\ndef network(x_in):\n global w_hid_units\n global a_hid_units\n global o_hid_units\n \n global w_out\n global a_out\n global o_out\n \n global delta_out\n global ny\n\n for i in range(1,11):\n a_hid_units[i] = x_in*w_hid_units[1][i] + 1*w_hid_units[0][i]\n o_hid_units[i] = fermi_function(a_hid_units[i])\n \n a_out = 0\n for i in range(11):\n a_out += o_hid_units[i]*w_out[i]\n o_out = a_out\n return o_out\n\ndef main():\n global w_hid_units\n global a_hid_units\n global o_hid_units\n \n global w_out\n global a_out\n global o_out\n \n global delta_out\n global ny\n \n ny = 0.0001\n \"\"\"\n Smaller, to reduce \"errors\" in error function.\n \"\"\"\n\n err_x = []\n err_y = []\n \n trained = []\n fx_values = []\n \n a_out = 0\n o_out = 0\n\n filename_training_trained = \"a3_2_training_trained.png\"\n filename_training_set = \"a3_2_training_set.png\"\n filename_error_set = \"a3_2_error_set.png\"\n\n training_set = list(range(1001))\n training_set = [ (i/50.0 - 10) for i in training_set ]\n \n \"\"\"\n training set has to contain values from -10 to 10,\n so for every x in range(1001) /50.0 -10 for values from\n -10 to 10\n \"\"\"\n #matplotlib.interactive(True)\n \n for i in training_set:\n fx_values.append(f(i))\n \n w_hid_units = [[random.uniform(-0.5,0.5) for i in range(11)] for j in range(2)]\n w_out = [random.uniform(-0.5,0.5) for i in range(11)]\n a_hid_units = list(range(11))\n o_hid_units = list(range(11))\n o_hid_units[0] = 1\n \n print(\"ny: \", ny)\n \n mse = 0\n for i in range(1001):\n y = network(training_set[i])\n mse += (y-fx_values[i])**2\n \n mse /= 1001\n print(\"mse_before_training: \",mse)\n \n #Training\n for k in range(500):\n for j in range(100):\n i = random.randrange(0,1001)\n y = network(training_set[i])\n backprop_out(fx_values[i])\n mse = 0\n for i in range(1001):\n y = network(training_set[i])\n mse += (y-fx_values[i])**2\n \n mse /= 1001\n #print(\"Iter:\\t\", k, \" Err:\",mse)\n err_x.append(k)\n err_y.append(mse)\n \n # Ausgabe am Ende\n mse = 0\n for i in range(1001):\n y = network(training_set[i])\n mse += (y-fx_values[i])**2\n trained.append(y)\n \n mse /= 1001\n print(\"mse_after_training: \",mse)\n pylab.plot(training_set, trained)\n pylab.title('the training set')\n pylab.xlabel('training set')\n pylab.ylabel('trained values')\n pylab.savefig(filename_training_trained)\n \n pylab.plot(training_set, fx_values)\n pylab.title('the training set')\n pylab.xlabel('training set')\n pylab.ylabel('f(x) values')\n pylab.savefig(filename_training_set)\n \n pylab.plot(err_x, err_y)\n pylab.title('error function')\n pylab.xlabel('err_x')\n pylab.ylabel('err_y')\n pylab.savefig(filename_error_set)\n \nif __name__ == '__main__':\n main()","sub_path":"Python_Dienstag_14_16/03/a3_2.py","file_name":"a3_2.py","file_ext":"py","file_size_in_byte":3728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"7180191","text":"#!/usr/bin/python3\n\n\"\"\"\n Summary\n -------\n Get a parameter entry from DB for a specific telescope or a site.\n The application receives a parameter name, a site, a telescope (if applicable) and \\\n optionally a version. It then prints out the parameter entry.\n If no version is provided, the value of the current model is printed..\n\n Command line arguments\n ----------------------\n parameter (str, required)\n Parameter name\n\n site (str, required)\n South or North.\n\n telescope (str, optional)\n Telescope model name (e.g. LST-1, SST-D, ...)\n\n log_level (str, optional)\n Log level to print (default=INFO).\n\n Raises\n ------\n KeyError in case the parameter requested does not exist in the model parameters.\n\n Example\n -------\n Get the mirror_list parameter from the DB.\n\n .. code-block:: console\n\n python applications/get_parameter.py --parameter mirror_list --site North --telescope LST-1\\\n --model_version prod5\n\n Expected final print-out message:\n\n .. code-block:: console\n\n {'Applicable': True,\n 'File': True,\n 'Type': 'str',\n 'Value': 'mirror_CTA-N-LST1_v2019-03-31.dat',\n 'Version': '2020-06-28',\n '_id': ObjectId('608834f257df2db2531b8e78'),\n 'entry_date': datetime.datetime(2021, 4, 27, 15, 59, 46, tzinfo=)}\n\n\"\"\"\n\nimport logging\nfrom pprint import pprint\n\nimport simtools.util.general as gen\nfrom simtools import db_handler\nfrom simtools.configuration import configurator\n\n\ndef main():\n\n config = configurator.Configurator(\n description=(\n \"Get a parameter entry from DB for a specific telescope or a site. \"\n \"The application receives a parameter name a site, a telescope (if applicable), \"\n \" and optionally a version. It then prints out the parameter entry. \"\n \"If no version is provided, the value of the current model is printed. \"\n )\n )\n config.parser.add_argument(\"--parameter\", help=\"Parameter name\", type=str, required=True)\n args_dict, db_config = config.initialize(db_config=True, telescope_model=True)\n\n logger = logging.getLogger()\n logger.setLevel(gen.get_log_level_from_user(args_dict[\"log_level\"]))\n\n db = db_handler.DatabaseHandler(mongo_db_config=db_config)\n\n if args_dict[\"telescope\"] is not None:\n pars = db.get_model_parameters(\n args_dict[\"site\"], args_dict[\"telescope\"], args_dict[\"model_version\"]\n )\n else:\n pars = db.get_site_parameters(args_dict[\"site\"], args_dict[\"model_version\"])\n if args_dict[\"parameter\"] not in pars:\n raise KeyError(f\"The requested parameter, {args_dict['parameter']}, does not exist.\")\n print()\n pprint(pars[args_dict[\"parameter\"]])\n print()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"simtools/applications/get_parameter.py","file_name":"get_parameter.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"301290333","text":"import datetime\nfrom bitmovin import Bitmovin, Encoding, S3Input, S3Output, H264CodecConfiguration, \\\n AACCodecConfiguration, H264Profile, StreamInput, SelectionMode, Stream, EncodingOutput, ACLEntry, ACLPermission, \\\n MP4Muxing, MuxingStream, CloudRegion, SmoothManifest, MP4Representation, PlayReadyDRM, PlayReadyMethod, \\\n SmoothContentProtection, Condition\nfrom bitmovin.errors import BitmovinError\n\nAPI_KEY = ''\n\nS3_INPUT_ACCESSKEY = ''\nS3_INPUT_SECRETKEY = ''\nS3_INPUT_BUCKETNAME = ''\nS3_INPUT_PATH = ''\n\nS3_OUTPUT_ACCESSKEY = ''\nS3_OUTPUT_SECRETKEY = ''\nS3_OUTPUT_BUCKETNAME = ''\n\nPLAYREADY_KEYSEED = ''\nPLAYREADY_KID = ''\nPLAYREADY_LA_URL = ''\n\ndate_component = str(datetime.datetime.now()).replace(' ', '_').replace(':', '-').split('.')[0].replace('_', '__')\nOUTPUT_BASE_PATH = 'output/python-smooth/{}/'.format(date_component)\n\n# Please set here the encoding profiles. You can modify height, bitrate and fps.\nencoding_profiles_h264 = [\n dict(height=240, bitrate=400, fps=None, profile=H264Profile.HIGH),\n dict(height=360, bitrate=800, fps=None, profile=H264Profile.HIGH),\n dict(height=480, bitrate=1200, fps=None, profile=H264Profile.HIGH),\n dict(height=720, bitrate=2400, fps=None, profile=H264Profile.HIGH),\n]\n\ndef main():\n bitmovin = Bitmovin(api_key=API_KEY)\n\n s3_input = S3Input(access_key=S3_INPUT_ACCESSKEY,\n secret_key=S3_INPUT_SECRETKEY,\n bucket_name=S3_INPUT_BUCKETNAME,\n name='Sample S3 Output')\n s3_input = bitmovin.inputs.S3.create(s3_input).resource\n\n s3_output = S3Output(access_key=S3_OUTPUT_ACCESSKEY,\n secret_key=S3_OUTPUT_SECRETKEY,\n bucket_name=S3_OUTPUT_BUCKETNAME,\n name='Sample S3 Output')\n\n s3_output = bitmovin.outputs.S3.create(s3_output).resource\n\n acl_entry = ACLEntry(permission=ACLPermission.PUBLIC_READ)\n\n encoding = Encoding(name='example mp4 encoding for smooth + playready',\n cloud_region=CloudRegion.GOOGLE_EUROPE_WEST_1)\n encoding = bitmovin.encodings.Encoding.create(encoding).resource\n\n encoding_configs = []\n\n # Iterate over all encoding profiles and create the H264 configuration with the defined height and bitrate.\n for idx, _ in enumerate(encoding_profiles_h264):\n profile_h264 = encoding_profiles_h264[idx]\n encoding_config = dict(profile_h264=profile_h264)\n h264_codec = H264CodecConfiguration(\n name='H264 Codec {}p {}k Configuration'.format(profile_h264.get('height'),\n profile_h264.get('bitrate')),\n bitrate=profile_h264.get('bitrate') * 1000,\n height=profile_h264.get('height'),\n profile=profile_h264.get('profile'),\n rate=profile_h264.get(\"fps\"))\n encoding_config['h264_codec'] = bitmovin.codecConfigurations.H264.create(h264_codec).resource\n encoding_configs.append(encoding_config)\n\n audio_codec_configuration = AACCodecConfiguration(name='example_audio_codec_configuration_english',\n bitrate=128000,\n rate=48000)\n\n audio_codec_configuration = bitmovin.codecConfigurations.AAC.create(audio_codec_configuration).resource\n\n video_input_stream = StreamInput(input_id=s3_input.id,\n input_path=S3_INPUT_PATH,\n selection_mode=SelectionMode.AUTO)\n audio_input_stream = StreamInput(input_id=s3_input.id,\n input_path=S3_INPUT_PATH,\n selection_mode=SelectionMode.AUTO)\n\n # With the configurations and the input file streams are now created and muxed later on.\n for encoding_config in encoding_configs:\n encoding_profile = encoding_config.get(\"profile_h264\")\n video_stream_condition = Condition(attribute=\"HEIGHT\", operator=\">=\", value=str(encoding_profile.get('height')))\n video_stream_h264 = Stream(codec_configuration_id=encoding_config.get(\"h264_codec\").id,\n input_streams=[video_input_stream],\n conditions=video_stream_condition,\n name='Stream H264 {}p_{}k'.format(encoding_profile.get('height'),\n encoding_profile.get('bitrate')))\n\n encoding_config['h264_stream'] = bitmovin.encodings.Stream.create(object_=video_stream_h264,\n encoding_id=encoding.id).resource\n\n audio_stream = Stream(codec_configuration_id=audio_codec_configuration.id,\n input_streams=[audio_input_stream],\n name='Sample Stream AUDIO')\n\n audio_stream = bitmovin.encodings.Stream.create(object_=audio_stream, encoding_id=encoding.id).resource\n\n for encoding_config in encoding_configs:\n encoding_profile = encoding_config.get(\"profile_h264\")\n video_muxing_stream_h264 = MuxingStream(encoding_config.get(\"h264_stream\").id)\n video_muxing_output_h264 = EncodingOutput(output_id=s3_output.id, output_path=OUTPUT_BASE_PATH, acl=[acl_entry])\n\n video_muxing_h264 = MP4Muxing(filename='video_{}p.ismv'.format(encoding_profile.get('height')),\n fragment_duration=4000,\n streams=[video_muxing_stream_h264],\n outputs=[video_muxing_output_h264],\n name='Sample Muxing {}p'.format(encoding_profile.get('height')))\n\n encoding_config['h264_muxing'] = bitmovin.encodings.Muxing.MP4.create(object_=video_muxing_h264,\n encoding_id=encoding.id).resource\n\n playready_drm = PlayReadyDRM(key_seed=PLAYREADY_KEYSEED,\n kid=PLAYREADY_KID,\n method=PlayReadyMethod.PIFF_CTR,\n la_url=PLAYREADY_LA_URL,\n outputs=[video_muxing_output_h264],\n name=\"PlayReady\")\n\n encoding_config['playready_drm'] = bitmovin.encodings.Muxing.MP4.DRM.PlayReady.create(object_=playready_drm,\n encoding_id=encoding.id,\n muxing_id=encoding_config['h264_muxing'].id).resource\n\n audio_muxing_stream = MuxingStream(audio_stream.id)\n audio_muxing_output = EncodingOutput(output_id=s3_output.id, output_path=OUTPUT_BASE_PATH, acl=[acl_entry])\n\n audio_muxing = MP4Muxing(filename='audio.isma',\n fragment_duration=4000,\n streams=[audio_muxing_stream],\n outputs=[audio_muxing_output],\n name='Sample Muxing AUDIO')\n\n audio_muxing = bitmovin.encodings.Muxing.MP4.create(object_=audio_muxing, encoding_id=encoding.id).resource\n\n playready_audio = PlayReadyDRM(key_seed=PLAYREADY_KEYSEED,\n kid=PLAYREADY_KID,\n method=PlayReadyMethod.PIFF_CTR,\n la_url=PLAYREADY_LA_URL,\n outputs=[audio_muxing_output],\n name='PlayReady')\n\n playready_audio = bitmovin.encodings.Muxing.MP4.DRM.PlayReady.create(object_=playready_audio,\n encoding_id=encoding.id,\n muxing_id=audio_muxing.id).resource\n\n bitmovin.encodings.Encoding.start(encoding_id=encoding.id)\n\n try:\n bitmovin.encodings.Encoding.wait_until_finished(encoding_id=encoding.id)\n except BitmovinError as bitmovin_error:\n print(\"Exception occurred while waiting for encoding to finish: {}\".format(bitmovin_error))\n\n manifest_output = EncodingOutput(output_id=s3_output.id,\n output_path=OUTPUT_BASE_PATH,\n acl=[acl_entry])\n\n smooth_manifest = SmoothManifest(server_manifest_name='example_manifest_smooth.ism',\n client_manifest_name='example_manifest_smooth.ismc',\n outputs=[manifest_output],\n name='Sample SmoothStreaming Manifest')\n smooth_manifest = bitmovin.manifests.Smooth.create(object_=smooth_manifest).resource\n\n for encoding_config in encoding_configs:\n encoding_profile = encoding_config.get(\"profile_h264\")\n muxing = encoding_config.get('h264_muxing')\n mp4_representation = MP4Representation(encoding_id=encoding.id,\n muxing_id=muxing.id,\n media_file='video_{}p.ismv'.format(encoding_profile.get('height')))\n\n encoding_config['h264_smooth'] = bitmovin.manifests.Smooth.MP4Representation.create(manifest_id=smooth_manifest.id,\n object_=mp4_representation)\n\n mp4_representation_audio = MP4Representation(encoding_id=encoding.id,\n muxing_id=audio_muxing.id,\n media_file='audio.isma')\n\n bitmovin.manifests.Smooth.MP4Representation.create(manifest_id=smooth_manifest.id, object_=mp4_representation_audio)\n\n content_protection = SmoothContentProtection(encoding_id=encoding.id,\n muxing_id=audio_muxing.id,\n drm_id=playready_audio.id)\n\n bitmovin.manifests.Smooth.ContentProtection.create(object_=content_protection, manifest_id=smooth_manifest.id)\n\n bitmovin.manifests.Smooth.start(manifest_id=smooth_manifest.id)\n\n try:\n bitmovin.manifests.Smooth.wait_until_finished(manifest_id=smooth_manifest.id)\n except BitmovinError as bitmovin_error:\n print(\"Exception occurred while waiting for Smooth manifest creation to finish: {}\".format(bitmovin_error))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"examples/encoding/create_simple_mp4_smooth_playready_encoding.py","file_name":"create_simple_mp4_smooth_playready_encoding.py","file_ext":"py","file_size_in_byte":10832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"387497506","text":"#!/usr/bin/python3\n''' Project for studying doctest in python '''\n\n\ndef print_square(size):\n '''Function that prints a square'''\n\n if not isinstance(size, int):\n raise TypeError(\"size must be an integer\")\n\n if size < 0:\n raise ValueError(\"size must be >= 0\")\n\n for _ in range(size):\n print('#' * size)\n","sub_path":"0x07-python-test_driven_development/4-print_square.py","file_name":"4-print_square.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"329847943","text":"import numpy as np\nimport random\nimport matplotlib.pyplot as plt\nimport time\nimport csv\n\nclass GeneticAlgorithm(object):\n def __init__(self, pop_size, gen_size, mutation_rate, crossover_rate, epochs):\n self.pop_size = pop_size # no.chromosomes selected\n self.gen_size = gen_size # no.variables = 50\n # self.K = K # no.mem/tournament\n self.mutation_rate = mutation_rate # mutation rate\n self.crossover_rate = crossover_rate # crossover_rate\n self.epochs = epochs\n\n def initial_population(self):\n pop = [np.random.uniform(-10, 10, self.gen_size) for _ in range(0, self.pop_size)] \n pop = np.array(pop)\n return pop # initial population\n\n # fitness function: pop_size (array: sum)\n def get_fitness(self, pop):\n fitness = np.zeros((self.pop_size, 1))\n for i in range(0, self.pop_size):\n sum = 0\n # print(pop[i])\n for j in range(0, self.gen_size):\n if (j % 2 == 0):\n sum += pop[i, j] ** 2\n else:\n sum += pop[i, j] ** 3\n fitness[i] = sum\n return fitness\n\n # get best chromo\n def get_best(self, fitness):\n return min(fitness)\n\n # select parent by fitness\n def tournament_selection(self, fitness):\n i = random.randint(0, self.pop_size - 1)\n j = random.randint(0, self.pop_size - 1)\n while (i == j):\n j = random.randint(0, self.pop_size - 1)\n\n if (fitness[i] < fitness[j]):\n return i\n else:\n return j\n \n # pop = [x1, x2, ... ,x50]\n def select_parents(self, pop, fitness):\n parents = np.zeros((self.pop_size, self.gen_size))\n for i in range(0, self.pop_size):\n parents[i] = pop[self.tournament_selection(fitness)]\n return parents\n\n # par1, par2 = [x1, x2, ... ,x50]\n def crossover_OX1(self, par1, par2):\n f = np.random.random_sample()\n if (f < self.crossover_rate):\n cp1 = random.randint(1, self.gen_size - 2)\n cp2 = random.randint(1, self.gen_size - 2)\n while (cp1 >= cp2):\n cp1 = random.randint(1, self.gen_size - 2)\n cp2 = random.randint(1, self.gen_size - 2)\n \n child = np.zeros((1, self.gen_size))\n child[0, cp1:cp2+1] = par1[cp1:cp2+1]\n\n temp = np.zeros((1, self.gen_size))\n id = 0\n for i in range(cp2+1, self.gen_size):\n if (par2[i] not in par1[cp1:cp2+1]):\n temp[0, id] = par2[i]\n id += 1 \n for i in range(cp2+1):\n if (par2[i] not in par1[cp1:cp2+1]):\n temp[0, id] = par2[i]\n id += 1 \n k = self.gen_size - cp2 - 1\n child[0, cp2+1:] = temp[0, :k]\n child[0, :cp1] = temp[0, k:k + cp1]\n return child\n else:\n return par1\n\n # mutation with random resetting \n def random_resetting(self, child):\n f = np.random.random_sample()\n if (f < self.mutation_rate):\n index = random.randint(0, self.gen_size - 1)\n num = np.random.uniform(-10, 10, 1)\n child[index] = num\n return child\n\n def creat_child(self, parents):\n child = np.zeros((self.pop_size, self.gen_size))\n for k in range(self.pop_size):\n i = random.randint(0, self.pop_size - 1)\n j = random.randint(0, self.pop_size - 1)\n while (i == j):\n j = random.randint(0, self.pop_size - 1)\n child[k, :] = self.crossover_OX1(parents[i], parents[j])\n \n for k in range(0, self.pop_size):\n child[k] = self.random_resetting(child[k])\n return child\n\n def draw_chart(self, Epochs, TS_OX1_RS, mean_time, score):\n plt.plot(Epochs, TS_OX1_RS, 'r-')\n plt.axis([0, self.epochs, -25000, 25000])\n plt.xlabel('Epochs')\n plt.ylabel('Best score')\n plt.text(1800, 10000, str(mean_time))\n plt.text(1800, 8000, str(score))\n plt.show()\n\n def implement_with_RS(self, pop):\n sta_time = []\n best = []\n \n Epochs = np.arange(1, self.epochs + 1, dtype=int)\n Epochs = np.reshape(Epochs, (self.epochs, 1))\n TS_OX1_RS = np.zeros((self.epochs, 1), dtype=float)\n\n fitness = self.get_fitness(pop)\n parents = self.select_parents(pop, fitness)\n with open('D:\\\\Lab609\\\\GeneticAlgorithm\\\\TS_OX1_RS.csv', 'w') as csvfile:\n fieldnames = ['Epochs', 'TS_OX1_RS']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n for id in range(0, self.epochs):\n start = time.clock()\n child = np.zeros((self.pop_size, self.gen_size))\n child = self.creat_child(parents)\n \n self.pop_size = self.pop_size\n child_score = self.get_fitness(child)\n best_score = self.get_best(child_score)\n TS_OX1_RS[id] = best_score\n\n fitness = self.get_fitness(child)\n parents = self.select_parents(child, fitness)\n \n writer.writerow({'Epochs' : Epochs[id,0], 'TS_OX1_RS' : TS_OX1_RS[id,0]})\n\n time_waste = time.clock() - start\n sta_time.append(time_waste)\n best.append(best_score)\n \n print(\"Iteration: \", id + 1, \", best score: \", best_score, \", time: \",time_waste)\n \n mean_time = sum(sta_time)/len(sta_time)\n score = min(best)\n\n # draw chart\n self.draw_chart(Epochs, TS_OX1_RS, mean_time, score)\n\nif __name__ == \"__main__\":\n pop_size = 100\n gen_size = 50\n mutation_rate = 0.03\n crossover_rate = 0.65#0.9\n epochs = 3000\n GA = GeneticAlgorithm(pop_size, gen_size, mutation_rate, crossover_rate, epochs)\n population = GA.initial_population()\n GA.implement_with_RS(population)","sub_path":"anh_duc/week1/GA_with_TS_OX1_RS.py","file_name":"GA_with_TS_OX1_RS.py","file_ext":"py","file_size_in_byte":6113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"253924720","text":"from __future__ import absolute_import\n\nfrom datetime import datetime\n\nfrom flask import current_app\nimport mock\n\nfrom changes.config import db\nfrom changes.constants import Result, Status\nfrom changes.testutils import TestCase\nfrom changes.models import FailureReason\nfrom changes.listeners.analytics_notifier import (\n build_finished_handler, _get_phabricator_revision_url, _get_failure_reasons\n)\n\n\ndef ts_to_datetime(ts):\n return datetime.utcfromtimestamp(ts)\n\n\nclass AnalyticsNotifierTest(TestCase):\n\n def setUp(self):\n super(AnalyticsNotifierTest, self).setUp()\n\n def _set_config_url(self, url):\n current_app.config['ANALYTICS_POST_URL'] = url\n\n @mock.patch('changes.listeners.analytics_notifier.post_build_data')\n def test_no_url(self, post_fn):\n self._set_config_url(None)\n project = self.create_project(name='test', slug='test')\n build = self.create_build(project, result=Result.failed)\n build_finished_handler(build_id=build.id.hex)\n self.assertEquals(post_fn.call_count, 0)\n\n @mock.patch('changes.listeners.analytics_notifier.post_build_data')\n def test_failed_build(self, post_fn):\n URL = \"https://analytics.example.com/report?source=changes\"\n self._set_config_url(URL)\n project = self.create_project(name='test', slug='project-slug')\n self.assertEquals(post_fn.call_count, 0)\n duration = 1234\n created = 1424998888\n started = created + 10\n finished = started + duration\n\n build = self.create_build(project, result=Result.failed, target='D1',\n label='Some sweet diff', duration=duration,\n date_created=ts_to_datetime(created), date_started=ts_to_datetime(started),\n date_finished=ts_to_datetime(finished))\n\n job = self.create_job(build=build, result=Result.failed)\n jobphase = self.create_jobphase(job)\n jobstep = self.create_jobstep(jobphase, status=Status.finished, result=Result.failed)\n db.session.add(FailureReason(step_id=jobstep.id, job_id=job.id, build_id=build.id, project_id=project.id,\n reason='missing_tests'))\n db.session.commit()\n\n with mock.patch('changes.listeners.analytics_notifier._get_phabricator_revision_url') as mock_get_phab:\n mock_get_phab.return_value = 'https://example.com/D1'\n with mock.patch('changes.listeners.analytics_notifier._get_failure_reasons') as mock_get_failures:\n mock_get_failures.return_value = ['aborted', 'missing_tests']\n build_finished_handler(build_id=build.id.hex)\n\n expected_data = {\n 'build_id': build.id.hex,\n 'number': 1,\n 'target': 'D1',\n 'project_slug': 'project-slug',\n 'result': 'Failed',\n 'label': 'Some sweet diff',\n 'is_commit': True,\n 'duration': 1234,\n 'date_created': created,\n 'date_started': started,\n 'date_finished': finished,\n 'phab_revision_url': 'https://example.com/D1',\n 'failure_reasons': ['aborted', 'missing_tests'],\n }\n post_fn.assert_called_once_with(URL, expected_data)\n\n def test_get_failure_reasons_no_failures(self):\n project = self.create_project(name='test', slug='project-slug')\n build = self.create_build(project, result=Result.passed, target='D1',\n label='Some sweet diff')\n self.assertEquals(_get_failure_reasons(build), [])\n\n def test_get_failure_reasons_multiple_failures(self):\n project = self.create_project(name='test', slug='project-slug')\n build = self.create_build(project, result=Result.failed, target='D1',\n label='Some sweet diff')\n job = self.create_job(build=build, result=Result.failed)\n jobphase = self.create_jobphase(job)\n jobstep = self.create_jobstep(jobphase, status=Status.finished, result=Result.failed)\n for reason in ('missing_tests', 'timeout', 'aborted'):\n db.session.add(FailureReason(step_id=jobstep.id, job_id=job.id, build_id=build.id, project_id=project.id,\n reason=reason))\n jobstep2 = self.create_jobstep(jobphase, status=Status.finished, result=Result.failed)\n for reason in ('timeout', 'insufficient_politeness'):\n db.session.add(FailureReason(step_id=jobstep2.id, job_id=job.id, build_id=build.id, project_id=project.id,\n reason=reason))\n db.session.commit()\n\n self.assertEquals(_get_failure_reasons(build),\n ['aborted', 'insufficient_politeness', 'missing_tests', 'timeout'])\n\n def test_get_phab_revision_url_diff(self):\n project = self.create_project(name='test', slug='test')\n source_data = {'phabricator.revisionURL': 'https://tails.corp.dropbox.com/D6789'}\n source = self.create_source(project, data=source_data)\n build = self.create_build(project, result=Result.failed, source=source, message='Some commit')\n self.assertEquals(_get_phabricator_revision_url(build), 'https://tails.corp.dropbox.com/D6789')\n\n def test_get_phab_revision_url_commit(self):\n project = self.create_project(name='test', slug='test')\n source_data = {}\n source = self.create_source(project, data=source_data)\n msg = \"\"\"\n Some fancy commit.\n\n Summary: Fixes T33417.\n\n Test Plan: Added tests.\n\n Reviewers: mickey\n\n Reviewed By: mickey\n\n Subscribers: changesbot\n\n Maniphest Tasks: T33417\n\n Differential Revision: https://tails.corp.dropbox.com/D6789\"\"\"\n build = self.create_build(project, result=Result.failed, source=source, message=msg)\n self.assertEquals(_get_phabricator_revision_url(build), 'https://tails.corp.dropbox.com/D6789')\n\n def test_get_phab_revision_url_commit_conflict(self):\n project = self.create_project(name='test', slug='test')\n source_data = {}\n source = self.create_source(project, data=source_data)\n msg = \"\"\"\n Some fancy commit.\n\n Summary: Fixes T33417.\n Adds messages like:\n Differential Revision: https://tails.corp.dropbox.com/D1234\n\n Test Plan: Added tests.\n\n Reviewers: mickey\n\n Reviewed By: mickey\n\n Subscribers: changesbot\n\n Maniphest Tasks: T33417\n\n Differential Revision: https://tails.corp.dropbox.com/D6789\"\"\"\n build = self.create_build(project, result=Result.failed, source=source, message=msg)\n self.assertEquals(_get_phabricator_revision_url(build), None)\n\n def test_get_phab_revision_url_no_message(self):\n project = self.create_project(name='test', slug='test')\n source_data = {}\n source = self.create_source(project, data=source_data)\n build = self.create_build(project, result=Result.failed, source=source, message=None)\n self.assertEquals(_get_phabricator_revision_url(build), None)\n","sub_path":"tests/changes/listeners/test_analytics_notifier.py","file_name":"test_analytics_notifier.py","file_ext":"py","file_size_in_byte":7161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"231797449","text":"import cv2\nimport numpy as np\nimport random\n\nO = np.ones((256,256,1,1))\nZ = np.zeros((256,256,1,1))\n\nA = np.expand_dims(cv2.imread('./I_sharp.png'),axis=3) / 255\nB = np.expand_dims(cv2.imread('./I_blurred.png'),axis=3) / 255\n\nR = np.concatenate((Z,Z,O),axis=2)\nG = np.concatenate((Z,O,Z),axis=2)\n\ndata = np.concatenate((A,B,R,G),axis=3)\n\nwhile(1):\n\tidx = random.randint(0,3)\n\n\tframe = data[:,:,:,idx]\n\n\tcv2.imshow('Frame', frame)\n\n\tif cv2.waitKey(500) & 0xFF == ord('q'):\n\t\tbreak\n","sub_path":"python/generateTest.py","file_name":"generateTest.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"557074781","text":"# -*- coding: utf-8 -*-\n\nfrom flask import jsonify, make_response, current_app\nfrom flask.ext.restful import Resource\nfrom module.user.user import User as UserModel\nfrom module.user.userinfo import UserInfo as UserInfoModel\nfrom module.user.useralipay import UserAlipay\nfrom module.user.user_wechat import UserWechat\nfrom flask.ext.login import current_user\nfrom control.pp import logit\nfrom control.ErrorMessages import ErrorMessages as errMsgs\nfrom view.tools.tools import sendException\n\n\nclass MyUserInfo(Resource):\n\n @logit\n def get(self):\n try:\n if current_user.is_authenticated():\n uid = current_user.id\n user = UserModel.query.get(uid)\n info = UserInfoModel.query.filter_by(uid=uid).first()\n alipay = UserAlipay.query.filter_by(\n uid=uid, isValid=True).first()\n alipay_acct = alipay.alipay_account if alipay else ''\n wechat = UserWechat.query.filter_by(uid=uid).first()\n wechat_acct = wechat.wechat_openid if wechat else ''\n\n if user and info:\n userlist = {\n 'id': user.id,\n 'nickname': user.nickname,\n 'email': user.email,\n 'avatar': info.avatar,\n 'alipay_acct': alipay_acct,\n 'wechat_acct': wechat_acct\n }\n return make_response(jsonify({\"user\": userlist}), 200)\n make_response(jsonify({'messages': errMsgs.NOT_EXIST}), 404)\n return make_response(jsonify({'messages': errMsgs.NOT_LOGIN}), 401)\n except Exception as e:\n current_app.logger.exception('MyUserInfo get()')\n sendException(e, 'MyUserInfo get()')\n return make_response(jsonify({'messages': errMsgs.SERVER_ERROR}), 500)\n","sub_path":"api/user/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"544834155","text":"import newspaper\nfrom newspaper import Article\nimport os\nfrom queue import Queue\nimport json\nimport time\nfrom fake_useragent import UserAgent\nimport telnetlib\nimport random\nimport requests\nimport pymysql\n\n\nclass NewsSpider:\n\n def __init__(self):\n self.current_path = os.path.dirname(__file__)\n self.main_url_file = 'platform list.txt'\n self.file_path = os.path.join(self.current_path, self.main_url_file)\n self.news_paper_size = None\n self.news_brand = None\n self.Article_detas_list = None\n self.user_agent_list = None\n self.ip_address_list = None\n self.Article_details = {}\n self.connet_mysql = None\n self.cursor_db = None\n self.art_url = None \n self.article_url_queue = Queue()\n\n # # if platform list.txt is empty, get populate url list and write into the file\n def url_path(self):\n if os.path.getsize(self.file_path) == 0:\n with open(self.file_path, \"w+\", encoding=\"utf-8\") as uf:\n popular_urls_list= newspaper.popular_urls()\n for i in range(len(popular_urls_list)):\n uf.write(popular_urls_list[i])\n uf.write(\"\\n\")\n print(\"populate url list have worte\")\n\n # get different platform url\n def platform_url_list(self):\n platform_list = []\n with open(self.file_path, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n line = line.strip()\n print(line)\n platform_list.append(line)\n print(platform_list)\n return platform_list\n print(\"*******end of get platform_url*********\")\n # build source and get articles urls\n\n def articles_url_list(self,plat_url_list):\n print(\"*******start get articles_url*********\")\n for platfrom_url in plat_url_list:\n news_paper = newspaper.build(platfrom_url,memoize_articles=False) #got all\n# news_paper = newspaper.build(platfrom_url) # got from start\n for article in news_paper.articles:\n article_list = []\n self.news_brand = news_paper.brand\n article_list.append(self.news_brand)\n article_list.append(article.url)\n print(article_list)\n self.article_url_queue.put(article_list)\n print(\"have got\", self.news_brand, news_paper.size(), \"articles'\")\n print(\"*******end of get articles_url*********\")\n\n # download articles and parse\n def parse_article(self):\n print(\"*******start of parse article*********\")\n self.user_agent_list = self.User_Agent()\n# self.ip_address_list = self.get_ip_address()\n self.connect_mysql()\n while not self.article_url_queue.empty():\n article_url = self.article_url_queue.get()\n print(article_url[1])\n self.art_url=article_url[1]\n verfity_result = self.verfity_art_url(article_url[1])\n if verfity_result == 200:\n print(\"sleep 3 secs\")\n time.sleep(3)\n Article_html = Article(url=article_url[1])\n try:\n Article_html.download()\n except Exception:\n print(\"error in url\",Article_html)\n continue\n else:\n Article_html.parse()\n self.Article_details = {}\n self.Article_details[\"class\"] = article_url[0]\n self.Article_details[\"title\"] = Article_html.title if len(Article_html.title) > 0 else ' '\n self.Article_details[\"top_image\"] = Article_html.top_image if len(Article_html.top_image) > 0 else ' '\n self.Article_details[\"author\"] = Article_html.authors if len(Article_html.authors) > 0 else ' '\n self.Article_details[\"Image_list\"] = Article_html.images if len(Article_html.images) > 0 else ' '\n self.Article_details[\"Videos\"] = Article_html.movies if len(Article_html.movies) > 0 else ' '\n self.Article_details[\"Text\"] = Article_html.text if len(Article_html.text) > 0 else ' '\n if self.Article_details[\"Text\"] and self.Article_details[\"title\"] is not ' ':\n Article_html.nlp()\n self.Article_details[\"summary\"] = Article_html.summary if len(Article_html.summary) > 0 else ' '\n self.Article_details[\"keywords\"] = Article_html.keywords if len(Article_html.keywords) > 0 else ' '\n else:\n self.Article_details[\"summary\"] = ' '\n self.Article_details[\"keywords\"] = ' '\n print(self.Article_details)\n# self.save_data(Article_details)\n self.save_into_db()\n else:\n print(\"invalid article, pass\")\n self.article_url_queue.task_done()\n print(\"*******end of get parse_article*********\")\n\n def User_Agent(self):\n ua = UserAgent(verify_ssl=False)\n print(\"got UserAgent\")\n return ua\n\n def verfity_art_url(self,article_url):\n user_agent = self.user_agent_list.random\n headers = {\"user-agent\":user_agent}\n print(headers)\n url = article_url\n response = requests.get(url=url, headers=headers, timeout=5)\n print(response.status_code)\n return response.status_code\n\n def connect_mysql(self):\n self.connet_mysql = pymysql.connect(host='127.0.0.1',\n user='root',\n password ='Max13579',\n db ='newsdata',\n port = 3306,\n charset='utf8')\n self.cursor = self.connet_mysql.cursor()\n print(\"connected to MySQL DB\")\n\n # save article data, use mysqldb to repleace txt\n# def save_data(self,Article_detas):\n# print(\"*******start save_data*********\")\n# file_path = os.path.join(self.current_path ,'news details.txt')\n# with open(file_path, \"a\", encoding=\"utf-8\") as pf:\n# # for content in Article_detas_list:\n# details = str(Article_detas)\n# pf.write(json.dumps(details, ensure_ascii=False, indent=1))\n# pf.write(\"\\n\")\n# print(\"saved successfully\")\n\n def save_into_db(self):\n sql = 'insert into news_content(news_platform,news_title,news_top_image_url,news_author,news_image_list,news_videos,news_text,news_summary,news_keywords,news_url) values(\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\",\"%s\")' \\\n %(pymysql.escape_string(self.Article_details[\"class\"]),\n pymysql.escape_string(self.Article_details[\"title\"]),\n pymysql.escape_string(self.Article_details[\"top_image\"]),\n self.Article_details[\"author\"],\n self.Article_details[\"Image_list\"],\n pymysql.escape_string(self.Article_details[\"Videos\"]),\n pymysql.escape_string(self.Article_details[\"Text\"]),\n pymysql.escape_string(self.Article_details[\"summary\"]),\n self.Article_details[\"keywords\"],\n pymysql.escape_string(self.art_url))\n print(sql)\n try:\n self.cursor.execute(sql)\n self.connet_mysql.commit()\n except:\n print(\"*****************************error at sql %s while save data into DB\" %sql)\n\n # main logic:\n def run(self):\n self.url_path()\n # 1. prepare url list for different platform\n plat_url_list = self.platform_url_list()\n # 2. according to the different platform to get all the articles url\n self.articles_url_list(plat_url_list)\n # 3. parse url and get article details and parse it. at last save article details into DB\n self.parse_article()\n # 4. close DB\n self.cursor.close()\n self.connet_mysql.close()\n\nif __name__ == '__main__':\n NewsSpider = NewsSpider()\n NewsSpider.run()\n","sub_path":"News_Spider.py","file_name":"News_Spider.py","file_ext":"py","file_size_in_byte":8156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"372653015","text":"#!/usr/bin/env python\nimport random\nimport rclpy\nimport sys\n\nfrom rclpy.executors import MultiThreadedExecutor\nfrom rclpy.callback_groups import MutuallyExclusiveCallbackGroup\nfrom suave.task_bridge_none import TaskBridgeNone\nfrom system_modes_msgs.srv import ChangeMode\nfrom system_modes_msgs.srv import GetAvailableModes\n\n\nclass TaskBridgeRandom(TaskBridgeNone):\n def __init__(self):\n super().__init__()\n\n self.declare_parameter('adaptation_period', 15)\n self.adaptation_period = self.get_parameter('adaptation_period').value\n\n self.generate_path_modes_cli = self.create_client(\n GetAvailableModes,\n '/f_generate_search_path/get_available_modes',\n callback_group=self.client_cb_group)\n\n self.follow_pipeline_modes_cli = self.create_client(\n GetAvailableModes,\n '/f_follow_pipeline/get_available_modes',\n callback_group=self.client_cb_group)\n\n self.available_modes_cli = {\n 'f_generate_search_path': self.generate_path_modes_cli,\n 'f_follow_pipeline': self.follow_pipeline_modes_cli,\n }\n\n self.reasoner_timer = self.create_timer(\n self.adaptation_period,\n self.reasoner_cb,\n callback_group=self.task_cb_group\n )\n\n def reasoner_cb(self):\n for task_name in self.current_tasks:\n function_names = self.task_functions_dict[task_name]\n for function in function_names:\n self.forward_task_request(function)\n\n def forward_task_request(self, function):\n modes_cli = self.available_modes_cli[function]\n mode_name = random.choice(\n self.call_service(\n modes_cli, GetAvailableModes.Request()).available_modes\n )\n\n cli = self.sm_cli_dict[function]\n return self.call_sysmode_change_mode(function, mode_name)\n\n\ndef main():\n print('Starting random task bridge node')\n\n rclpy.init(args=sys.argv)\n\n task_bridge_node = TaskBridgeRandom()\n\n executor = MultiThreadedExecutor()\n rclpy.spin(task_bridge_node, executor=executor)\n\n task_bridge_node.destroy_node()\n rclpy.shutdown()\n","sub_path":"suave_metacontrol/suave_metacontrol/task_bridge_random.py","file_name":"task_bridge_random.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"179304489","text":"################################################################################\n# Modules and functions import statements\n################################################################################\n\nimport logging\nimport sys\n\nfrom flask import url_for, get_flashed_messages\nfrom helpers.app_runtime import jinja2_env, app_settings\n\n################################################################################\n# Functions\n################################################################################\n\ndef get_model(cookie_json=None):\n caller = sys._getframe(1) # '_getframe(1)' gets previous stack; \n # '_getframe()' gets current stack\n caller_name = caller.f_code.co_name # returns 'view_home'\n module_name = caller.f_globals['__name__'] # returns 'modules.default_routes'\n package_name = caller.f_globals['__package__'] # returns 'modules'\n\n context = cookie_json if cookie_json is not None else {}\n context['url_for'] = url_for # function for Flask\n context['get_flashed_messages'] = get_flashed_messages # function for Flask\n context['app_settings'] = app_settings # make application settings available\n context['view_name'] = caller_name\n context['view_module'] = module_name\n context['view_package'] = package_name\n context['view_id'] = \"{0}.{1}\".format(module_name, caller_name)\n\n # context['auth_cookie'] = request.cookies.get(appconfig[\"application\"][\"auth_cookie_name\"])\n # context['current_datetime'] = datetime.now()\n # context = {\n # 'auth_cookie' : request.cookies.get(appconfig[\"application\"][\"auth_cookie_name\"]),\n # 'current_datetime' : datetime.now()\n # }\n return context\n\ndef view(model=None, view_path=None):\n if view_path is None:\n caller = sys._getframe(1) # '_getframe(1)' gets previous stack; \n # '_getframe()' gets current stack\n caller_name = caller.f_code.co_name # returns 'view_home'\n module_name = caller.f_globals['__name__'] # returns 'modules.default_routes'\n package_name = caller.f_globals['__package__'] # returns 'modules'\n\n view_path = module_name.split('.')\n view_path.remove(package_name)\n view_path.append(\"{0}.html\".format(caller_name))\n view_path = '/'.join(view_path) # returns 'default_routes/view_home.html\n\n logging.info(\"fetching view [{0}]\".format(view_path))\n\n if model is None:\n model = get_model()\n\n return jinja2_env.get_template(view_path).render(model)\n\n################################################################################\n# Export module variables\n################################################################################\n \n # N/A\n\n################################################################################\n# Main function\n################################################################################\n\nif __name__ == '__main__':\n pass\n","sub_path":"google-cloud/hci/public-web/helpers/app_helper.py","file_name":"app_helper.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"357720103","text":"# 客户端\n'''\nclient端流程\n1, 建立通信socket\n2, 发送内容到指定服务器\n3, 接受服务器给定的反馈内容\n'''\nimport socket\n\ndef clientFunc():\n sock = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)\n # 给服务器发送\n text = \"i love china\"\n\n # 发送的娥数据必须用bytes格式\n data = text.encode()\n\n # 发送 然后等待\n sock.sendto(data,(\"127.0.0.1\",7852))\n\n # 服务器反馈的内容\n data,addr = sock.recvfrom(500)\n data = data.decode()\n print(data)\n print(addr)\nif __name__ == '__main__':\n clientFunc()","sub_path":"02 高级语法系列/cp net编程/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"212917987","text":"# coding: utf-8\n\n\"\"\"\n Collibra Data Governance Center Core API\n\n

The Core REST API allows you to create your own integrations with Collibra Data Governance Center.

Create custom applications to help users get access to the right data.

# noqa: E501\n\n The version of the OpenAPI document: 2.0\n Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\nfrom collibra_core.configuration import Configuration\n\n\nclass AddAssetTypeAssignmentRuleRequest(object):\n \"\"\"NOTE: This class is auto generated by OpenAPI Generator.\n Ref: https://openapi-generator.tech\n\n Do not edit the class manually.\n \"\"\"\n\n \"\"\"\n Attributes:\n openapi_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n openapi_types = {\n 'workflow_definition_id': 'str',\n 'asset_type_id': 'str',\n 'domain_id': 'str',\n 'community_id': 'str',\n 'status_id': 'str'\n }\n\n attribute_map = {\n 'workflow_definition_id': 'workflowDefinitionId',\n 'asset_type_id': 'assetTypeId',\n 'domain_id': 'domainId',\n 'community_id': 'communityId',\n 'status_id': 'statusId'\n }\n\n def __init__(self, workflow_definition_id=None, asset_type_id=None, domain_id=None, community_id=None, status_id=None, local_vars_configuration=None): # noqa: E501\n \"\"\"AddAssetTypeAssignmentRuleRequest - a model defined in OpenAPI\"\"\" # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration()\n self.local_vars_configuration = local_vars_configuration\n\n self._workflow_definition_id = None\n self._asset_type_id = None\n self._domain_id = None\n self._community_id = None\n self._status_id = None\n self.discriminator = None\n\n self.workflow_definition_id = workflow_definition_id\n self.asset_type_id = asset_type_id\n if domain_id is not None:\n self.domain_id = domain_id\n if community_id is not None:\n self.community_id = community_id\n if status_id is not None:\n self.status_id = status_id\n\n @property\n def workflow_definition_id(self):\n \"\"\"Gets the workflow_definition_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n\n The ID of the workflow definition containing the assignment rule to be added. # noqa: E501\n\n :return: The workflow_definition_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :rtype: str\n \"\"\"\n return self._workflow_definition_id\n\n @workflow_definition_id.setter\n def workflow_definition_id(self, workflow_definition_id):\n \"\"\"Sets the workflow_definition_id of this AddAssetTypeAssignmentRuleRequest.\n\n The ID of the workflow definition containing the assignment rule to be added. # noqa: E501\n\n :param workflow_definition_id: The workflow_definition_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :type: str\n \"\"\"\n if self.local_vars_configuration.client_side_validation and workflow_definition_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `workflow_definition_id`, must not be `None`\") # noqa: E501\n\n self._workflow_definition_id = workflow_definition_id\n\n @property\n def asset_type_id(self):\n \"\"\"Gets the asset_type_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n\n The ID of the asset type the added rule should refer to. # noqa: E501\n\n :return: The asset_type_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :rtype: str\n \"\"\"\n return self._asset_type_id\n\n @asset_type_id.setter\n def asset_type_id(self, asset_type_id):\n \"\"\"Sets the asset_type_id of this AddAssetTypeAssignmentRuleRequest.\n\n The ID of the asset type the added rule should refer to. # noqa: E501\n\n :param asset_type_id: The asset_type_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :type: str\n \"\"\"\n if self.local_vars_configuration.client_side_validation and asset_type_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `asset_type_id`, must not be `None`\") # noqa: E501\n\n self._asset_type_id = asset_type_id\n\n @property\n def domain_id(self):\n \"\"\"Gets the domain_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n\n The ID of the domain the assignment rule should apply for. # noqa: E501\n\n :return: The domain_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :rtype: str\n \"\"\"\n return self._domain_id\n\n @domain_id.setter\n def domain_id(self, domain_id):\n \"\"\"Sets the domain_id of this AddAssetTypeAssignmentRuleRequest.\n\n The ID of the domain the assignment rule should apply for. # noqa: E501\n\n :param domain_id: The domain_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :type: str\n \"\"\"\n\n self._domain_id = domain_id\n\n @property\n def community_id(self):\n \"\"\"Gets the community_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n\n The ID of the community the assignment rule should apply for. # noqa: E501\n\n :return: The community_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :rtype: str\n \"\"\"\n return self._community_id\n\n @community_id.setter\n def community_id(self, community_id):\n \"\"\"Sets the community_id of this AddAssetTypeAssignmentRuleRequest.\n\n The ID of the community the assignment rule should apply for. # noqa: E501\n\n :param community_id: The community_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :type: str\n \"\"\"\n\n self._community_id = community_id\n\n @property\n def status_id(self):\n \"\"\"Gets the status_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n\n The ID of the status the assignment rule should apply for. # noqa: E501\n\n :return: The status_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :rtype: str\n \"\"\"\n return self._status_id\n\n @status_id.setter\n def status_id(self, status_id):\n \"\"\"Sets the status_id of this AddAssetTypeAssignmentRuleRequest.\n\n The ID of the status the assignment rule should apply for. # noqa: E501\n\n :param status_id: The status_id of this AddAssetTypeAssignmentRuleRequest. # noqa: E501\n :type: str\n \"\"\"\n\n self._status_id = status_id\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, AddAssetTypeAssignmentRuleRequest):\n return False\n\n return self.to_dict() == other.to_dict()\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n if not isinstance(other, AddAssetTypeAssignmentRuleRequest):\n return True\n\n return self.to_dict() != other.to_dict()\n","sub_path":"collibra_core/models/add_asset_type_assignment_rule_request.py","file_name":"add_asset_type_assignment_rule_request.py","file_ext":"py","file_size_in_byte":8258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"436462999","text":"from datetime import datetime, timezone\nfrom decimal import Decimal\nfrom os import environ\nfrom pathlib import Path\nfrom uuid import UUID, uuid4\n\nimport requests\nfrom boto3.dynamodb.conditions import Attr, Key\nfrom connexion import problem\nfrom connexion.apps.flask_app import FlaskJSONEncoder\nfrom flask import jsonify, make_response\nfrom flask_cors import CORS\nfrom jsonschema import draft4_format_checker\n\nfrom hyp3_api import DYNAMODB_RESOURCE, connexion_app\nfrom hyp3_api.openapi import get_spec\nfrom hyp3_api.util import convert_floats_to_decimals, format_time, get_remaining_jobs_for_user, \\\n get_request_time_expression\nfrom hyp3_api.validation import GranuleValidationError, validate_jobs\n\n\nclass DecimalEncoder(FlaskJSONEncoder):\n def default(self, o):\n if isinstance(o, Decimal):\n if o == int(o):\n return int(o)\n return float(o)\n return super(DecimalEncoder, self).default(o)\n\n\n@draft4_format_checker.checks('uuid')\ndef is_uuid(val):\n try:\n UUID(val, version=4)\n except ValueError:\n return False\n return True\n\n\n@connexion_app.app.before_request\ndef check_system_available():\n if environ['SYSTEM_AVAILABLE'] != \"true\":\n message = 'HyP3 is currently unavailable. Please try again later.'\n error = {\n 'detail': message,\n 'status': 503,\n 'title': 'Service Unavailable',\n 'type': 'about:blank'\n }\n return make_response(jsonify(error), 503)\n\n\ndef post_jobs(body, user):\n print(body)\n\n quota = get_user(user)['quota']\n if quota['remaining'] - len(body['jobs']) < 0:\n max_jobs = quota['max_jobs_per_month']\n message = f'Your monthly quota is {max_jobs} jobs. You have {quota[\"remaining\"]} jobs remaining.'\n return problem(400, 'Bad Request', message)\n\n try:\n validate_jobs(body['jobs'])\n except requests.HTTPError as e:\n print(f'WARN: CMR search failed: {e}')\n except GranuleValidationError as e:\n return problem(400, 'Bad Request', str(e))\n\n request_time = format_time(datetime.now(timezone.utc))\n table = DYNAMODB_RESOURCE.Table(environ['JOBS_TABLE_NAME'])\n\n for job in body['jobs']:\n job['job_id'] = str(uuid4())\n job['user_id'] = user\n job['status_code'] = 'PENDING'\n job['request_time'] = request_time\n if not body.get('validate_only'):\n job = convert_floats_to_decimals(job)\n table.put_item(Item=job)\n\n return body\n\n\ndef get_jobs(user, start=None, end=None, status_code=None, name=None):\n table = DYNAMODB_RESOURCE.Table(environ['JOBS_TABLE_NAME'])\n\n key_expression = Key('user_id').eq(user)\n if start is not None or end is not None:\n key_expression &= get_request_time_expression(start, end)\n\n filter_expression = Attr('job_id').exists()\n if status_code is not None:\n filter_expression &= Attr('status_code').eq(status_code)\n if name is not None:\n filter_expression &= Attr('name').eq(name)\n\n response = table.query(\n IndexName='user_id',\n KeyConditionExpression=key_expression,\n FilterExpression=filter_expression,\n )\n return {'jobs': response['Items']}\n\n\ndef get_job_by_id(job_id):\n table = DYNAMODB_RESOURCE.Table(environ['JOBS_TABLE_NAME'])\n response = table.get_item(Key={'job_id': job_id})\n if 'Item' not in response:\n return problem(404, 'Not Found', f'job_id does not exist: {job_id}')\n return response['Item']\n\n\ndef get_names_for_user(user):\n table = DYNAMODB_RESOURCE.Table(environ['JOBS_TABLE_NAME'])\n key_expression = Key('user_id').eq(user)\n response = table.query(\n IndexName='user_id',\n KeyConditionExpression=key_expression,\n )\n names = {record['name'] for record in response['Items'] if 'name' in record}\n return sorted(list(names))\n\n\ndef get_max_jobs_per_month(user):\n table = DYNAMODB_RESOURCE.Table(environ['USERS_TABLE_NAME'])\n response = table.get_item(Key={'user_id': user})\n if 'Item' in response:\n max_jobs_per_month = response['Item']['max_jobs_per_month']\n else:\n max_jobs_per_month = int(environ['MONTHLY_JOB_QUOTA_PER_USER'])\n return max_jobs_per_month\n\n\ndef get_user(user):\n max_jobs = get_max_jobs_per_month(user)\n\n return {\n 'user_id': user,\n 'quota': {\n 'max_jobs_per_month': max_jobs,\n 'remaining': get_remaining_jobs_for_user(user, max_jobs),\n },\n 'job_names': get_names_for_user(user)\n }\n\n\napi_spec_file = Path(__file__).parent / 'api-spec' / 'openapi-spec.yml'\napi_spec = get_spec(api_spec_file)\nconnexion_app.app.json_encoder = DecimalEncoder\nconnexion_app.add_api(api_spec, validate_responses=True, strict_validation=True)\nCORS(connexion_app.app, origins=r'https?://([-\\w]+\\.)*asf\\.alaska\\.edu', supports_credentials=True)\n","sub_path":"apps/api/src/hyp3_api/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":4864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"309460293","text":"\nfrom Networks import *\nfrom torch import optim\nimport torch\nimport time\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nimport numpy as np\nimport torchvision.transforms.functional as TF\n\nclass Sketch_Classification(nn.Module):\n def __init__(self, hp):\n super(Sketch_Classification, self).__init__()\n self.Network = eval(hp.backbone_name + '_Network(hp)')\n self.train_params = self.parameters()\n self.optimizer = optim.Adam(self.train_params, hp.learning_rate)\n self.loss = nn.CrossEntropyLoss()\n self.hp = hp\n self.step = 0\n\n def train_supervised(self, batch, step):\n self.train()\n self.step = step\n self.optimizer.zero_grad()\n output = self.Network(batch['sketch_img'].to(device))\n\n loss = self.loss(output, batch['sketch_label'].to(device))\n loss.backward()\n self.optimizer.step()\n return loss.item()\n\n def train_rotation_self_supervised(self, batch, step):\n\n batch_image_4x = []\n label_4x = []\n for image in batch['sketch_img']:\n batch_image_4x.append(image)\n label_4x.append(0)\n image = TF.normalize(image, mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],\n std=[1/0.229, 1/0.224, 1/0.225])\n pil_image = TF.to_pil_image(image)\n tensor_rotate = TF.normalize(TF.to_tensor(TF.rotate(pil_image, 90)),\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n batch_image_4x.append(tensor_rotate)\n label_4x.append(1)\n tensor_rotate = TF.normalize(TF.to_tensor(TF.rotate(pil_image, 180)),\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n batch_image_4x.append(tensor_rotate)\n label_4x.append(2)\n tensor_rotate = TF.normalize(TF.to_tensor(TF.rotate(pil_image, 270)),\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n batch_image_4x.append(tensor_rotate)\n label_4x.append(3)\n\n random_indices = torch.randperm(len(label_4x))\n batch_image_4x = torch.stack(batch_image_4x)[random_indices]\n label_4x = torch.tensor(label_4x)[random_indices]\n\n self.train()\n self.step = step\n self.optimizer.zero_grad()\n output = self.Network(batch_image_4x.to(device))\n loss = self.loss(output, label_4x.to(device))\n loss.backward()\n self.optimizer.step()\n return loss.item()\n\n\n def fine_tune_linear(self, datloader_Train, datloader_Test):\n self.freeze_weights()\n\n Train_Image_Feature = {}\n Train_Image_Label = []\n\n Test_Image_Feature = {}\n Test_Image_Label = []\n\n start_time = time.time()\n self.eval()\n\n with torch.no_grad():\n\n for i_batch, sampled_batch in enumerate(datloader_Train):\n sketch_feature = self.Network.extract_features(sampled_batch['sketch_img'].to(device))\n if not Train_Image_Feature:\n for key in list(sketch_feature.keys()):\n Train_Image_Feature[key] = []\n for key in list(sketch_feature.keys()):\n Train_Image_Feature[key].extend(sketch_feature[key].detach())\n Train_Image_Label.extend(sampled_batch['sketch_label'])\n if i_batch%50 == 0:\n print('Extracting Training Features:' + str(i_batch) + '/' + str(len(datloader_Train)))\n\n\n for i_batch, sampled_batch in enumerate(datloader_Test):\n sketch_feature = self.Network.extract_features(sampled_batch['sketch_img'].to(device))\n if not Test_Image_Feature:\n for key in list(sketch_feature.keys()):\n Test_Image_Feature[key] = []\n for key in list(sketch_feature.keys()):\n Test_Image_Feature[key].extend(sketch_feature[key].detach())\n Test_Image_Label.extend(sampled_batch['sketch_label'])\n if i_batch%50 == 0:\n print('Extracting Testing Features: ' + str(i_batch) + '/' + str(len(datloader_Test)))\n\n Train_Image_Label, Test_Image_Label = torch.tensor(Train_Image_Label).to(device), torch.tensor(Test_Image_Label).to(device)\n save_result = []\n\n for i_key, key in enumerate(list(Train_Image_Feature.keys())[::-1]):\n\n Train_Feature, Test_Feature = torch.stack(Train_Image_Feature[key]), torch.stack(Test_Image_Feature[key])\n model = nn.Linear(Train_Feature.shape[1], 250).to(device)\n optimizer = optim.Adam(model.parameters(), 0.0001)\n\n max_epoch_finetune = [50, 200, 300, 400]\n batch_finetune = 128\n step = 0\n best_accuracy = 0\n\n for epoch in range(max_epoch_finetune[i_key]):\n\n for idx in range(len(Train_Feature) // batch_finetune):\n step = step + 1\n optimizer.zero_grad()\n batch_train_feature = Train_Feature[idx * batch_finetune: (idx + 1) * batch_finetune]\n batch_train_label = Train_Image_Label[idx * batch_finetune: (idx + 1) * batch_finetune]\n output = model(batch_train_feature)\n loss = F.cross_entropy(output, batch_train_label)\n loss.backward()\n optimizer.step()\n\n if step%100 == 0:\n prediction = output.argmax(dim=1, keepdim=True)\n bacth_accuracy = prediction.eq(batch_train_label.view_as(prediction)).sum().item()\n print('@FineTuning: {}, Epoch: {}, Steps: {}, Iter: {}, Loss: {}, '\n 'Train Accuracy: {}, Max Test Accuracy: {}'.format(key, epoch, step, idx, loss,\n bacth_accuracy/batch_finetune*100, best_accuracy))\n\n if step%1000 == 0:\n output = model(Test_Feature)\n prediction = output.argmax(dim=1, keepdim=True)\n test_accuracy = prediction.eq(Test_Image_Label.view_as(prediction)).sum().item()/Test_Feature.shape[0] * 100\n if test_accuracy > best_accuracy:\n best_accuracy = test_accuracy\n\n print(\"Step: {}::::Layer Name: {} ---> Accuracy: {}\".format(self.step, key, best_accuracy))\n save_result.append((key, best_accuracy))\n\n with open('Results.txt', 'a') as filehandle:\n filehandle.write('Step: ' + str(self.step) + '\\n')\n np.savetxt(filehandle, np.array(save_result), fmt='%s', comments=str(self.step))\n\n print('Time to Evaluate:{} Minutes'.format((time.time() - start_time)/60.))\n\n self.unfreeze_weights()\n\n\n return save_result[0][-1]\n\n def evaluate(self, dataloader_Test):\n self.eval()\n correct = 0\n test_loss = 0\n start_time = time.time()\n for i_batch, batch in enumerate(dataloader_Test):\n output = self.Network(batch['sketch_img'].to(device))\n test_loss += self.loss(output, batch['sketch_label'].to(device)).item()\n prediction = output.argmax(dim=1, keepdim=True).to('cpu')\n correct += prediction.eq(batch['sketch_label'].view_as(prediction)).sum().item()\n\n test_loss /= len(dataloader_Test.dataset)\n accuracy = 100. * correct / len(dataloader_Test.dataset)\n\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%), Time_Takes: {}\\n'.format(\n test_loss, correct, len(dataloader_Test.dataset), accuracy, (time.time() - start_time) ))\n\n return accuracy\n\n def freeze_weights(self):\n for name, x in self.named_parameters():\n x.requires_grad = False\n\n def unfreeze_weights(self):\n for name, x in self.named_parameters():\n x.requires_grad = True\n\n\n\n","sub_path":"baselines/RotationNet/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648107961","text":"# *- code: utf-8 -*\n\nimport json\nimport os\nimport subprocess\nimport datetime\n'''\n# initiating for a complete blogs list\narticles = []\nfor file in os.listdir():\n if file.endswith(\".md\"):\n with open(file, encoding='utf-8') as post:\n post = post.readlines()\n title = \"\"\n date = \"\"\n for line in post[:5]:\n if line.startswith('title:'):\n title = line.split(\":\")[1].strip()\n if line.startswith('date'):\n date = line.split(\":\")[1].strip().split()[0].strip()\n articles.append({\"title\":title, \"date\": date,\"content\":\"\".join(post[5:])}) \nct = json.dumps({\"post\":articles})\nwith open('db.json','w') as df:\n df.write(ct)\n \n'''\ndbs = None\ndate = None\nwith open('db.json') as blogs:\n try:\n dbs = json.loads(blogs.read())\n except:\n dbs = json.loads('{\"post\":[{\"title\":\"\",\"date\":\"\"}]}')\n\nfor file in os.listdir():\n if file.endswith(\".md\"):\n new_post = True\n with open(file,encoding='utf-8') as post:\n post = post.readlines()\n title = None\n date = None\n for line in post[:5]:\n if line.startswith('title:'):\n title = line.split(\":\")[1].strip()\n if line.startswith('date'):\n date = line.split(\":\")[1].strip().split()[0].strip()\n if not title or not date:\n continue\n for blog in dbs['post']:\n if blog['title'] == title and blog['date'] == date:\n new_post = False \n break\n if new_post:\n new_article = {\"title\":title,\"date\":date,\"content\":\"\".join(post[5:])}\n old_post = dbs['post']\n old_post.append(new_article)\n new_dbs = json.dumps({'post':old_post})\n with open('db.json','w') as post_list:\n post_list.write(new_dbs)\n \n dt = datetime.datetime.strptime(date,\"%Y-%m-%d\")\n year = str(dt.year)\n month = str(\"{:0>2d}\".format(dt.month))\n day = str(\"{:0>2d}\".format(dt.day))\n blog_url = os.path.join(os.path.pardir,'content',year,month,day,title)\n \n if not os.path.exists(blog_url):\n os.makedirs(blog_url)\n \n blog_url = blog_url.replace(os.sep,\"/\")\n cmd = 'pandoc ' + file + \" -s -o \\\"\" + blog_url + \"/index.html\\\" --template=default.html\"\n print(cmd)\n subprocess.call(cmd)\n pos = None\n ct = None\n with open('../index.html',encoding='utf-8') as page:\n ct = page.read()\n ltag = \"

2020

\"\n pos = ct.find(ltag)\n \n new_content = ct[:pos+len(ltag)] + \"\\n\" + title + \"\\n\" + dt.strftime(\"%Y-%m-%d\") + \"\" + ct[pos+len(ltag):]\n with open('../index.html','w',encoding='utf-8') as page:\n page.write(new_content)\n \nos.chdir('../')\nsubprocess.call(\"git add .\")\nsubprocess.call(\"git commit -m \\\"add new record \" + datetime.datetime.today().strftime(\"%Y-%m-%d\") + \"\\\"\")\nsubprocess.call(\"git push blog master\")\n","sub_path":"sources/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"542908366","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport aiohttp\nimport asyncio\nimport jmespath\nimport json\nimport logging\nimport requests\nimport signal\n\nfrom .providers import providers\n\nlogging.basicConfig(level=logging.WARNING)\nlogging.getLogger(\"requests\").setLevel(logging.WARNING)\n\nclass ActivityCount(object):\n \"\"\"Gather activity/share stats from social APIs\"\"\"\n def __init__(self, url=None):\n self.url = url or None\n self.responses = []\n\n def establish_client(self, loop):\n self.loop = asyncio.new_event_loop()\n self.client = aiohttp.ClientSession(loop=self.loop)\n asyncio.set_event_loop(self.loop)\n\n async def async_get_all(self, loop):\n self.establish_client(loop)\n \n for provider in providers:\n url = provider[\"endpoint\"].format(self.url)\n asyncio.ensure_future(self.collect_sharecount(url, provider), loop=self.loop)\n\n # loop over all providers\n pending = asyncio.Task.all_tasks(loop=self.loop)\n self.loop.run_until_complete(asyncio.gather(*pending, loop=self.loop))\n self.client.close()\n self.loop.close()\n\n async def get_json(self, url):\n async with self.client.get(url) as response:\n assert response.status == 200\n logging.debug(\"Got response for URL {0} with statuscode {1}\".format(url, response.status))\n response = await response.read()\n return response.decode('utf-8')\n\n async def collect_sharecount(self, url, provider):\n response = await self.get_json(url)\n j = json.loads(response)\n\n data = {\n \"provider\": provider[\"provider\"],\n \"metrics\": []\n }\n\n for m in provider[\"metrics\"]:\n data[\"metrics\"].append({\n \"count\": jmespath.search(m[\"path\"], j),\n \"label\": m[\"label\"]\n })\n \n self.responses.append(data)\n","sub_path":"metadoc/social/activity.py","file_name":"activity.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57354151","text":"# -*- coding: utf-8 -*-\n\n'''\n Based on Covenant's search\n Author Bugatsinho\n\n License summary below, for more details please read license.txt file\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 2 of the License, or\n (at your option) any later version.\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 General Public License for more details.\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n'''\nimport xbmc\nimport urllib\nimport json\nimport random\nimport urlparse\nimport sys\nimport re\nfrom resources.lib.modules import control\nfrom resources.lib.modules import cache\nfrom resources.lib.modules import client\nfrom resources.lib.modules import view\nfrom t0mm0.common.addon import Addon\n\naddon = Addon('plugin.video.releaseBB', sys.argv)\nLang = control.lang\nADDON = control.addon()\nFANART = ADDON.getAddonInfo('fanart')\nICON = ADDON.getAddonInfo('icon')\nNAME = ADDON.getAddonInfo('name')\nversion = ADDON.getAddonInfo('version')\nIconPath = control.addonPath + \"/resources/icons/\"\nbase = control.setting('domain')\nBASE_URL = 'http://%s' % base.lower()\n\ntry:\n from sqlite3 import dbapi2 as database\nexcept ImportError:\n from pysqlite2 import dbapi2 as database\n\n\ndef Search_bb(url):\n kodi_ver = float(xbmc.getInfoLabel(\"System.BuildVersion\")[:4])\n if kodi_ver >= 18:\n from resources.lib.modules import cfscrape as cfscrape\n else:\n from resources.lib.modules import cfscrape17 as cfscrape\n scraper = cfscrape.create_scraper()\n if 'new' == url:\n keyboard = xbmc.Keyboard()\n keyboard.setHeading(control.lang(32002).encode('utf-8'))\n keyboard.doModal()\n if keyboard.isConfirmed():\n _query = keyboard.getText()\n query = _query.encode('utf-8')\n try:\n query = urllib.quote_plus(query)\n referer_link = 'http://search.rlsbb.ru?s={0}'.format(query)\n\n url = 'http://search.rlsbb.ru/Home/GetPost?phrase={0}&pindex=1&content=true&type=Simple&rad=0.{1}'\n url = url.format(query, random.randint(0o000000000000001, 99999999999999999))\n #########save in Database#########\n term = urllib.unquote_plus(query).decode('utf-8')\n dbcon = database.connect(control.searchFile)\n dbcur = dbcon.cursor()\n dbcur.execute(\"DELETE FROM Search WHERE search = ?\", (term,))\n dbcur.execute(\"INSERT INTO Search VALUES (?,?)\", (url, term))\n dbcon.commit()\n dbcur.close()\n\n #########search in website#########\n headers = {'Referer': referer_link,\n 'X-Requested-With': 'XMLHttpRequest'}\n first = scraper.get(referer_link, headers=headers).text\n xbmc.sleep(10)\n html = scraper.get(url, headers=headers).text\n posts = json.loads(html)['results']\n posts = [(i['post_name'], i['post_title'], i['post_content'], i['domain']) for i in posts if i]\n for movieUrl, title, infos, domain in posts:\n base = BASE_URL if 'old' not in domain else 'http://old2.rlsbb.ru/'\n movieUrl = urlparse.urljoin(base, movieUrl) if not movieUrl.startswith('http') else movieUrl\n title = title.encode('utf-8')\n infos = infos.replace('\\\\', '')\n try:\n img = client.parseDOM(infos, 'img', ret='src')[0]\n img = img.replace('.ru', '.to')\n except:\n img = ICON\n\n try:\n fan = client.parseDOM(infos, 'img', ret='src')[1]\n except:\n fan = FANART\n\n try:\n desc = re.search(r'Plot:(.+?)

', OPEN, re.DOTALL)[0]\n else:\n Sinopsis = re.findall('

\\n

(.+?)

', OPEN, re.DOTALL)[0]\n\n except:\n Sinopsis = re.findall('

\\n

(.+?)

\\n

', '', Sinopsis)\n part = re.sub('\\.\\s+', '.', part)\n desc = clear_Title(part)\n desc = desc.decode('ascii', errors='ignore')\n return desc\n except BaseException:\n return 'N/A'\n\ndef clear_Title(txt):\n txt = re.sub('<.+?>', '', txt)\n txt = txt.replace(\""\", \"\\\"\").replace('()', '').replace(\"&\", \"&\").replace('–', ':')\n txt = txt.replace(\"&\", \"&\").replace('’', \"'\").replace(''', ':').replace('&#;', '\\'')\n txt = txt.replace(\"&\", \"&\").replace('”', '\"').replace('‘', '\"').replace(' ', '')\n txt = txt.replace(\" \", \"\").replace('“', '\"').replace('\\t', ' ').replace('\\n', ' ')\n return txt","sub_path":"plugin.video.releaseBB/resources/lib/modules/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":13822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"491075735","text":"import tornado\nfrom tornado.httpclient import AsyncHTTPClient\nfrom tornado import gen\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.options\nimport tornado.web\nfrom tornado.options import define, options\nimport sys\n\ndefine(\"port\", default=8888, help=\"run on the given port\", type=int)\n\n\ndef dot():\n \"\"\"callback for showing that IOLoop is still responsive while we wait\"\"\"\n sys.stdout.write('.')\n sys.stdout.flush()\n\nclass MainHandler(tornado.web.RequestHandler):\n @tornado.gen.coroutine\n def get(self):\n futures = []\n print('begginging')\n http_client = AsyncHTTPClient()\n for i in range(1,60):\n try:\n #response = yield http_client.fetch('http://www.google.com')\n request = tornado.httpclient.HTTPRequest(url='http://www.google.com',\n connect_timeout=60.0,\n request_timeout=60.0)\n response = yield tornado.gen.Task(http_client.fetch, request)\n \n #print(response.body)\n except Exception as e:\n # Other errors are possible, such as IOError.\n print(\"Error: \" + str(e))\n except tornado.gen.BadYieldError as e:\n print(\"error: \" + str(e))\n ##print(response)\n http_client.close()\n print(\"response is {} long\".format(len(response.body)))\n self.write(\"ok\")\n #print(response)\n\n\ndef main():\n tornado.options.parse_command_line()\n application = tornado.web.Application([\n (r\"/\", MainHandler),\n ])\n http_server = tornado.httpserver.HTTPServer(application)\n http_server.listen(options.port)\n beat = tornado.ioloop.PeriodicCallback(dot, 100)\n beat.start()\n tornado.ioloop.IOLoop.current().start()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"async_server/async.py","file_name":"async.py","file_ext":"py","file_size_in_byte":1885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"20546088","text":"##### DUELS #####\n\n# Amount of time before a duel request expires\nduelReqExpiryTime = {\"days\":1}\n# duelReqExpiryTime as a user-friendly string for printing\nduelReqExpiryTimeStr = \"1 day\"\n# The amount to vary ship stats (+-) by before executing a duel\nduelVariancePercent = 0.05\n\n# Max number of entries that can be printed for a duel log\nduelLogMaxLength = 10\n\n# Percentage probability of a user envoking a cloak module in a given timeStep, should they have one equipped\nduelCloakChance = 20\n\n\n\n##### SHOPS #####\n\n# Amount of time to wait between refreshing stock of all shops\nshopRefreshStockPeriod = {\"days\":0, \"hours\":12, \"minutes\":0, \"seconds\":0}\n\n# The number of ranks to use when randomly picking shop stock\nnumShipRanks = 10\nnumWeaponRanks = 10\nnumModuleRanks = 7\nnumTurretRanks = 3\n\n# The default number of items shops should generate every shopRefreshStockPeriod\nshopDefaultShipsNum = 5\nshopDefaultWeaponsNum = 5\nshopDefaultModulesNum = 5\nshopDefaultTurretsNum = 2\n\n# bbTurret is the only item that has a probability not to be spawned. This metric indicates the percentage chance of turrets being stocked on a given day\nturretSpawnProbability = 45\n\n\n\n##### BOUNTIES #####\n\nmaxBountiesPerFaction = 5\n\n# can be \"fixed\" or \"random\"\nnewBountyDelayType = \"random\"\n\n# only spawn bounties at this time\nnewBountyFixedDailyTime = {\"hours\":18, \"minutes\":40, \"seconds\":0}\n# use the above, or just spawn after every newBountyFixedDelta\nnewBountyFixedUseDailyTime = False\n\n# time to wait inbetween spawning bounties\nnewBountyFixedDelta = {\"days\":0, \"hours\":0, \"minutes\":40, \"seconds\":0}\n\n# when using random delay generation, use this as the minimum wait time in seconds\nnewBountyDelayMin = 15 * 60\n# when using random delay generation, use this as the maximum wait time in seconds\nnewBountyDelayMax = 1 * 60 * 60\n\n# The number of credits to award for each bPoint (each system in a criminal route)\nbPointsToCreditsRatio = 1000\n\n# time to put users on cooldown between using !bb check\ncheckCooldown = {\"minutes\":3}\n\n# number of bounties ahead of a checked system in a route to report a recent criminal spotting (+1)\ncloseBountyThreshold = 4\n\n\n\n##### SAVING #####\n\n# The time to wait inbetween database autosaves.\nsavePeriod = {\"hours\":1}\n\n# path to JSON files for database saves\nuserDBPath = \"saveData/users.json\"\nguildDBPath = \"saveData/guilds.json\"\nbountyDBPath = \"saveData/bounties.json\"\n\n\n\n##### SCHEDULING #####\n\n# Whether to execute timedtask checks every timedTaskLatenessThresholdSeconds (\"fixed\"), or to calculate the delay to wait until the next TimedTask is schedule to expire (\"dynamic\")\ntimedTaskCheckingType = \"fixed\"\n\n# How late a timed task acceptably be\n# I.e a scheduled task may expire up to timedTaskLatenessThresholdSeconds seconds after their intended expiration time.\n# replaces the depracated 'delayFactor' variable\ntimedTaskLatenessThresholdSeconds = 10\n\n\n\n##### MISC #####\n\n# prefix for bot commands. dont forget a space if you want one!\ncommandPrefix = \"$\"\n\n# When a user message prompts a DM to be sent, this emoji will be added to the message reactions.\ndmSentEmoji = \"📬\"\n\n# max number of characters accepted by nameShip\nmaxShipNickLength = 30\n\n# The default emojis to list in a reaction menu\ndefaultMenuEmojis = [\"0️⃣\", \"1️⃣\", \"2️⃣\", \"3️⃣\", \"4️⃣\", \"5️⃣\", \"6️⃣\", \"7️⃣\", \"8️⃣\", \"9️⃣\", \"🔟\"]\n\n\n\n##### ADMINISTRATION #####\n\n# discord user IDs of all developers\ndevelopers = [188618589102669826, 448491245296418817]\n\n# titles to give each type of user when reporting error messages etc\ndevTitle = \"officer\"\nadminTitle = \"commander\"\nuserTitle = \"pilot\"\n\n# Servers where bountyBot commands are disabled. Currently this is just the emoji servers:\ndisabledServers = [723704980246233219, 723702782640783361, 723708988830515231, 723704665560055848, 723705817764986900, 723703454635393056, 723708655031156742, 723706906517962814, 723704087962583131, 723704350131748935]\n\n\n\n##### HANGARS #####\n\n# The maximum number of items that will be displayed per page of a user's hangar, when all item types are requested\nmaxItemsPerHangarPageAll = 3\n# The maximum number of items that will be displayed per page of a user's hangar, when a single item type is requested\nmaxItemsPerHangarPageIndividual = 5\n\n# Names to be used when checking input to !bb hangar and bbUser.numInventoryPages\nvalidItemNames = [\"ship\", \"weapon\", \"module\", \"turret\", \"all\"]\n\n\n\n##### INTERNAL #####\n# Do not touch these!\nnewBountyDelayReset = False\nnewBountyFixedDeltaChanged = False","sub_path":"BB/bbConfig/bbConfig.py","file_name":"bbConfig.py","file_ext":"py","file_size_in_byte":4506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"297631859","text":"\"\"\"\nBootstrap's user's development environment by creating cloud resources required by SAM CLI\n\"\"\"\n\nimport logging\n\nimport boto3\n\nimport click\n\nfrom botocore.config import Config\nfrom botocore.exceptions import ClientError, BotoCoreError, NoRegionError, NoCredentialsError, ProfileNotFound\n\nfrom samcli.commands.exceptions import UserException, CredentialsError, RegionError\n\n\nSAM_CLI_STACK_PREFIX = \"aws-sam-cli-managed-\"\nLOG = logging.getLogger(__name__)\n\n\nclass ManagedStackError(UserException):\n def __init__(self, ex):\n self.ex = ex\n message_fmt = f\"Failed to create managed resources: {ex}\"\n super().__init__(message=message_fmt.format(ex=self.ex))\n\n\ndef manage_stack(profile, region, stack_name, template_body):\n try:\n if profile:\n session = boto3.Session(profile_name=profile, region_name=region if region else None)\n cloudformation_client = session.client(\"cloudformation\")\n else:\n cloudformation_client = boto3.client(\n \"cloudformation\", config=Config(region_name=region if region else None)\n )\n except ProfileNotFound as ex:\n raise CredentialsError(\n f\"Error Setting Up Managed Stack Client: the provided AWS name profile '{profile}' is not found. \"\n \"please check the documentation for setting up a named profile: \"\n \"https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html\"\n ) from ex\n except NoCredentialsError as ex:\n raise CredentialsError(\n \"Error Setting Up Managed Stack Client: Unable to resolve credentials for the AWS SDK for Python client. \"\n \"Please see their documentation for options to pass in credentials: \"\n \"https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html\"\n ) from ex\n except NoRegionError as ex:\n raise RegionError(\n \"Error Setting Up Managed Stack Client: Unable to resolve a region. \"\n \"Please provide a region via the --region parameter or by the AWS_REGION environment variable.\"\n ) from ex\n return _create_or_get_stack(cloudformation_client, stack_name, template_body)\n\n\ndef _create_or_get_stack(cloudformation_client, stack_name, template_body):\n try:\n ds_resp = cloudformation_client.describe_stacks(StackName=stack_name)\n stacks = ds_resp[\"Stacks\"]\n stack = stacks[0]\n click.echo(\"\\n\\tLooking for resources needed for deployment: Found!\")\n _check_sanity_of_stack(stack, stack_name)\n return stack[\"Outputs\"]\n except ClientError:\n click.echo(\"\\n\\tLooking for resources needed for deployment: Not found.\")\n\n try:\n stack = _create_stack(\n cloudformation_client, stack_name, template_body\n ) # exceptions are not captured from subcommands\n _check_sanity_of_stack(stack, stack_name)\n return stack[\"Outputs\"]\n except (ClientError, BotoCoreError) as ex:\n LOG.debug(\"Failed to create managed resources\", exc_info=ex)\n raise ManagedStackError(str(ex)) from ex\n\n\ndef _check_sanity_of_stack(stack, stack_name):\n tags = stack.get(\"Tags\", None)\n outputs = stack.get(\"Outputs\", None)\n\n # For some edge cases, stack could be in invalid state\n # Check if stack information contains the Tags and Outputs as we expected\n if tags is None or outputs is None:\n stack_state = stack.get(\"StackStatus\", None)\n msg = (\n f\"Stack {stack_name} is missing Tags and/or Outputs information and therefore not in a \"\n f\"healthy state (Current state:{stack_state}). Failing as the stack was likely not created \"\n f\"by the AWS SAM CLI\"\n )\n raise UserException(msg)\n\n # Sanity check for non-none stack? Sanity check for tag?\n try:\n sam_cli_tag = next(t for t in tags if t[\"Key\"] == \"ManagedStackSource\")\n if not sam_cli_tag[\"Value\"] == \"AwsSamCli\":\n msg = (\n \"Stack \"\n + stack_name\n + \" ManagedStackSource tag shows \"\n + sam_cli_tag[\"Value\"]\n + \" which does not match the AWS SAM CLI generated tag value of AwsSamCli. \"\n \"Failing as the stack was likely not created by the AWS SAM CLI.\"\n )\n raise UserException(msg)\n except StopIteration as ex:\n msg = (\n \"Stack \" + stack_name + \" exists, but the ManagedStackSource tag is missing. \"\n \"Failing as the stack was likely not created by the AWS SAM CLI.\"\n )\n raise UserException(msg) from ex\n\n\ndef _create_stack(cloudformation_client, stack_name, template_body):\n click.echo(\"\\tCreating the required resources...\")\n change_set_name = \"InitialCreation\"\n change_set_resp = cloudformation_client.create_change_set(\n StackName=stack_name,\n TemplateBody=template_body,\n Tags=[{\"Key\": \"ManagedStackSource\", \"Value\": \"AwsSamCli\"}],\n ChangeSetType=\"CREATE\",\n ChangeSetName=change_set_name, # this must be unique for the stack, but we only create so that's fine\n )\n stack_id = change_set_resp[\"StackId\"]\n change_waiter = cloudformation_client.get_waiter(\"change_set_create_complete\")\n change_waiter.wait(\n ChangeSetName=change_set_name, StackName=stack_name, WaiterConfig={\"Delay\": 15, \"MaxAttempts\": 60}\n )\n cloudformation_client.execute_change_set(ChangeSetName=change_set_name, StackName=stack_name)\n stack_waiter = cloudformation_client.get_waiter(\"stack_create_complete\")\n stack_waiter.wait(StackName=stack_id, WaiterConfig={\"Delay\": 15, \"MaxAttempts\": 60})\n ds_resp = cloudformation_client.describe_stacks(StackName=stack_name)\n stacks = ds_resp[\"Stacks\"]\n click.echo(\"\\tSuccessfully created!\")\n return stacks[0]\n","sub_path":"samcli/lib/utils/managed_cloudformation_stack.py","file_name":"managed_cloudformation_stack.py","file_ext":"py","file_size_in_byte":5823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"332233155","text":"f1=open(\"input2\",'r')\r\nf2=open(\"output2.txt\",'w')\r\nt=int(f1.readline()[:-1])\r\nfor k in range(1,t+1):\r\n r= str(f1.readline()[:-1])\r\n s,m=r.split()\r\n s=list(s)\r\n m=int(m)\r\n i = 0\r\n flag=0\r\n imp = \"IMPOSSIBLE\"\r\n moves = 0\r\n while i <= len(s) - m:\r\n if s[i] == '-':\r\n moves += 1\r\n for j in range(i, i + m):\r\n if s[j] == '+':\r\n s[j] = '-'\r\n else:\r\n s[j] = '+'\r\n i = i + 1\r\n for i in range(0, len(s)):\r\n if s[i] == '-':\r\n flag = 1\r\n break\r\n if flag == 0:\r\n f2.write(\"Case #{}: {}\".format(k, moves) + '\\n')\r\n else:\r\n f2.write(\"Case #{}: {}\".format(k, imp) + '\\n')\r\n","sub_path":"solutions_python/Problem_199/2773.py","file_name":"2773.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"138491432","text":"\"\"\"\nMain run file.\n\"\"\"\n\nimport pandas as pd\nimport argparse\nfrom settings import conf\nfrom load_data import load_action_units_x, load_facial_attributes_x,\\\n load_body_keypoints_x\nfrom utils import load_model, evaluate_model, \\\n average_ensemble, wtd_average_ensemble\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Process OpenPose output.')\n\n parser.add_argument('--model_path', required=True,\n help='Path of folder containing models.')\n\n args = parser.parse_args()\n model_path = args.model_path\n\n val_data = pd.read_csv(conf.VAL_DATA, delimiter=',')\n val_engagement_value = val_data.attention\n\n print(\"Validating models\")\n\n val_x_openface_au = load_action_units_x(val_data, \"val\")\n print(\"Loaded validation FAU features\")\n val_x_openface_face = load_facial_attributes_x(val_data, \"val\")\n print(\"Loaded validation FA features\")\n val_x_openpose = load_body_keypoints_x(val_data, \"val\")\n print(\"Loaded validation BL features\")\n\n au_model = load_model(conf.MODEL_AU_NAME, model_path)\n pred_au = au_model.predict(val_x_openface_au)\n mse_au, pcc_au = evaluate_model(pred_au, val_engagement_value)\n print(\"FAU: MSE = {}, PCC = {}\".format(mse_au, pcc_au))\n\n face_model = load_model(conf.MODEL_FACE_NAME, model_path)\n pred_face = face_model.predict(val_x_openface_face)\n mse_face, pcc_face = evaluate_model(pred_face, val_engagement_value)\n print(\"FA: MSE = {}, PCC = {}\".format(mse_face, pcc_face))\n\n bodylandmark_model = load_model(conf.MODEL_BL_NAME, model_path)\n pred_bl = bodylandmark_model.predict(val_x_openpose)\n mse_bl, pcc_bl = evaluate_model(pred_bl, val_engagement_value)\n print(\"BL: MSE = {}, PCC = {}\".format(mse_bl, pcc_bl))\n\n average_pred = average_ensemble(pred_au, pred_face, pred_bl)\n mse_avg, pcc_avg = evaluate_model(average_pred, val_engagement_value)\n print(\"Avg Ensemble: MSE = {}, PCC = {}\".format(mse_avg, pcc_avg))\n\n wtd_average_pred = wtd_average_ensemble(pred_au, pred_face, pred_bl)\n mse_wtd, pcc_wtd = evaluate_model(\n wtd_average_pred, val_engagement_value)\n print(\"Wtd Avg Ensemble: MSE = {}, PCC = {}\".format(mse_wtd, pcc_wtd))\n","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"599093662","text":"from app.models import db, Comment\nfrom faker import Faker\nimport random\n\nfaker = Faker()\n\ndef seed_comments():\n \n for i in range(50):\n comments = Comment(\n content = faker.sentence()\n )\n db.session.add(comments)\n db.session.commit()\n \ndef undo_comments():\n db.session.execute('TRUNCATE comments RESTART IDENTITY CASCADE;')\n db.session.commit()\n","sub_path":"app/seeds/comments.py","file_name":"comments.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"54945386","text":"# -*- coding: utf-8 -*-\n#\n# Convex Search Algorithm\n#\nimport random\nimport statistics as stats\n\n###########################\n### AUXILIARY FUNCTIONS ###\n###########################\n\ndef all_equal(bunch):\n first = bunch[0]\n return all(x == first for x in bunch)\n\ndef any_greater_than(bunch, threshold):\n return any(x >= threshold for x in bunch)\n\n\n### Using any_two_greater_than instead of any_greater_than may be preferable\n### because it avoids constructing a mating pool of individuals above average\n### containing only a single individidual. Doing recombination only on one\n### individual can only lead to the same individual, and this might accelerate\n### premature convergence. Whereas if we have a mating pool consisting of at\n### least two different individuals, the offspring are more likely to be\n### different than parents.\ndef any_two_greater_than(bunch, threshold):\n return len(set([x for x in bunch if x >= threshold])) > 2\n\n##################################\n### PROBLEMS' FITNESS FUNCTION ###\n##################################\n\n### Leading-Ones\n\ndef leadingones_fitness(individual):\n for position in range(INDIVIDUAL_SIZE):\n if individual[position] == 0:\n break\n else:\n position += 1\n return position\ndef leadingones_avg_fitness():\n ### avg(n) is the sum of fitnesses of all sequences with leading ones,\n ### i.e. (2^n) - 1, divided by the number of sequences with leading ones,\n ### i.e. 2^(n - 1):\n ###\n ### avg(n) = (2^n) - 1 / 2^(n - 1)\n ### = (2^n / 2^n-1) - (1 / 2^n-1)\n ### = 2 - 2^(1 - n) .\n ###\n ### In the limit: lim n->+inf (2 - 2^(1 - n)) = 2 - 0 = 2 .\n return 2 - (2**(1 - INDIVIDUAL_SIZE))\n\n### One-Max\n\ndef onemax_fitness(individual):\n return sum(individual)\n\ndef onemax_avg_fitness():\n return sum([0.5 for _ in range(INDIVIDUAL_SIZE)])\n\n######################\n### REPRESENTATION ###\n######################\n\ndef create_ind():\n return [random.randint(0, 1) for _ in range(INDIVIDUAL_SIZE)]\n\ndef create_pop():\n return [create_ind() for _ in range(POPULATION_SIZE)]\n\ndef evaluate_pop(population):\n return [PROBLEM_FITNESS(individual) for individual in population]\n\n#################################\n### SELECTION META-HEURISTICS ###\n#################################\n\ndef select_better_than_worst(population, fitness_population):\n worst_fitness = min(fitness_population)\n return [individual\n for (individual, fitness) in zip(population, fitness_population)\n if fitness > worst_fitness]\n\ndef select_above_avg(population, fitness_population):\n avg_fitness = stats.mean(fitness_population)\n return [individual\n for (individual, fitness) in zip(population, fitness_population)\n if fitness >= avg_fitness]\n\n#####################\n### RECOMBINATION ###\n#####################\n\ndef convex_recombination_pop(mating_pool):\n return [convex_recombination_ind(mating_pool)\n for _ in range(POPULATION_SIZE)]\n\ndef convex_recombination_ind(mating_pool):\n transposed_mating_pool = zip(*mating_pool)\n def recombine_column(col):\n return col[0] if all_equal(col) else random.randint(0, 1)\n return list(map(recombine_column, transposed_mating_pool))\n\n#####################\n### CONVEX SEARCH ###\n#####################\n\ndef convex_search():\n gens = 0\n population = create_pop()\n fitness_population = evaluate_pop(population)\n while (not all_equal(population)) and (gens < MAX_GENERATIONS):\n mating_pool = population\n if not all_equal(fitness_population):\n mating_pool = select_better_than_worst(\n population,\n fitness_population\n )\n population = convex_recombination_pop(mating_pool)\n fitness_population = evaluate_pop(population)\n gens += 1\n return (fitness_population[0], gens)\n\ndef convex_search2():\n gens = 0\n population = create_pop()\n fitness_population = evaluate_pop(population)\n while (not all_equal(population)) and (gens < MAX_GENERATIONS):\n mating_pool = population\n avg_fitness_pop = stats.mean(fitness_population)\n if any_two_greater_than(fitness_population, avg_fitness_pop):\n mating_pool = select_above_avg(population, fitness_population)\n elif not all_equal(fitness_population):\n mating_pool = select_better_than_worst(\n population,\n fitness_population\n )\n population = convex_recombination_pop(mating_pool)\n fitness_population = evaluate_pop(population)\n gens += 1\n return (fitness_population[0], gens)\n\n\n\n\n############\n### MAIN ###\n############\n\n### (Corollary 9) Recommended population sizes for a given individual size,\n### so that \"normal\" convex search optimises LeadingOnes in O(n log n).\n### - population size: 25, 40, 60, 75\n### - individual size: 10, 100, 1000, 10000\n\nSEARCH = convex_search2\nPROBLEM_FITNESS = leadingones_fitness\nMAX_RUNS = 300\nMAX_GENERATIONS = 100\nPOPULATION_SIZE = 50\nINDIVIDUAL_SIZE = 100\n\ndef main():\n ### Settings\n print(\"-------------------- SETTINGS --------------------\")\n print(\"Search algorithm: %s\" % SEARCH.__name__)\n print(\"Problem fitness function: %s\" % PROBLEM_FITNESS.__name__)\n print(\"Max runs: %d, Max gens.: %d, Pop. size: %d, Ind. size: %d\" %\n (MAX_RUNS, MAX_GENERATIONS, POPULATION_SIZE, INDIVIDUAL_SIZE)\n )\n ### Start\n print(\"-------------------- START --------------------\")\n runs = 0\n fitnesses = []\n generations = []\n while (runs < MAX_RUNS):\n (fit, gens) = SEARCH()\n fitnesses.append(fit)\n generations.append(gens)\n runs += 1\n print(\"Runs: %d | fitness: %d, gens.: %d\" % (runs, fit, gens))\n ### Results\n print(\"-------------------- SUMMARY --------------------\")\n print(\"Fitness | Max: %d, Min: %d, Avg: %f, Median high: %f, Stdev: %f\"\n % (max(fitnesses),\n min(fitnesses),\n stats.mean(fitnesses),\n stats.median_high(fitnesses),\n stats.stdev(fitnesses))\n )\n print(\"Generations | Max: %d, Min: %d, Avg: %f, Median low: %f, Stdev: %f\"\n % (max(generations),\n min(generations),\n stats.mean(generations),\n stats.median_low(generations),\n stats.stdev(generations))\n )\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"ces.py","file_name":"ces.py","file_ext":"py","file_size_in_byte":6423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"300769585","text":"from rest_framework_mongoengine.serializers import DocumentSerializer\nfrom eledata.models.watcher import *\n\n\nclass GeneralWatcherSerializer(DocumentSerializer):\n class Meta:\n model = Watcher\n depth = 2\n fields = [\n 'sku_id', 'product_name', 'item_url', 'default_price', 'final_price', 'seller_name', 'seller_url', 'images',\n 'platform', 'current_stock', 'comments_count', 'bundle', 'detail', 'support', 'model', 'seller_location',\n 'sales_count', 'search_keyword', 'search_rank', 'search_order', 'last_crawling_timestamp', ]\n","sub_path":"eledata/serializers/watcher.py","file_name":"watcher.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"267657956","text":"\"\"\"\n@Author: jinzhuan\n@File: baidu.py\n@Desc: \n\"\"\"\nfrom ..processor import Processor\nfrom cogie.core import DataTable\n\n\nclass BaiduRelationProcessor(Processor):\n\n def __init__(self, label_list=None, path=None, padding=None, unknown='',\n bert_model='hfl/chinese-roberta-wwm-ext', max_length=256, blank_padding=True, mask_entity=False):\n super().__init__(label_list, path, padding, unknown, bert_model, max_length)\n self.max_length = max_length\n self.blank_padding = blank_padding\n self.mask_entity = mask_entity\n\n def process(self, dataset):\n datable = DataTable()\n for i in range(len(dataset)):\n token, relation, subj_start, subj_end, obj_start, obj_end = dataset[i]\n label_id = self.vocabulary.to_index(relation)\n item = {'token': token, 'h': {'pos': [subj_start, subj_end + 1]}, 't': {'pos': [obj_start, obj_end + 1]}}\n indexed_tokens, att_mask, pos1, pos2 = self.tokenize(item)\n datable('input_ids', indexed_tokens)\n datable('attention_mask', att_mask)\n datable('pos1', pos1)\n datable('pos2', pos2)\n datable('label_id', label_id)\n\n datable('input_ids', indexed_tokens)\n datable('attention_mask', att_mask)\n datable('pos1', pos2)\n datable('pos2', pos1)\n datable('label_id', self.vocabulary.to_index(''))\n return datable\n\n def tokenize(self, item):\n # Sentence -> token\n if 'text' in item:\n sentence = item['text']\n is_token = False\n else:\n sentence = item['token']\n is_token = True\n pos_head = item['h']['pos']\n pos_tail = item['t']['pos']\n\n pos_min = pos_head\n pos_max = pos_tail\n if pos_head[0] > pos_tail[0]:\n pos_min = pos_tail\n pos_max = pos_head\n rev = True\n else:\n rev = False\n\n if not is_token:\n sent0 = self.tokenizer.tokenize(sentence[:pos_min[0]])\n ent0 = self.tokenizer.tokenize(sentence[pos_min[0]:pos_min[1]])\n sent1 = self.tokenizer.tokenize(sentence[pos_min[1]:pos_max[0]])\n ent1 = self.tokenizer.tokenize(sentence[pos_max[0]:pos_max[1]])\n sent2 = self.tokenizer.tokenize(sentence[pos_max[1]:])\n else:\n sent0 = self.tokenizer.tokenize(' '.join(sentence[:pos_min[0]]))\n ent0 = self.tokenizer.tokenize(' '.join(sentence[pos_min[0]:pos_min[1]]))\n sent1 = self.tokenizer.tokenize(' '.join(sentence[pos_min[1]:pos_max[0]]))\n ent1 = self.tokenizer.tokenize(' '.join(sentence[pos_max[0]:pos_max[1]]))\n sent2 = self.tokenizer.tokenize(' '.join(sentence[pos_max[1]:]))\n\n if self.mask_entity:\n ent0 = ['[unused4]'] if not rev else ['[unused5]']\n ent1 = ['[unused5]'] if not rev else ['[unused4]']\n else:\n ent0 = ['[unused0]'] + ent0 + ['[unused1]'] if not rev else ['[unused2]'] + ent0 + ['[unused3]']\n ent1 = ['[unused2]'] + ent1 + ['[unused3]'] if not rev else ['[unused0]'] + ent1 + ['[unused1]']\n\n re_tokens = ['[CLS]'] + sent0 + ent0 + sent1 + ent1 + sent2 + ['[SEP]']\n pos1 = 1 + len(sent0) if not rev else 1 + len(sent0 + ent0 + sent1)\n pos2 = 1 + len(sent0 + ent0 + sent1) if not rev else 1 + len(sent0)\n pos1 = min(self.max_length - 1, pos1)\n pos2 = min(self.max_length - 1, pos2)\n\n indexed_tokens = self.tokenizer.convert_tokens_to_ids(re_tokens)\n avai_len = len(indexed_tokens)\n\n # Position\n # pos1 = torch.tensor([[pos1]]).long()\n # pos2 = torch.tensor([[pos2]]).long()\n\n # Padding\n if self.blank_padding:\n while len(indexed_tokens) < self.max_length:\n indexed_tokens.append(0) # 0 is id for [PAD]\n indexed_tokens = indexed_tokens[:self.max_length]\n # indexed_tokens = torch.tensor(indexed_tokens).long().unsqueeze(0) # (1, L)\n\n # Attention mask\n # att_mask = torch.zeros(indexed_tokens.size()).long() # (1, L)\n # att_mask[0, :avai_len] = 1\n att_mask = [0] * len(indexed_tokens)\n for i in range(min(avai_len, self.max_length)):\n att_mask[i] = 1\n\n return indexed_tokens, att_mask, [pos1], [pos2]\n","sub_path":"cogie/io/processor/re/baidu.py","file_name":"baidu.py","file_ext":"py","file_size_in_byte":4369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128085002","text":"\"\"\"\nhttps://github.com/apache/spark/blob/master/examples/src/main/python/ml/dataframe_example.py\n\"\"\"\n\nfrom __future__ import print_function\n\nimport os\nimport tempfile\nimport shutil\n\nfrom pyspark.sql import SparkSession\nfrom pyspark.mllib.stat import Statistics\nfrom pyspark.mllib.util import MLUtils\n\nspark = SparkSession.builder.appName(\"DataFrameML\").getOrCreate()\n\ndf = spark.read.format(\"libsvm\").load(\"testdata/libsvm.data\").cache()\nprint(\"Schema from LIBSVM:\")\ndf.printSchema()\nprint(\"Loaded training data as a DataFrame with \" + str(df.count()) + \" records.\")\n\n# show statistical summary of labels\nlabelSummary = df.describe(\"label\")\nlabelSummary.show()\n\n# convert features column to an RDD of vectors\nfeatures = MLUtils.convertVectorColumnsFromML(df, \"features\") \\\n .select(\"features\") \\\n .rdd \\\n .map(lambda r: r.features)\nsummary = Statistics.colStats(features)\nprint(\"Selected features solumn with average values:\\n\" + str(summary.mean()))\n\n# save the records in a parquet file\ntempdir = tempfile.NamedTemporaryFile(delete=False).name\nos.unlink(tempdir)\nprint(\"Saving to \" + tempdir + \" as Parquet file.\")\ndf.write.parquet(tempdir)\n\n# load the records back\nprint(\"Loading Parquet file with UDT from \" + tempdir)\nnewDF = spark.read.parquet(tempdir)\nprint(\"Schema from Parquet:\")\nnewDF.printSchema()\nshutil.rmtree(tempdir)\n\nspark.stop()\n","sub_path":"refs/pyspark/ml/df.py","file_name":"df.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"473181387","text":"from config import GS1_USER, GS1_PASS, GS1_URL, BINARY_LOC\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support import expected_conditions as EC\nimport os\nimport time\n\n\n\nclass Page:\n\n def __init__(self):\n chrome_options = Options()\n chrome_options.headless = False\n chrome_options.binary_location = BINARY_LOC\n\n self.driver = webdriver.Chrome(os.path.abspath('chromedriver'),\n options=chrome_options)\n self.driver.implicitly_wait(10)\n\n # can we add the chrome_options here?\n def quit(self):\n return self.driver.quit()\n\n def login(self):\n driver = self.driver\n driver.get(GS1_URL)\n driver.find_element_by_xpath(\n '//input[@id=\"signInName\"]').send_keys(GS1_USER)\n driver.find_element_by_xpath(\n '//input[@id=\"password\"]').send_keys(GS1_PASS)\n\n driver.find_element_by_xpath(\n '//button[@id=\"next\"]').click()\n # lands on 'Products' page\n driver.find_element_by_xpath(\n '//*[@id=\"product\"]/a').click()\n return print('Logged in...')\n\n def create_new(self, obj):\n driver = self.driver\n driver.find_element_by_xpath('//*[@id=\"addnewproduct\"]/a').click()\n # Single pass of GS1 creation loop.\n de = driver.find_element_by_xpath('//input[@id=\"txtProductDescription\"]')\n de.send_keys(obj.name)\n # Brand Name\n bn = driver.find_element_by_xpath('//input[@id=\"txtBrandName\"]')\n bn.send_keys(obj.brand_name)\n # SKU\n sku = driver.find_element_by_xpath('//input[@id=\"txtSKU\"]')\n sku.send_keys(obj.sku)\n # Need to save here\n driver.find_element_by_xpath('//button[@id=\"btnSave\"]').click()\n #Waits for the auto assign to pop up, clicks it and then moves to the continue model.\n time.sleep(1)\n element = WebDriverWait(driver, 10).until(\n EC.element_to_be_clickable((By.XPATH, '//button[@id=\"btnAutoAssign\"]'))\n ).click()\n driver.find_element_by_xpath('//button[@id=\"btnAutoAssignGtinStartContinue\"]').click()\n \n## # Save right here.\n## time.sleep(1)\n## elementd = WebDriverWait(driver, 10).until(\n## EC.element_to_be_clickable((By.XPATH, '//button[@id=\"btnSave\"]'))\n## )\n## elementd.click()\n#### driver.find_element_by_xpath('//button[@id=\"btnSave\"]').click()\n## # Try to find status dropdown and change\n try:\n time.sleep(3)\n dropdown = Select(driver.find_element_by_xpath('//select[@id=\"ddlStatus\"]'))\n dropdown.select_by_visible_text(\"In Use\")\n\n # Save again.\n driver.find_element_by_xpath('//button[@id=\"btnSave\"]').click()\n # asks for continue if marked 'In Use'\n try:\n driver.find_element_by_xpath('//button[@id=\"btnConfirmInUse\"]').click()\n except:\n print('failed')\n\n except Exception as err:\n print(err)\n driver.find_element_by_xpath('//button[@id=\"btnSave\"]').click()\n print('Item saved as Draft.')\n pass\n\n def download(self):\n driver = self.driver\n # Currently only starts from download point\n # Move on to barcode\n driver.find_element_by_xpath(\n '//*[@id=\"ProductdetailTabs\"]/li[6]/a').click()\n \n # Attempts to wait for the 'preview' button.\n time.sleep(1)\n driver.find_element_by_xpath(\n '//button[@id=\"btnPreview\"]').click()\n # downloads preview PNG\n driver.find_element_by_xpath(\n '//button[@id=\"btnDownload\"]').click()\n upc = driver.find_element_by_xpath('/html/body/main/div/div[1]/div[1]/div/div/h1').text\n self.upc = upc.split(' ')[2][3:-1]\n print(f'Downloading png for {self.upc}')\n\n # closes\n time.sleep(1)\n driver.find_element_by_xpath(\n '//*[@id=\"barcodePreviewModal\"]/div[2]/div[1]/div[1]').click()\n print('download button clicked...')\n # returns the UPC into the object.\n return self.upc\n\n def find_existing(self, sku):\n download_path = os.path.expanduser('~') + '\\\\downloads\\\\'\n driver = self.driver\n sku_field = driver.find_element_by_xpath('//input[@id=\"dtProductListSKU5\"]')\n sku_field.send_keys(sku)\n time.sleep(1)\n driver.find_element_by_xpath('//*[@id=\"dtProductList\"]/tbody/tr/td[3]/a').click()\n upc = self.download()\n old_file = f'00{upc} UPC-A SST1.png'\n new_file = f'{sku} UPC.png'\n os.rename(download_path + old_file, download_path + new_file)\n## Rename file? \n driver.get('https://dh.gs1us.org/Product/Home') \n\n pass\n def create_and_download(self, obj):\n self.create_new(self, obj)\n self.download()\n\n return\n\n\n","sub_path":"GS1Class.py","file_name":"GS1Class.py","file_ext":"py","file_size_in_byte":5174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"596875915","text":"import torch\nfrom torch.optim import Optimizer\nimport math\nimport sys\nimport traceback\nfrom functools import lru_cache\nfrom torch_ps.import_util import get_libtorch_embedding_module_dim\n\n\nclass BaseTorchPSOptimizer(Optimizer):\n def __init__(self, params, defaults):\n if \"staleness\" not in defaults:\n ValueError(\"staleness is not specified\")\n if isinstance(defaults[\"staleness\"], bool):\n ValueError(\"staleness must be bool\")\n super(BaseTorchPSOptimizer, self).__init__(params, defaults)\n self.safe = defaults.get(\"safe\", True)\n assert isinstance(self.safe, bool)\n\n def step(self, rank=-1, total_ranks=1, staleness=1, chunked_grad=True):\n if self.defaults[\"staleness\"]:\n staleness_coef = 1.0 / max(staleness, 1)\n # print(staleness_coef, chunked_grad)\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n if chunked_grad:\n grad = p.grad.data.view(-1)\n if len(grad) > total_ranks > 1:\n grad = grad.chunk(total_ranks)\n if rank < len(grad):\n grad = grad[rank]\n grad.mul_(staleness_coef)\n elif rank == 0:\n p.grad.mul_(staleness_coef)\n else:\n p.grad.mul_(staleness_coef, total_ranks)\n\n\nclass FastEmbeddingOptimizerBase(BaseTorchPSOptimizer):\n def share_memory(self):\n pass\n\n def step(self, rank=-1, total_ranks=1, staleness=1, closure=None):\n super(FastEmbeddingOptimizerBase, self).step(rank, total_ranks, staleness, False)\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n if p.hash_table is None:\n raise ValueError(\"No hash table found. Seems you've forgot to set hash table\")\n\n p.hash_table.update(p.grad, self.updater, total_ranks, self.safe)\n\n def update_counter(self, value=1):\n self.updater.update_counter(value)\n\n def erase_useless(self):\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n if p.hash_table is None:\n raise ValueError(\"No hash table found. Seems you've forgot to set hash table\")\n\n p.hash_table.erase_useless(self.updater)\n\n\nclass EmbeddingFTRLOptimizer(FastEmbeddingOptimizerBase):\n def __init__(self, params, dim=1, lr_eta=0.1, lr_beta=0.0001, l2=0, l1=0, decay=1.0, ttl=1 << 31, staleness=False, safe=True):\n self.updater = getattr(get_libtorch_embedding_module_dim(dim), 'FTRLEmbeddingUpdater_{}'.format(dim))(lr_eta, lr_beta, l1, l2, decay, ttl)\n super(EmbeddingFTRLOptimizer, self).__init__(params, dict(dim=dim, lr_eta=lr_eta, lr_beta=lr_beta, l2=l2, l1=l1, ttl=ttl, staleness=staleness, safe=safe))\n\n\nclass Adagrad(BaseTorchPSOptimizer):\n \"\"\"Implements Adagrad algorithm.\n\n It has been proposed in `Adaptive Subgradient Methods for Online Learning\n and Stochastic Optimization`_.\n\n Arguments:\n params (iterable): iterable of parameters to optimize or dicts defining\n parameter groups\n lr (float, optional): learning rate (default: 1e-2)\n lr_decay (float, optional): learning rate decay (default: 0)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n .. _Adaptive Subgradient Methods for Online Learning and Stochastic\n Optimization: http://jmlr.org/papers/v12/duchi11a.html\n \"\"\"\n\n def __init__(self, params, lr=1e-2, lr_beta=0.0001, lr_decay=0, weight_decay=0, staleness=False):\n defaults = dict(lr=lr, lr_decay=lr_decay, lr_beta=lr_beta, weight_decay=weight_decay, staleness=staleness)\n super(Adagrad, self).__init__(params, defaults)\n\n for group in self.param_groups:\n for p in group['params']:\n state = self.state[p]\n state['step'] = 1\n state['sum'] = torch.zeros_like(p.data).view(-1)\n\n def share_memory(self):\n for group in self.param_groups:\n for p in group['params']:\n state = self.state[p]\n state['sum'].share_memory_()\n\n def step(self, rank=-1, total_ranks=1, staleness=1, closure=None):\n \"\"\"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n super(Adagrad, self).step(rank, total_ranks, staleness)\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n\n state = self.state[p]\n\n chunked = False\n grad = p.grad.data.view(-1)\n data = p.data.view(-1)\n cur_sum = state['sum']\n if len(grad) > total_ranks > 1:\n chunked = True\n grad = grad.chunk(total_ranks)\n if rank < len(grad):\n grad = grad[rank]\n data = data.chunk(total_ranks)[rank]\n cur_sum = cur_sum.chunk(total_ranks)[rank]\n else:\n continue\n elif rank > 0:\n continue\n\n state['step'] += 1\n\n if group['weight_decay'] != 0:\n grad = grad.add(group['weight_decay'], data)\n\n clr = group['lr'] / (1 + (state['step'] - 1) * group['lr_decay'])\n\n cur_sum.addcmul_(1, grad, grad)\n std = cur_sum.sqrt().add_(group['lr_beta'])\n p.data.addcdiv_(-clr, grad, std)\n\n return loss\n\n\nclass Adam(BaseTorchPSOptimizer):\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\n weight_decay=0, amsgrad=False, staleness=False):\n if not 0.0 <= betas[0] < 1.0:\n raise ValueError(\"Invalid beta parameter at index 0: {}\".format(betas[0]))\n if not 0.0 <= betas[1] < 1.0:\n raise ValueError(\"Invalid beta parameter at index 1: {}\".format(betas[1]))\n defaults = dict(lr=lr, betas=betas, eps=eps,\n weight_decay=weight_decay, amsgrad=amsgrad, staleness=staleness)\n super(Adam, self).__init__(params, defaults)\n for group in self.param_groups:\n for p in group['params']:\n state = self.state[p]\n state['step'] = 0\n # Exponential moving average of gradient values\n state['exp_avg'] = torch.zeros_like(p.data).view(-1)\n # Exponential moving average of squared gradient values\n state['exp_avg_sq'] = torch.zeros_like(p.data).view(-1)\n if amsgrad:\n # Maintains max of all exp. moving avg. of sq. grad. values\n state['max_exp_avg_sq'] = torch.zeros_like(p.data).view(-1)\n\n def __setstate__(self, state):\n super(Adam, self).__setstate__(state)\n for group in self.param_groups:\n group.setdefault('amsgrad', False)\n\n def share_memory(self):\n for group in self.param_groups:\n for p in group['params']:\n state = self.state[p]\n state['step'] = 0\n state['exp_avg'].share_memory_()\n state['exp_avg_sq'].share_memory_()\n if group['amsgrad']:\n # Maintains max of all exp. moving avg. of sq. grad. values\n state['max_exp_avg_sq'].share_memory_()\n\n def step(self, rank=-1, total_ranks=1, staleness=1):\n super(Adam, self).step(rank, total_ranks, staleness)\n for group in self.param_groups:\n for p in group['params']:\n if p.grad is None:\n continue\n chunked = False\n grad = p.grad.data.view(-1)\n data = p.data.view(-1)\n if len(grad) > total_ranks > 1:\n chunked = True\n grad = grad.chunk(total_ranks)\n if rank < len(grad):\n grad = grad[rank]\n data = data.chunk(total_ranks)[rank]\n else:\n continue\n elif rank > 0:\n continue\n\n if grad.is_sparse:\n raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')\n amsgrad = group['amsgrad']\n\n state = self.state[p]\n\n if state['step'] is None:\n state['step'] = 0\n state['step'] += 1\n step = state['step']\n\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n if chunked:\n exp_avg = exp_avg.chunk(total_ranks)[rank]\n exp_avg_sq = exp_avg_sq.chunk(total_ranks)[rank]\n if amsgrad:\n max_exp_avg_sq = state['max_exp_avg_sq']\n if chunked:\n max_exp_avg_sq = max_exp_avg_sq.chunk(total_ranks)[rank]\n beta1, beta2 = group['betas']\n\n if group['weight_decay'] != 0:\n grad = grad.add(group['weight_decay'], data)\n\n # Decay the first and second moment running average coefficient\n\n exp_avg.mul_(beta1).add_(1 - beta1, grad)\n exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n if amsgrad:\n # Maintains the maximum of all 2nd moment running avg. till now\n torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)\n # Use the max. for normalizing running avg. of gradient\n denom = max_exp_avg_sq.sqrt().add_(group['eps'])\n else:\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n\n bias_correction1 = 1 - beta1 ** step\n bias_correction2 = 1 - beta2 ** step\n step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1\n if staleness:\n step_size /= staleness\n\n data.addcdiv_(-step_size, exp_avg, denom)\n\n\nclass ParameterServerOptimizer(object):\n def __init__(self, *optimizers):\n self.threaded_optimizers = [x for x in optimizers if isinstance(x, FastEmbeddingOptimizerBase)]\n self.synchronous_optimizers = [x for x in optimizers if not isinstance(x, FastEmbeddingOptimizerBase)]\n\n def share_memory(self):\n for x in self.threaded_optimizers + self.synchronous_optimizers:\n x.share_memory()\n\n def synchronous_step(self, rank=-1, total_ranks=1, staleness=1):\n try:\n for x in self.synchronous_optimizers:\n x.step(rank, total_ranks, staleness)\n finally:\n _, exc, _ = sys.exc_info()\n if exc is not None:\n traceback.print_exc()\n\n def thread_step(self, rank=-1, total_ranks=1, staleness=0):\n try:\n for x in self.threaded_optimizers:\n x.step(rank, total_ranks, staleness)\n finally:\n _, exc, _ = sys.exc_info()\n if exc is not None:\n traceback.print_exc()\n\n def step(self, rank=-1, total_ranks=1, staleness=0):\n self.thread_step(rank, total_ranks, staleness)\n self.synchronous_step(rank, total_ranks, staleness)\n\n def model_dict(self):\n res = {\n \"threaded_optimizers\" : [opt.state_dict() for opt in self.threaded_optimizers],\n \"synchronous_optimizers\" : [opt.state_dict() for opt in self.synchronous_optimizers]\n }\n return res\n\n def load_model_dict(self, state_dict):\n assert len(self.threaded_optimizers) == len(state_dict.get(\"threaded_optimizers\", []))\n assert len(self.synchronous_optimizers) == len(state_dict.get(\"synchronous_optimizers\", []))\n\n for i, local_state_dict in enumerate(state_dict.get(\"threaded_optimizers\", [])):\n self.threaded_optimizers[i].load_state_dict(local_state_dict)\n for i, local_state_dict in enumerate(state_dict.get(\"synchronous_optimizers\", [])):\n self.synchronous_optimizers[i].load_state_dict(local_state_dict)\n","sub_path":"torch_ps/optim.py","file_name":"optim.py","file_ext":"py","file_size_in_byte":12726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"649272513","text":"import sopel.module\n\nbodypartsarray = ['head','chest','arm','junk','leg']\narmorarray = ['helmet','breastplate','gauntlets','codpiece','greaves']\n\n@sopel.module.commands('armor')\ndef mainfunction(bot, trigger):\n bot.say(\"testing armor\")\n for bodypart in bodypartsarray:\n armortype = array_compare(bot, bodypart, bodypartsarray, armorarray)\n bot.say(str(bodypart) + \" = \" + str(armortype))\n \n \n \ndef array_compare(bot, indexitem, arraytoindex, arraytocompare):\n item = ''\n for x, y in zip(arraytoindex, arraytocompare):\n if x == indexitem:\n item = y\n return item\n \n \n","sub_path":"modules/testexamples/armor.py","file_name":"armor.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648529058","text":"import xml.etree.cElementTree as ET\r\nfrom collections import defaultdict\r\nimport re\r\nimport pprint\r\n\r\nOSMFILE = \"delaware_beach.osm\"\r\nstreet_type_re = re.compile(r'\\b\\S+\\.?$', re.IGNORECASE)\r\n\r\n\r\nexpected = [\"Boardwalk\", \"Street\", \"Avenue\", \"Boulevard\", \"Drive\", \"Court\", \"Place\", \"Square\", \"Lane\", \"Road\",\r\n \"Trail\", \"Parkway\", \"Commons\",\"Crescent\",\"Close\",\"East\",\"West\",\"North\",\"South\",\"Way\",\"Terrace\"]\r\n\r\n# UPDATE THIS VARIABLE\r\nmapping = { \"St\": \"Street\",\r\n \"St.\": \"Street\",\r\n \"Blvd\": \"Boulevard\",\r\n \"Blvd.\": \"Boulevard\",\r\n \"Ave\": \"Avenue\",\r\n \"Ave.\": \"Avenue\",\r\n \"Rd\": \"Road\",\r\n \"STREET\": \"Street\",\r\n \"avenue\": \"Avenue\",\r\n \"street\": \"Street\",\r\n \"E\": \"East\",\r\n \"W\": \"West\",\r\n \"Ln\": \"Lane\",\r\n \"Cout\": \"Court\",\r\n \"Hwy\": \"Highway\",\r\n \"N\": \"North\",\r\n \"S\": \"South\",\r\n \"Bdwk\": \"Boardwalk\"\r\n }\r\n\r\n\r\ndef audit_street_type(street_types, street_name):\r\n m = street_type_re.search(street_name)\r\n if m:\r\n street_type = m.group()\r\n if street_type not in expected:\r\n street_types[street_type].add(street_name)\r\n\r\n\r\ndef is_street_name(elem):\r\n return (elem.attrib['k'] == \"addr:street\")\r\n\r\n\r\ndef audit(osmfile):\r\n osm_file = open(osmfile, \"r\")\r\n street_types = defaultdict(set)\r\n for event, elem in ET.iterparse(osm_file, events=(\"start\",)):\r\n\r\n if elem.tag == \"node\" or elem.tag == \"way\":\r\n for tag in elem.iter(\"tag\"):\r\n if is_street_name(tag):\r\n audit_street_type(street_types, tag.attrib['v'])\r\n print (street_types)\r\n break\r\n osm_file.close()\r\n return street_types\r\n\r\n\r\ndef update_name(name, mapping):\r\n\r\n for key, value in mapping.iteritems():\r\n if re.search(key, name):\r\n name = re.sub(street_type_re, value, name)\r\n\r\n return name\r\n\r\n\r\ndef do_things():\r\n st_types = audit(OSMFILE)\r\n pprint.pprint(dict(st_types))\r\n\r\n return\r\n\r\nif __name__ == '__main__':\r\n do_things()","sub_path":"clean_streets_audit.py","file_name":"clean_streets_audit.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"250734260","text":"'''\nFind the nth digit of the infinite integer sequence 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ...\n\nNote:\nn is positive and will fit within the range of a 32-bit signed integer (n < 231).\n\nExample 1:\n\nInput:\n3\n\nOutput:\n3\nExample 2:\n\nInput:\n11\n\nOutput:\n0\n\nExplanation:\nThe 11th digit of the sequence 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, ... is a 0, which is part of \nthe number 10.\n'''\n\ndef nth_digit(n):\n start, size, step = 1, 1, 9\n\n while n > size * step:\n n, start, size, step = n - size * step, start * 10, size + 1, step * 10\n\n return int(str(start + (n - 1) // size)[(n - 1) % size])\n\ndef nth_digit_v2(n):\n start, size = 1, 1\n\n while n > size:\n n, start = n - size, start + 1\n size = len(str(start))\n\n return int(str(start)[n-1])\n\n","sub_path":"algorithms/math/nth_digit.py","file_name":"nth_digit.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"430082362","text":"import datetime, inspect\nfrom paw_wis.sequencers import Sequence\nfrom paw_wis.tasks import Task\nfrom paw_wis import debugging\nfrom paw_wis.sqs import SQSClient, SQSQueues\n\n\nclass SampleTask(Task):\n '''\n This is an example task.\n '''\n def __init__(self, task_output_queue, sqs_client, task_pickup_queue=None, echo_text=None, show_timestamp=True, set_error=False):\n '''\n This is an example task that will produce a message for another example task to consume\n :param task_output_queue: String with the queue name of where messages will be posted\n :param sqs_client: SQSClient instance\n :param task_pickup_queue: [OPTIONAL] String with queue to check for messages to process. DEFAULT=None\n :param echo_text: [OPTIONAL] String with text to echo. DEFAULT=None\n :param show_timestamp: Boolean to indicate if timestamps must be shown. DEFAULT=True\n :param set_error: Boolean to indicate if this task must set an error condition. DEFAULT=False\n '''\n debugging.debug_dump(\"example_queue: %s\" % sqs_client.queues.is_queue_managed('example_queue'), line_number=inspect.currentframe().f_lineno, stack=\"%s\" % inspect.getfile(inspect.currentframe()))\n super(SampleTask, self).__init__(task_pickup_queue, task_output_queue, sqs_client)\n if echo_text is None:\n self.init_params['Echo'] = ''\n else:\n self.init_params['Echo'] = echo_text\n self.init_params['DateStr'] = None\n if show_timestamp is not None:\n self.init_params['DateStr'] = datetime.datetime.now().strftime('%Y-%m-%d %X')\n self.init_params['Err'] = set_error\n\n def run(self):\n print(\"*** I am a running SampleTask() instance...\")\n if 'QueuedData' in self.init_params:\n self.init_params['Echo'] = \"Data from Queue: %s\" % self.init_params['QueuedData']\n if self.init_params['DateStr'] is not None:\n self.data = '[%s] %s' % (self.init_params['DateStr'], self.init_params['Echo'])\n else:\n self.data = '[No DateStr Data] %s' % self.init_params['Echo']\n if 'Err' in self.init_params or 'Error' in self.init_params:\n self.is_error = self.init_params['Err']\n if self.is_error:\n self.data = '[ERROR RAISED] %s' % self.data\n debugging.debug_dump(\"MY DATA: %s\" % self.data, line_number=inspect.currentframe().f_lineno, stack=\"%s\" % inspect.getfile(inspect.currentframe()))\n print(\"*** DATA: %s\" % self.data)\n print(\"*** DONE\")\n\n\nclass SampleSequence(Sequence):\n def __init__(self, show_timestamps=True, set_error=False, tasks_to_use=('t1', 't2')):\n q = SQSQueues()\n if q.is_queue_managed('example_queue') is False:\n q.create_queue('example_queue')\n debugging.debug_dump(\"queue 'example_queue' managed: %s\" % q.is_queue_managed('example_queue'), line_number=inspect.currentframe().f_lineno, stack=\"%s\" % inspect.getfile(inspect.currentframe()))\n # task_output_queue, sqs_client, task_pickup_queue=None, echo_text=None, show_timestamp=True, set_error=False\n t1 = SampleTask(task_output_queue='example_queue', sqs_client=SQSClient(q), echo_text='SampleSequence is working!', show_timestamp=show_timestamps, set_error=set_error)\n t2 = SampleTask(task_output_queue='example_queue', sqs_client=SQSClient(q), task_pickup_queue='example_queue', echo_text=None, show_timestamp=show_timestamps, set_error=set_error)\n final_tasks = []\n for t2use in tasks_to_use:\n if t2use == 't1':\n final_tasks.append(t1)\n elif t2use == 't2':\n final_tasks.append(t2)\n final_tasks = tuple(final_tasks)\n super(SampleSequence, self).__init__(final_tasks)\n\n\nSampleSequence(tasks_to_use=('t1', 't2'))","sub_path":"tests/sequencer_test_01.py","file_name":"sequencer_test_01.py","file_ext":"py","file_size_in_byte":3824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"645215798","text":"from __future__ import division\nfrom numpy.linalg import norm\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport model_aggregator\nimport softmax_model_test\nimport softmax_model_obj\nimport poisoning_compare\nimport heapq\nimport numpy as np\nimport utils\n\nimport pdb\nimport sys\n\nnp.set_printoptions(suppress=True)\n\n\n# Just a simple sandbox for testing out python code, without using Go.\ndef debug_signal_handler(signal, frame):\n import pdb\n pdb.set_trace()\n\n\nimport signal\n\nsignal.signal(signal.SIGINT, debug_signal_handler)\n\n\ndef basic_conv(dataset, num_params, softmax_test, iterations=3000):\n batch_size = 5\n\n # Global\n # numFeatures = softmax_model.init(dataset, epsilon=epsilon)\n softmax_model = softmax_model_obj.SoftMaxModel(dataset, numClasses)\n\n print(\"Start training\")\n\n weights = np.random.rand(num_params) / 100.0\n\n train_progress = np.zeros(iterations)\n test_progress = np.zeros(iterations)\n\n for i in range(iterations):\n deltas = softmax_model.privateFun(1, weights, batch_size)\n weights = weights + deltas\n\n if i % 100 == 0:\n print(\"Train error: %.10f\" % softmax_test.train_error(weights))\n\n print(\"Done iterations!\")\n print(\"Train error: %d\", softmax_test.train_error(weights))\n print(\"Test error: %d\", softmax_test.test_error(weights))\n return weights\n\n\ndef rescale(x, a, b):\n minNum = np.min(x)\n maxNum = np.max(x)\n return (b - a) * (x - minNum) / (maxNum - minNum) + a\n\n\ndef cos(vecA, vecB):\n return np.dot(vecA, vecB) / (np.linalg.norm(vecA) * np.linalg.norm(vecB))\n\n\n# Variant of non_iid, where noise is added to poisoner_indices\ndef non_iid(model_names, numClasses, numParams, softmax_test, topk_prop, iterations=3000, numSybils=2,\n ideal_attack=False, poisoner_indices=[], solution=None):\n batch_size = 50\n topk = int(numParams / 10)\n\n list_of_models = []\n\n for dataset in model_names:\n list_of_models.append(softmax_model_obj.SoftMaxModel(dataset, numClasses))\n\n # Include the model that sends the ideal vector on each iteration\n if ideal_attack:\n list_of_models.append(softmax_model_obj.SoftMaxModelEvil(dataPath +\n \"_bad_ideal_4_9\", numClasses))\n\n numClients = len(list_of_models)\n model_aggregator.init(numClients, numParams, numClasses)\n\n print(\"\\nStart training across \" + str(numClients) + \" clients with solution \" + str(solution) + '.')\n\n weights = np.random.rand(numParams) / 100.0\n lr = np.ones(numClients, )\n acc_in_iterations = []\n delta_all = []\n train_progress = []\n norm_progress = []\n loss_progress = []\n\n summed_deltas = np.zeros((numClients, numParams))\n\n for i in range(iterations):\n\n delta = np.zeros((numClients, numParams))\n\n # Significant features filter\n # sig_features_idx = np.argpartition(weights, -topk)[-topk:]\n sig_features_idx = np.arange(numParams)\n\n for k in range(len(list_of_models)):\n delta[k, :], _ = list_of_models[k].privateFun(weights, batch_size)\n\n # normalize delta\n if np.linalg.norm(delta[k, :]) > 1:\n delta[k, :] = delta[k, :] / np.linalg.norm(delta[k, :])\n\n # Add adversarial noise\n noisevec = rescale(np.random.rand(numParams), np.min(delta), np.max(delta))\n delta[poisoner_indices[0], :] = delta[poisoner_indices[0], :] + noisevec\n delta[poisoner_indices[1], :] = delta[poisoner_indices[1], :] - noisevec\n\n # Track the total vector from each individual client\n summed_deltas = summed_deltas + delta\n if solution:\n if solution == 'fg':\n # Use Foolsgold\n this_delta = model_aggregator.foolsgold(delta, summed_deltas, sig_features_idx, i, weights, lr,\n topk_prop,\n importance=False, importanceHard=True)\n if solution == 'ours':\n this_delta, lr = model_aggregator.foolsgold2(delta, summed_deltas, sig_features_idx, i, weights, lr,\n topk_prop,\n importance=False, importanceHard=True)\n if solution == 'krum':\n # Krum\n this_delta = model_aggregator.krum(delta, clip=1)\n if solution == 'average':\n this_delta = model_aggregator.average(delta)\n if solution == 'median':\n this_delta = model_aggregator.median(delta)\n if solution == 'trimmed_mean':\n this_delta = model_aggregator.trimmed_mean(delta, 0.2)\n else:\n this_delta = np.dot(delta.T, lr)\n\n weights = weights + this_delta\n\n if i % 10 == 0:\n delta_index = heapq.nlargest(20, range(len(this_delta)), this_delta.take)\n delta_each_client = []\n for idx in delta_index:\n delta_each_client.append(np.hstack(([i, idx], delta[:, idx], this_delta[idx])))\n delta_all.append(delta_each_client)\n norm_progress.append(np.mean(np.linalg.norm(delta, axis=1)))\n test_error = softmax_test.test_error(weights)\n train_progress.append(test_error)\n acc_in_iterations.append(\n [test_error] + list(poisoning_compare.eval(Xtest, ytest, weights, int(from_class), int(to_class),\n numClasses, numFeatures, verbose=False)))\n\n # if i % 100 == 0:\n # print(\"Validation error: %.5f\" % test_error)\n column = ['iteration', 'deltaInxex'] + ['client{}'.format(i) for i in range(numClients)] + ['combined']\n pd.DataFrame(columns=column,\n data=np.reshape(delta_all, (-1, len(column)))).to_csv(\n '_'.join(argv) + '_' + str(solution) + '_delta.csv')\n test_error = softmax_test.test_error(weights)\n acc_in_iterations.append(\n [test_error] + list(poisoning_compare.eval(Xtest, ytest, weights, int(from_class), int(to_class),\n numClasses, numFeatures, verbose=True)))\n # column = ['iteration', 'Test error', 'Accuracy overall', 'Accuracy on other digits',\n # 'Target Accuracy on source label',\n # 'Target Accuracy on target label', 'Target Attack Rate']\n # acc_in_iterations = np.insert(acc_in_iterations, 0, values=np.arange(0, iterations + 1, 10), axis=1)\n # res = pd.DataFrame(columns=column, data=acc_in_iterations)\n # res.to_csv('_'.join(argv) + '_' + str(solution) + '.csv')\n print(\"Done iterations!\")\n print(\"Train error: {}\".format(softmax_test.train_error(weights)))\n print(\"Test error: {}\".format(softmax_test.test_error(weights)))\n # pdb.set_trace()\n # import sklearn.metrics.pairwise as smp\n # cs = smp.cosine_similarity(summed_deltas)\n return weights\n\n\n# amazon: 50 classes, 10000 features\n# mnist: 10 classes, 784 features\n# kdd: 23 classes, 41 features\nif __name__ == \"__main__\":\n argv = sys.argv[1:]\n\n dataset = argv[0]\n iterations = int(argv[1])\n\n if dataset == \"mnist\":\n numClasses = 10\n numFeatures = 784\n elif dataset == \"kddcup\":\n numClasses = 23\n numFeatures = 41\n elif dataset == \"amazon\":\n numClasses = 50\n numFeatures = 10000\n else:\n print(\"Dataset \" + dataset + \" not found. Available datasets: mnist kddcup amazon\")\n\n numParams = numClasses * numFeatures\n dataPath = dataset + \"/\" + dataset\n\n full_model = softmax_model_obj.SoftMaxModel(dataPath + \"_train\", numClasses)\n Xtest, ytest = full_model.get_data()\n\n models = []\n\n for i in range(numClasses):\n # Try a little more IID\n models.append(dataPath + str(i)) # + str((i + 1) % 10) + str((i\n # + 2) % 10))\n from_class = to_class = None\n for attack in argv[2:]:\n attack_delim = attack.split(\"_\")\n sybil_set_size = attack_delim[0]\n from_class = attack_delim[1]\n to_class = attack_delim[2]\n for i in range(int(sybil_set_size)):\n models.append(dataPath + \"_bad_\" + from_class + \"_\" + to_class)\n\n softmax_test = softmax_model_test.SoftMaxModelTest(dataset, numClasses, numFeatures)\n # Hard code poisoners in a 2_x_x attack\n eval_data = np.ones((10, 5))\n for eval_i in range(1):\n topk_prop = 0.1 + eval_i * .1\n\n weights = non_iid(models, numClasses, numParams, softmax_test, topk_prop, iterations, int(sybil_set_size),\n ideal_attack=False, poisoner_indices=[10, 11], solution=None)\n non_iid(models, numClasses, numParams, softmax_test, topk_prop, iterations, int(sybil_set_size),\n ideal_attack=False, poisoner_indices=[10, 11], solution='ours')\n non_iid(models, numClasses, numParams, softmax_test, topk_prop, iterations, int(sybil_set_size),\n ideal_attack=False, poisoner_indices=[10, 11], solution='krum')\n non_iid(models, numClasses, numParams, softmax_test, topk_prop, iterations, int(sybil_set_size),\n ideal_attack=False, poisoner_indices=[10, 11], solution='median')\n non_iid(models, numClasses, numParams, softmax_test, topk_prop, iterations, int(sybil_set_size),\n ideal_attack=False, poisoner_indices=[10, 11], solution='trimmed_mean')\n\n # for attack in argv[2:]:\n # attack_delim = attack.split(\"_\")\n # from_class = attack_delim[1]\n # to_class = attack_delim[2]\n # score = poisoning_compare.eval(Xtest, ytest, weights, int(from_class), int(to_class), numClasses,\n # numFeatures)\n # eval_data[eval_i] = score\n #\n # np.savetxt('hard_topk_eval_data.csv', eval_data, '%.5f', delimiter=\",\")\n # # Sandbox: difference between ideal bad model and global model\n # compare = False\n # if compare:\n # bad_weights = basic_conv(dataPath + \"_bad_ideal_\" + from_class + \"_\" +\n # to_class, numParams, softmax_test)\n # poisoning_compare.eval(Xtest, ytest, bad_weights, int(from_class),\n # int(to_class), numClasses, numFeatures)\n\n # diff = np.reshape(bad_weights - weights, (numClasses, numFeatures))\n # abs_diff = np.reshape(np.abs(bad_weights - weights), (numClasses,\n # numFeatures))\n\n # pdb.set_trace()\n","sub_path":"ML/code/ML_noisyPoisoners.py","file_name":"ML_noisyPoisoners.py","file_ext":"py","file_size_in_byte":10494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"39494491","text":"\"\"\"\n This spider is a Recruitacommunity spider created on top of the ATSSpider\n scrapy crawl recruitacommunity -a mining_job_id=9999 -a iteration=1 -a extract=1 -a url=\"https://www.recruitacommunity.com/srctcb/RTI.home?t=7465\"\n\n sample seed url:\n https://www.recruitacommunity.com/srctcb/RTI.home?t=11265\n https://www.recruitacommunity.com/srctcb/RTI.home?t=62664\n https://www.recruitacommunity.com/srctcb/RTI.home?t=56260\n\n sample job url:\n https://www.recruitacommunity.com/srctcb/reqDetails.ajax?_reqID=5000017188110&_tcbWidgetID=164965\n\"\"\"\n\nfrom urllib import urlencode\nfrom re import compile, sub\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\nfrom urlparse import urljoin\n\nfrom brightcorp.base.atsspiders import ATSSpider\nfrom brightcorp.items import BrightcorpItemLoader\nfrom brightcorp.processors import Prefix, Replace, NormalizedJoin\n\n\nclass Recruitacommunity(ATSSpider):\n\n name = \"recruitacommunity\"\n handle_httpstatus_list = [404]\n download_delay = 1\n Redirect_Url = compile(r\",\\s*'(.*searchResults.page)',\")\n Widget_Id = compile(r'\"&_tcbWidgetID=(\\d+)\"')\n Job_Url = compile(r\"(_reqID=(\\S+)&_tcbWidgetID=\\S+)&_popup=\")\n Company_Id = compile(r\"\\?t=(\\d+)\")\n company_id = ''\n Location_Re = compile(r\"(\\(.*\\))\")\n cur_page = 0\n item_map = {\n 'Title': 'title',\n 'Location': 'location',\n 'Company Name': 'company',\n 'Position Title:': 'title',\n 'Location:': 'location',\n 'Job Family:': 'jobcategory',\n 'Position Type:': 'jobtype',\n 'City': 'city',\n 'State': 'state',\n 'Job Function': 'jobcategory',\n }\n\n def __init__(self, *args, **kwargs):\n super(Recruitacommunity, self).__init__(*args, **kwargs)\n\n self.company_id = self.Company_Id.search(self.start_urls[0])\n if self.company_id:\n self.company_id = self.company_id.group(1)\n\n def parse(self, response):\n redirect_url = response.xpath('//script').re(self.Redirect_Url)\n widget_id = response.xpath('//script').re(self.Widget_Id)\n if redirect_url and widget_id:\n params = {\n '_tcbWidgetID': widget_id[0],\n '_wrenderer_SEARCH_resultsStart': str(self.cur_page)\n }\n url = urljoin(\n response.url, '/srctcb%s?%s' % (\n redirect_url[0], urlencode(params)\n )\n )\n yield Request(\n callback=self.parse_jobs_list,\n url=url\n )\n\n def parse_jobs_list(self, response):\n selector = Selector(response)\n tableheads = selector.xpath(\n '//table[@class=\"tcbReqs\"]/thead//span[@class=\"tcbSearch_resultsHeader\"]/text()'\n ).extract()\n jobs = selector.xpath(\n '//table[@class=\"tcbReqs\"]//tr[contains(@class, \"tcbReqs\")]')\n for job in jobs:\n url = job.xpath('./td/a/@href').re(self.Job_Url)\n if url:\n meta = {}\n for index, th in enumerate(tableheads):\n if th in self.item_map:\n meta[self.item_map[th]] = job.xpath(\n \"./td[\" + str(index + 1) + \"]//text()\").extract()\n\n meta['ref_num'] = url[1]\n\n job_url = urljoin(\n response.url, '/srctcb/reqDetails.ajax?%s' % url[0]\n )\n yield Request(\n callback=self.parse_job_callback(),\n meta=meta,\n url=job_url\n )\n\n next_page = selector.xpath(\n '//div[@class=\"tcbSearch_nextPage\"]/input/@value').extract()\n if next_page:\n self.cur_page += 5\n yield Request(\n callback=self.parse_jobs_list,\n url=sub(\n '_wrenderer_SEARCH_resultsStart=(\\d+)',\n '_wrenderer_SEARCH_resultsStart=%s' % (self.cur_page),\n response.url\n )\n )\n\n def parse_job(self, response):\n loader = BrightcorpItemLoader(response=response)\n url = urljoin(\n response.url, '/srctcb/RTI.home?t=%(company)s&r=%(ref_id)s' % {\n 'company': self.company_id,\n 'ref_id': response.meta.get('ref_num')\n }\n )\n desc_xpath = '//td[contains(text(), \"%s\")]/../following-sibling::tr[1]'\n\n loader.add_xpath(\n 'description',\n desc_xpath % 'Description'\n )\n loader.add_xpath(\n 'requirements', desc_xpath % 'Requirements'\n )\n\n loader.add_value(\n 'referencenumber', response.meta.get('ref_num'),\n Prefix('%s-%s-' % (self.name, self.company_id))\n )\n loader.add_value(\n 'location', response.meta.get('location'),\n Replace('Working at Home;'), Replace(self.Location_Re)\n )\n if not loader.get_output_value('location'):\n loc = [response.meta.get(i)[0] for i in ['city', 'state'] if response.meta.get(i)]\n loader.add_value('location', loc, NormalizedJoin(\", \"))\n\n loader.add_value('jobcategory', response.meta.get('jobcategory'))\n loader.add_value('jobtype', response.meta.get('jobtype'))\n loader.add_value('company', response.meta.get('company'))\n loader.add_value('title', response.meta.get('title', ''))\n loader.add_value('url', url)\n loader.add_value('apply_url', url)\n\n yield loader.load_item()\n","sub_path":"brightcorp/brightcorp/spiders/recruitacommunity.py","file_name":"recruitacommunity.py","file_ext":"py","file_size_in_byte":5583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"213781682","text":"from mudwyrm_users.admin.achaea.trigger import Trigger, Alias, OnEvent\r\n\r\np = None\r\n\r\ndef init(processor):\r\n assert processor is not None\r\n global p\r\n p = processor\r\n \r\n\r\n#@OnEvent('BalanceChanged')\r\n#def on_balance_changed(type):\r\n# if balance(type):\r\n# p.echo(\"Balance gained: %s\" % type)\r\n# else:\r\n# p.echo(\"Balance lost: %s\" % type)\r\n\r\n@OnEvent('StatusChanged')\r\ndef on_status_changed(type, value):\r\n if value:\r\n p.hecho(('div', 'script', [\"%s is \" % type, ('span', 'green', \"up\"), \".\"]))\r\n p.debug(\"%s status is up.\" % type)\r\n else:\r\n p.hecho(('div', 'script', [\"%s is \" % type, ('span', 'red', \"down\"), \".\"]))\r\n p.debug(\"%s status is down.\" % type)\r\n","sub_path":"mudwyrm_users/admin/achaea/scripts/highlights.py","file_name":"highlights.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388926120","text":"# -*- coding: utf-8 -*-\nfrom openerp import models, fields, api\nimport datetime\nfrom dateutil.relativedelta import relativedelta\nfrom openerp.exceptions import Warning, ValidationError\nfrom num2words import num2words\n\n\nclass jmdcalculator(models.Model):\n _inherit = \"mail.thread\"\n _name = \"sofom.calculator\"\n\n @api.depends('lineas')\n def get_pago(self):\n for record in self:\n monto = 0\n for linea in record.lineas:\n monto = linea.total\n if record.frecuencia == \"26\":\n monto *= 2\n elif record.frecuencia == \"52\":\n monto *= 4\n record.pago = monto\n\n @api.one\n def get_comision(self):\n monto = 0\n if self.producto == \"nom\":\n if self.monto < 9000:\n monto = self.monto * 0.03\n elif self.monto >= 9000:\n monto = 406\n self.comision = monto\n\n @api.one\n def get_centavos(self):\n centavos = round(self.monto - int(self.monto), 2)\n self.centavos = str(int(centavos * 100)).zfill(2)\n\n @api.one\n def get_letra(self):\n self.monto_letra = num2words(self.monto, lang='es').upper()\n\n @api.one\n def get_tasaletra(self):\n tasa = round(self.tasa.name, 1)\n entero = (int(tasa))\n decimal = round(tasa - int(tasa), 2) * 100\n self.tasa_letra = num2words(entero, lang='es').upper() + \" PUNTO \" +\\\n num2words(decimal, lang='es').upper()\n\n name = fields.Char(\"Descripción\", default=lambda self: self.\n env[\"ir.sequence\"].get(\"sofom.calculator\"))\n monto = fields.Float(\"Monto\")\n monto_letra = fields.Char(\"Monto con Letra\", compute=get_letra)\n total = fields.Float(\"Total del Prestamo\")\n ciclo = fields.Integer(\"Ciclo\", related=\"lead.partner_id.ciclo\",\n store=\"True\")\n producto = fields.Selection([(\"micro\", \"Microcrédito\"),\n (\"nom\", \"Nómina\")], string=\"Producto\", related=\"lead.producto\",\n store=True)\n pagos = fields.Integer(\"Numero de Pagos\")\n tipo = fields.Selection([(\"no\", \"Principal\"),\n (\"si\", \"Interciclo\"), ('an', \"Anticipo de Nómina\")], string=\"Tipo\")\n tasa = fields.Many2one(\"sofom.tasa\", string=\"Tasa mensual\")\n tasa_letra = fields.Char(\"Tasa Letra\", compute=get_tasaletra)\n inicio = fields.Date(\"Fecha de inicio\")\n lineas = fields.One2many(\"sofom.calculator.line\", \"calculator\", \"Lineas\")\n lead = fields.Many2one(\"crm.lead\", string=\"Prospecto\")\n frecuencia = fields.Selection([(\"26\", \"Quincenal\"), (\"52\", \"Semanal\"),\n (\"12\", \"Mensual\")], string=\"Frecuencia\")\n plazo = fields.Many2one(\"sofom.plazo\", string=\"Plazo\")\n pago = fields.Float(\"Pago Mensual\", compute=get_pago)\n destino = fields.Many2one(\"sofom.destino\", string=\"Destino de Crédito\")\n apertura = fields.Float(\"Comisión por apertura\")\n cat = fields.Float(\"CAT\")\n partner_id = fields.Many2one(\"res.partner\",\n string=\"Cliente\", related=\"lead.partner_id\")\n ciclo_principal = fields.Many2one(\"sofom.credito\", string=\"Ciclo Principal\")\n tope = fields.Float(\"Tope del Crédito\",\n related=\"plazo.monto_max\")\n comision = fields.Float(\"Comisión por Apertura\", compute=get_comision)\n\n total_intereses = fields.Float(\"Total Intereses\")\n total_iva = fields.Float(\"Total IVA\")\n centavos = fields.Char(\"Centavos\", compute=get_centavos)\n\n @api.onchange('monto')\n def onchange_monto(self):\n if self.monto > self.tope:\n self.monto = 0.0\n self.write({'monto': 0.0})\n raise Warning('Excede el tope del monto')\n return False\n\n @api.multi\n def c_delete(self):\n for i in self.lineas:\n i.unlink()\n\n @api.one\n @api.constrains('monto')\n def check_monto(self):\n if self.monto > self.tope:\n raise ValidationError(\"El monto cotizado excede el tope permitido\")\n\n def c_payment(self, cr, uid, ids, context=None):\n ret = {}\n for i in self.browse(cr, uid, ids, context):\n #finicio = datetime.srtptime(i.inicio, \"%Y-%m-%d\")\n insoluto = i.monto\n interes_periodo = i.tasa.name / (float(i.plazo.frecuencia) / 12)\n pago_fijo = i.calculate_payment(insoluto, interes_periodo,\n i.plazo.pagos)\n dias_plazo = i.plazo.dias_ciclo\n meses_plazo = i.plazo.meses_ciclo\n inicio_obj = datetime.datetime.strptime(i.inicio, \"%Y-%m-%d\")\n siguiente_pago = inicio_obj\n gran_total = 0\n total_iva = 0\n total_intereses = 0\n for j in range(i.plazo.pagos):\n line_obj = self.pool.get(\"sofom.calculator.line\")\n interes = i.calculate_interest(interes_periodo, insoluto)\n capital = i.calculate_capital(interes, pago_fijo)\n insoluto -= capital\n iva = (interes / 1.16) * 0.16\n total = pago_fijo\n gran_total += total\n total_iva += iva\n total_intereses += interes\n print((\"Numero de pago \" + str(j)))\n line_obj.create(cr, uid, {\n 'calculator': i.id,\n 'fecha': siguiente_pago.strftime(\"%Y-%m-%d\"),\n 'monto': pago_fijo,\n 'npago': str((j + 1)),\n 'capital': capital,\n 'intereses': interes,\n 'iva': iva,\n 'total': total,\n 'restante': insoluto,\n })\n self.write(cr, uid, ids, {'pago': total})\n siguiente_pago += datetime.timedelta(days=dias_plazo)\n siguiente_pago += relativedelta(months=+meses_plazo)\n self.write(cr, uid, [i.id], {'total': gran_total,\n 'total_iva': total_iva, 'total_intereses': total_intereses})\n return ret\n\n def calculate_payment(self, prestamo, interes, numero_cuotas):\n interes = interes / 100\n return prestamo * (interes * ((interes + 1) ** numero_cuotas)) / \\\n (((interes + 1) ** numero_cuotas) - 1)\n\n def calculate_interest(self, interes, capital_pendiente):\n return (interes / 100) * capital_pendiente\n\n def calculate_capital(self, monto_interes, monto_pago):\n return monto_pago - monto_interes\n\n\nclass jmdcalculatorline(models.Model):\n _name = \"sofom.calculator.line\"\n name = fields.Char(\"Nombre\")\n calculator = fields.Many2one(\"sofom.calculator\", \"Calculator\")\n fecha = fields.Date(\"Fecha\")\n monto = fields.Float(\"Monto\")\n npago = fields.Char(\"Periodo\")\n capital = fields.Float(\"Capital\")\n intereses = fields.Float(\"Intereses\")\n iva = fields.Float(\"IVA\")\n total = fields.Float(\"Pago Total\")\n restante = fields.Float(\"Capital Restante\")","sub_path":"sofom/cotizador.py","file_name":"cotizador.py","file_ext":"py","file_size_in_byte":6830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"233737102","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Jesse Rubin - project Euler\n\"\"\"\nSub-string divisibility\nProblem 43\nThe number, 1406357289, is a 0 to 9 pandigital number because it is made up\nof each of the digits 0 to 9 in some order, but it also has a rather\ninteresting sub-string divisibility property.\n\nLet d1 be the 1st digit, d2 be the 2nd digit, and so on.\nIn this way, we note the following:\n\nd2d3d4=406 is divisible by 2\nd3d4d5=063 is divisible by 3\nd4d5d6=635 is divisible by 5\nd5d6d7=357 is divisible by 7\nd6d7d8=572 is divisible by 11\nd7d8d9=728 is divisible by 13\nd8d9d10=289 is divisible by 17\n\nFind the sum of all 0 to 9 pandigital numbers with this property.\n\"\"\"\n\nfrom bib.listless import int_from_digits\nfrom itertools import permutations\n\n\ndef pandigital_substring_thing(pandigit_list):\n if pandigit_list[0] == 0:\n return False # leading 0 shouldnt count\n else:\n div_primes = [2, 3, 5, 7, 11, 13, 17]\n for i in range(1, 8):\n if int_from_digits(pandigit_list[i:i + 3]) % div_primes[i - 1] != 0:\n return False\n return True\n\n\ndef p043():\n # well_they_gave_us_this_one = [1,4,0,6,3,5,7,2,8,9]\n # test_answer = pandigital_substring_thing(well_they_gave_us_this_one)\n # print(test_answer)\n circle_to_nine = [i for i in range(0, 10)] # circle is the way kids say 0 now a days\n pandigit_lists = [int_from_digits(i) for i in permutations(circle_to_nine) if pandigital_substring_thing(i)]\n return sum(pandigit_lists)\n\n\nif __name__ == '__main__':\n ans = p043()\n print(\"Sum of products: {}\".format(ans))\n","sub_path":"done/py/euler_043.py","file_name":"euler_043.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"475583696","text":"# Create your views here.\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.http import HttpResponseRedirect, HttpResponse, HttpResponseNotFound\nfrom django.template import RequestContext\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\nimport simplejson, re\nfrom django.db import connection\nfrom datetime import datetime\nfrom django.http import Http404\nfrom django.core.urlresolvers import resolve\nfrom decimal import *\nfrom annotation_server.models import *\nfrom annotation_server.utils import *\nimport logging\nfrom django.db.models import Q\n\n# get module logger\nlogger = logging.getLogger(__name__)\n\ndef search_genes(request, genome, search_string):\n \"\"\" \n Function for searching basic gene table currently: Ensembl (EnsGene table from UCSC genome browser)\n \"\"\"\n logger.debug(\"annotation_server.search_genes called for genome: %s search: %s\" % (genome, search_string)) \n \n if genome in SUPPORTED_GENOMES:\n current_table = eval(genome+ \"_EnsGene\")\n curr_vals = current_table.objects.filter(\n Q(name__contains=search_string) | Q(name2__contains=search_string)\n ).values('name', 'chrom', 'strand', 'txStart', 'txEnd', 'cdsStart', 'cdsEnd', 'exonCount', 'exonStarts', 'exonEnds')\n \n data = ValuesQuerySetToDict(curr_vals)\n return HttpResponse(data, 'application/json')\n else:\n return HttpResponse(status=400)\n \n # Postbio query\n #cursor = connection.cursor() \n #query = \"\"\"Select a.name, a.symbol, a.synonyms,\n #b.region as end, b.chrom FROM (SELECT name, symbol,\n #synonyms from dm3.flybase2004xref where symbol ilike '%s') a JOIN\n #(SELECT f.name, f.region, f.seq_id, s.name as chrom FROM dm3.flybase f JOIN\n #dm3.sequence s ON f.seq_id = s.id where s.name = 'chr2L' OR s.name = 'chr2R'\n #OR s.name = 'chr3L' OR s.name = 'chr3R' OR s.name = 'chr4' OR s.name =\n #'chrX' ) b ON a.name = b.name \"\"\" % (search_string)\n #cursor.execute(query)\n \n return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\ndef search_extended_genes(request, genome, search_string):\n \"\"\" \n Function for searching extended gene tables currently: GenCode (hg19), Flybase (dm3), or Wormbase (ce10)\n \"\"\"\n logger.debug(\"annotation_server.search_extended_genes called for genome: %s search: %s\" % (genome, search_string))\n\n\n # extended genes\n #url(r'^search_genes/(?P[a-zA-Z0-9]+)/(?P[a-zA-Z0-9]+)/$', 'search_genes' ),\n # extended genes\n #url(r'^get_genes/(?P[a-zA-Z0-9]+)/(?P[a-zA-Z0-9]+)/(?P[0-9]+)/(?P[0-9]+)/$', 'get_genes' ),\n \n\ndef get_sequence(request, genome, chrom, start, end):\n \"\"\" \n returns sequence for a specified chromosome start and end\n \"\"\"\n logger.debug(\"annotation_server.get_sequence called for genome: %s chrom: %s\" % (genome, chrom)) \n offset = int(end) - int(start)\n \n # NO SUBSTRING METHOD USING DJANGO ORM\n if genome in SUPPORTED_GENOMES:\n cursor = connection.cursor() \n db_table = 'annotation_server_dm3_sequence'\n query = \"\"\"select name as chrom, substr(seq, %s, %s) as seq from annotation_server_%s_sequence where name = '%s'\"\"\" % (start, offset, genome, chrom)\n cursor.execute(query)\n return HttpResponse(cursor_to_json(cursor), 'application/javascript') \n else:\n return HttpResponse(status=400)\n\n # POSTBIO QUERY\n #cursor = connection.cursor() \n #query = \"\"\"select name as chrom, substr(seq, %s, %s) as seq from %s.sequence where name = '%s'\"\"\" % (start, offset, genome, chrom)\n #cursor.execute(query)\n #return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\ndef get_length(request, genome):\n \"\"\"\n Returns all chromosome lengths depending on genome i.e. dm3, hg18, etc.\n \"\"\"\n logger.debug(\"annotation_server.get_length called for genome: %s\" % (genome)) \n \n if genome in SUPPORTED_GENOMES:\n current_table = eval(genome+ \"_ChromInfo\")\n data = ValuesQuerySetToDict(current_table.objects.values('chrom', 'size'))\n return HttpResponse(data, 'application/json')\n else:\n return HttpResponse(status=400)\n \n # POSTBIO QUERY\n #cursor = connection.cursor() \n #query = \"\"\"SELECT chrom, size from %s.chrominfo where chrom !~* '_' order by size desc\"\"\" % (genome) \n #cursor.execute(query)\n #return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\n\ndef get_chrom_length(request, genome, chrom):\n \"\"\"\n returns the length of a specified chromosome\n \"\"\"\n logger.debug(\"annotation_server.get_chrom_length called for genome: %s chromosome: %s\" % (genome, chrom)) \n \n if genome in SUPPORTED_GENOMES:\n current_table = eval(genome+ \"_ChromInfo\")\n curr_vals = current_table.objects.filter(chrom__iexact=chrom).values('chrom', 'size')\n data = ValuesQuerySetToDict(curr_vals)\n return HttpResponse(data, 'application/json')\n else:\n return HttpResponse(status=400)\n \n # TODO: return genome lengths according to chrom order i.e. 1,2,3 etc. \n #cursor = connection.cursor() \n #if (chrom):\n # query = \"\"\"SELECT chrom, size from %s.chrominfo where chrom ilike '%s'\"\"\" % (genome, chrom)\n #cursor.execute(query)\n #return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\ndef get_cytoband(request, genome, chrom):\n \"\"\"\n returns the length of a specified chromosome\n \"\"\"\n logger.debug(\"annotation_server.get_cytoband called for genome: %s chromosome: %s\" % (genome, chrom)) \n \n if genome in SUPPORTED_GENOMES:\n current_table = eval(genome+ \"_CytoBand\")\n curr_vals = current_table.objects.filter(chrom__iexact=chrom).values('chrom', 'chromStart', 'chromEnd', 'name', 'gieStain')\n data = ValuesQuerySetToDict(curr_vals)\n return HttpResponse(data, 'application/json')\n else:\n return HttpResponse(status=400)\n \n #cursor = connection.cursor() \n #query = \"\"\"SELECT s.name as chrom, #region as end, region_name from dm3.cytobandideo c join dm3.sequence s on s.id = c.seq_id where s.name ilike '%s' order by region;\"\"\" % (chrom)\n #cursor.execute(query)\n #return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\ndef get_genes(request, genome, chrom, start, end):\n \"\"\"\n gets a list of genes within a range i.e. gene start, cds, gene symbol\n \"\"\"\n logger.debug(\"annotation_server.get_genes called for genome: %s chromosome: %s\" % (genome, chrom)) \n \n if genome in SUPPORTED_GENOMES:\n current_table = eval(genome+ \"_EnsGene\")\n curr_vals = current_table.objects.filter(\n Q(chrom__iexact=chrom),\n Q(cdsStart__range=(start, end)) | Q(cdsEnd__range=(start, end))\n ).values('name', 'chrom', 'strand', 'txStart', 'txEnd', 'cdsStart', 'cdsEnd', 'exonCount', 'exonStarts', 'exonEnds')\n data = ValuesQuerySetToDict(curr_vals)\n return HttpResponse(data, 'application/json')\n else:\n return HttpResponse(status=400)\n \n \n # Postbio query\n #cursor = connection.cursor() \n #query = \"\"\"SELECT x.symbol, r.name, #r.region as end, case when r.same_orient then '+' else '-' end as strand, #r.cds as cds_end from dm3.flybase r join dm3.flyBase2004Xref x on r.name = x.name JOIN (select id, name from dm3.sequence where name = '%s') n ON n.id = r.seq_id and region && int_interval '(%s,%s)' order by region\"\"\" % (chrom, start, end)\n #cursor.execute(query) \n #return HttpResponse(cursor_to_json(cursor), 'application/javascript')\n\n'''\ndef get_exons(request, genome, chrom, start, end):\n #Get list of all gene exons within a specified range\n \n print \"annotation_server.get_exons\"\n \n cursor = connection.cursor() \n query = \"\"\"select x.symbol, r.name, s.name as chrom, case when r.same_orient then '+' else '-' end as strand, #r.region as gene_end, #e.region as exonend, exon_id from dm3.flybase r join dm3.sequence s on r.seq_id=s.id join dm3.flybase_exon ie on isoform_id=r.id join dm3.exon e on exon_id=e.id join dm3.flyBase2004Xref x on r.name = x.name where s.name = '%s' and r.region && int_interval'(%s,%s)' order by r.id, # ? AND time < ?\n GROUP BY sensor_id''', [from_time, to_time])\n return measurements\n\ndef timeline(sensor_id):\n \"\"\"\n Returns all measurements for one particular sensor.\n \"\"\"\n timeline = db.dict_query('''SELECT time, temperature FROM measurements\n WHERE sensor_id = ? ORDER BY time''', [sensor_id])\n return timeline\n\n\n\n","sub_path":"examples/i2maps_projects/modules/weather/sensors.py","file_name":"sensors.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"240770417","text":"#!/usr/bin/python\n\nimport re\n\npostcode = str(raw_input('Enter a postcode: '))\n\nm = re.search(r'^[A-Z]{2}[0-9][0-9]?\\s?[0-9][A-Z]{2}$', postcode, re.I)\n\nif m:\n\tprint('{} is valid'.format(m.group(0)))\nelse:\n\tprint('invalid')\n","sub_path":"regex/postcode.py","file_name":"postcode.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"576153974","text":"import time, functools\ndef metric(fn):\n\n # @functools.wraps(fn)\n def wrapper(*args,**kwargs):\n time_start=time.time()\n res=fn(*args,**kwargs)\n time_end=time.time()-time_start\n print('%s usertime %s ms'% (fn.__name__,1000*time_end))\n return res\n return wrapper\n# 测试\n@metric\ndef fast(x, y):\n time.sleep(0.0012)\n return x + y;\n\n@metric\ndef slow(x, y, z):\n time.sleep(0.1234)\n return x * y * z;\n\nf = fast(11, 22)\ns = slow(11, 22, 33)\nprint(f,s)\nif f == 33 and s == 7986:\n print('success!')\nelse:\n print('wrong')\n\nf = fast(22, 44)\ns = slow(22, 44, 66)\n\nprint(fast.__name__)\nprint(slow.__name__)","sub_path":"PythonBasic/DecoratorDemo.py","file_name":"DecoratorDemo.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"370991831","text":"import numpy as np\nfrom multiagent.core import World, Agent, Landmark\nfrom multiagent.scenario import BaseScenario\nimport math\nimport copy\n\nattack_angle = 90\ndefense_angle = 90\nfire_range = 0.3\ncomput_range = 0.6\n\n\nclass Scenario(BaseScenario):\n def make_world(self):\n print(\"**********liyuan scenario*************\")\n world = World()\n # set any world properties first\n world.dim_c = 2\n num_good_agents = 3\n num_adversaries = 3\n num_agents = num_adversaries + num_good_agents\n num_landmarks = 0\n\n self.num_green = copy.deepcopy(num_good_agents)\n self.num_red = copy.deepcopy(num_adversaries)\n # add agents\n world.agents = [Agent() for i in range(num_agents)]\n for i, agent in enumerate(world.agents):\n agent.name = 'agent %d' % i\n agent.collide = True\n agent.silent = True\n agent.adversary = True if i < num_adversaries else False\n agent.size = 0.04 if agent.adversary else 0.04\n #agent.accel = 3.0 if agent.adversary else 2.0\n agent.accel = 2.0 if agent.adversary else 2.0\n #agent.accel = 20.0 if agent.adversary else 25.0\n #agent.max_speed = 1.5 if agent.adversary else 1.2\n agent.max_speed = 1.0 if agent.adversary else 1.0\n #agent.max_speed = 1.0 if agent.adversary else 0.3 ###changed by liyuan\n agent.death = False\n\n agent.chi = np.array([0.1,0])\n\n if agent.adversary:\n agent.lock_num=[0 for j in range(num_good_agents)]\n else:\n agent.lock_num=[0 for j in range(num_adversaries)]\n # add landmarks\n world.landmarks = [Landmark() for i in range(num_landmarks)]\n for i, landmark in enumerate(world.landmarks):\n landmark.name = 'landmark %d' % i\n landmark.collide = True\n landmark.movable = False\n landmark.size = 0.2\n landmark.boundary = False\n # make initial conditions\n self.reset_world(world)\n return world\n\n\n def reset_world(self, world):\n # random properties for agents\n for i, agent in enumerate(world.agents):\n agent.color = np.array([0.35, 0.85, 0.35]) if not agent.adversary else np.array([0.85, 0.35, 0.35])\n # random properties for landmarks\n for i, landmark in enumerate(world.landmarks):\n landmark.color = np.array([0.25, 0.25, 0.25])\n # set random initial states\n for agent in world.agents:\n agent.state.p_pos = np.random.uniform(-1, +1, world.dim_p)\n agent.state.p_vel = np.zeros(world.dim_p)\n agent.state.c = np.zeros(world.dim_c)\n agent.death = False\n for i, landmark in enumerate(world.landmarks):\n if not landmark.boundary:\n landmark.state.p_pos = np.random.uniform(-0.9, +0.9, world.dim_p)\n landmark.state.p_vel = np.zeros(world.dim_p)\n \n if agent.adversary:\n agent.lock_num=[0 for j in range(self.num_green)]\n else:\n agent.lock_num=[0 for j in range(self.num_red)]\n\n\n def benchmark_data(self, agent, world):\n # returns data for benchmarking purposes\n if agent.adversary:\n collisions = 0\n for a in self.good_agents(world):\n if self.is_collision(a, agent) and a.death == False:\n collisions += 1\n return collisions\n else:\n return 0\n\n '''\n def is_collision(self, agent1, agent2):\n if agent1.death or agent2.death:\n return False\n delta_pos = agent1.state.p_pos - agent2.state.p_pos\n dist = np.sqrt(np.sum(np.square(delta_pos)))\n #dist_min = agent1.size + agent2.size\n dist_min = 0.1\n return True if dist < dist_min else False\n '''\n\n ##liyuan: compute the number of lokcing number of the agent\n def compute_lock_num(self, agent, world):\n opponent = []\n if agent.adversary:\n opponent = self.good_agents(world)\n else:\n opponent = self.adversaries(world)\n \n for i, opp in enumerate(opponent):\n if self.is_collision(opp,agent):\n agent.lock_num[i] += 1\n else:\n agent.lock_num[i] = 0\n \n ###liyuan: True if agent1 win, False for others\n def is_collision(self, agent1, agent2):\n if agent1.death or agent2.death:\n return False\n\n ###liyuan:judged by angle\n delta_pos = agent2.state.p_pos - agent1.state.p_pos\n distance = np.sqrt(np.sum(np.square(delta_pos)))\n if distance <= 1e-5:\n return False\n \n agent1_chi = [agent1.state.p_vel[0],agent1.state.p_vel[1]]\n\n if abs(agent1.state.p_vel[0]) < 1e-5 and abs(agent1.state.p_vel[1])<1e-5:\n agent1_chi[0] = 0.1\n agent1_chi[1] = 0\n agent2_chi = [agent2.state.p_vel[0],agent2.state.p_vel[1]]\n\n if abs(agent2.state.p_vel[0]) < 1e-5 and abs(agent2.state.p_vel[1])<1e-5:\n agent2_chi[0] = 0.1\n agent2_chi[1] = 0\n\n agent1_chi_value = np.sqrt(np.sum(np.square(agent1_chi)))\n agent1_cross = (delta_pos[0]*agent1_chi[0]+delta_pos[1]*agent1_chi[1])/(distance*agent1_chi_value)\n if agent1_cross < -1:\n agent1_cross = -1\n if agent1_cross > 1:\n agent1_cross = 1\n agent1_angle = math.acos(agent1_cross)\n\n\n agent2_chi_value = np.sqrt(np.sum(np.square(agent2_chi)))\n agent2_cross = (-delta_pos[0]*agent2_chi[0]-delta_pos[1]*agent2_chi[1])/(distance*agent2_chi_value)\n if agent2_cross < -1:\n agent2_cross = -1\n if agent2_cross > 1:\n agent2_cross = 1\n agent2_angle = math.acos(agent2_cross)\n\n revised_defense = 180-defense_angle/2\n if distance < fire_range and agent2_angle*180/math.pi>revised_defense and agent1_angle*180/math.pirevised_defense:\n #return True,2\n #else:\n return False\n \n ###liyuan: True if agent1 win, False for others\n def will_hit(self, agent1, agent2,hit_range):\n if agent1.death or agent2.death:\n return False\n\n ###liyuan:judged by angle\n delta_pos = agent2.state.p_pos - agent1.state.p_pos\n distance = np.sqrt(np.sum(np.square(delta_pos)))\n if distance <= 1e-5:\n return False\n \n agent1_chi = [agent1.state.p_vel[0],agent1.state.p_vel[1]]\n\n if abs(agent1.state.p_vel[0]) < 1e-5 and abs(agent1.state.p_vel[1])<1e-5:\n agent1_chi[0] = 0.1\n agent1_chi[1] = 0\n agent2_chi = [agent2.state.p_vel[0],agent2.state.p_vel[1]]\n\n if abs(agent2.state.p_vel[0]) < 1e-5 and abs(agent2.state.p_vel[1])<1e-5:\n agent2_chi[0] = 0.1\n agent2_chi[1] = 0\n\n agent1_chi_value = np.sqrt(np.sum(np.square(agent1_chi)))\n agent1_cross = (delta_pos[0]*agent1_chi[0]+delta_pos[1]*agent1_chi[1])/(distance*agent1_chi_value)\n if agent1_cross < -1:\n agent1_cross = -1\n if agent1_cross > 1:\n agent1_cross = 1\n agent1_angle = math.acos(agent1_cross)\n\n\n agent2_chi_value = np.sqrt(np.sum(np.square(agent2_chi)))\n agent2_cross = (-delta_pos[0]*agent2_chi[0]-delta_pos[1]*agent2_chi[1])/(distance*agent2_chi_value)\n if agent2_cross < -1:\n agent2_cross = -1\n if agent2_cross > 1:\n agent2_cross = 1\n agent2_angle = math.acos(agent2_cross)\n\n revised_defense = 180-defense_angle/2\n if distance < hit_range and agent2_angle*180/math.pi>revised_defense and agent1_angle*180/math.pirevised_defense:\n #return True,2\n #else:\n return False\n\n # return all agents that are not adversaries\n def good_agents(self, world):\n return [agent for agent in world.agents if not agent.adversary]\n\n # return all adversarial agents\n def adversaries(self, world):\n return [agent for agent in world.agents if agent.adversary]\n\n\n def reward(self, agent, world):\n # Agents are rewarded based on minimum agent distance to each landmark\n main_reward = self.adversary_reward(agent, world) if agent.adversary else self.agent_reward(agent, world)\n return main_reward\n\n def agent_reward(self, agent, world):\n ####added by liyuan\n if agent.death == True:\n return 0\n # Agents are negatively rewarded if caught by adversaries\n rew = 0\n #shape = False\n shape = True\n adversaries = self.adversaries(world)\n '''\n if shape: # reward can optionally be shaped (increased reward for increased distance from adversary)\n for adv in adversaries:\n ###changed by liyuan\n if adv.death == True:\n continue\n rew += 0.1 * np.sqrt(np.sum(np.square(agent.state.p_pos - adv.state.p_pos)))\n '''\n self.compute_lock_num(agent, world)\n if agent.collide:\n for i,a in enumerate(adversaries):\n ###changed by liyuan\n if self.is_collision(a, agent) and a.death == False:\n #if agent.lock_num[i]>=3 and a.death == False:\n #rew -= 10\n agent.death = True\n \n\n # agents are penalized for exiting the screen, so that they can be caught by the adversaries\n def bound(x):\n if x < 0.9:\n return 0\n if x < 1.0:\n return (x - 0.9) * 10\n return min(np.exp(2 * x - 2), 10)\n for p in range(world.dim_p):\n x = abs(agent.state.p_pos[p])\n rew -= bound(x)\n \n for p in range(world.dim_p):\n x = abs(agent.state.p_pos[p])\n if (x > 1.0):\n rew -= 20\n break\n\n return rew\n\n def adversary_reward(self, agent, world):\n ####added by liyuan\n if agent.death == True:\n return 0\n # Adversaries are rewarded for collisions with agents\n rew = 0\n #shape = False\n shape = True\n agents = self.good_agents(world)\n adversaries = self.adversaries(world)\n \n \n '''\n if shape: # reward can optionally be shaped (decreased reward for increased distance from agents)\n for adv in adversaries:\n ###rew -= 0.1 * min([np.sqrt(np.sum(np.square(a.state.p_pos - adv.state.p_pos))) for a in agents])\n if adv.death == False:\n dis = []\n for a in agents:\n if a.death == False:\n dis.append(np.sqrt(np.sum(np.square(a.state.p_pos - adv.state.p_pos))))\n if len(dis) > 0:\n rew -= 0.1 * min(dis)\n '''\n \n if shape: \n dis = []\n for a in agents:\n if a.death == False:\n dis.append(np.sqrt(np.sum(np.square(a.state.p_pos - agent.state.p_pos))))\n if len(dis) > 0:\n rew -= 0.1*min(dis)\n \n '''\n eat_num = 0\n by_eat_num = 0\n \n for a in agents:\n if self.will_hit(agent,a,comput_range):\n eat_num=eat_num+1\n elif self.will_hit(a,agent,comput_range):\n by_eat_num=by_eat_num+1\n rew += 0.1*(eat_num-by_eat_num)\n '''\n \n \n self.compute_lock_num(agent, world)\n if agent.collide:\n for ag in agents:\n for i,adv in enumerate(adversaries):\n ###changed by liyuan\n if self.is_collision(adv,ag) and ag.death == False and adv.death == False:\n #if self.ag.lock_num[i]>=3 and ag.death == False and adv.death == False:\n if adv is agent:\n rew += 4\n else:\n rew += 2\n break\n \n if agent.collide:\n for ag in agents:\n for i,adv in enumerate(adversaries):\n if self.is_collision(ag,adv) and ag.death == False and adv.death == False:\n #if self.ag.lock_num[i]>=3 and ag.death == False and adv.death == False:\n if not (adv is agent):\n rew -= 2\n \n ###if the red agent is eatten\n if agent.collide:\n for i,ag in enumerate(agents):\n if self.is_collision(ag, agent) and ag.death == False:\n #if ag.death == False and agent.lock_num[i]>=3:\n agent.death = True\n rew -= 4 \n break\n \n for adv in adversaries:\n if adv.death == False:\n exceed = False\n for p in range(world.dim_p):\n x = abs(adv.state.p_pos[p])\n if (x > 1.0):\n exceed = True\n break\n if exceed == True:\n if adv is agent:\n rew -= 10\n else:\n rew -=0\n break\n\n return rew\n\n def observation(self, agent, world):\n # get positions of all entities in this agent's reference frame\n entity_pos = []\n for entity in world.landmarks:\n if not entity.boundary:\n entity_pos.append(entity.state.p_pos - agent.state.p_pos)\n # communication of all other agents\n comm = []\n other_pos = []\n other_vel = []\n other_chi = []\n our_chi = []\n\n my_chi = np.zeros(1)\n if abs(agent.state.p_vel[0])<1e-5 and abs(agent.state.p_vel[1])<1e-5:\n my_chi[0] = 0\n else:\n my_chi[0] = math.atan2(agent.state.p_vel[1],agent.state.p_vel[0])\n our_chi.append(my_chi)\n\n temp_agents=[]\n for agent_i in world.agents:\n if agent_i.adversary == agent.adversary:\n temp_agents.append(agent_i)\n for agent_i in world.agents:\n if agent_i.adversary != agent.adversary:\n temp_agents.append(agent_i)\n\n for other in temp_agents:\n if other is agent: continue \n ###changed by liyuan\n if other.death:\n comm.append(np.zeros(world.dim_c))\n other_pos.append(np.zeros(world.dim_p))\n other_vel.append(np.zeros(world.dim_p))\n tmp_chi = np.zeros(1)\n other_chi.append(tmp_chi)\n else:\n comm.append(other.state.c)\n other_pos.append(other.state.p_pos - agent.state.p_pos)\n #if not other.adversary:\n other_vel.append(other.state.p_vel)\n\n tmp_chi = np.zeros(1)\n if abs(other.state.p_vel[0])<1e-5 and abs(other.state.p_vel[1])<1e-5:\n tmp_chi[0] = 0\n else:\n tmp_chi[0] = math.atan2(other.state.p_vel[1],other.state.p_vel[0])\n other_chi.append(tmp_chi)\n\n action_number=[np.zeros(5)]\n\n #comm.append(other.state.c)\n #other_pos.append(other.state.p_pos - agent.state.p_pos)\n #if not other.adversary:\n #other_vel.append(other.state.p_vel)\n return np.concatenate([agent.state.p_vel] + [agent.state.p_pos] + entity_pos + other_pos + other_vel + our_chi + other_chi+action_number)\n\n ##added by liyuan: if all green nodes die, this epsoid is over.\n def done(self, agent, world):\n allDie = True\n agents = self.good_agents(world)\n for agent in agents:\n if agent.death == False:\n allDie = False\n break\n return allDie\n","sub_path":"MADDPG/multiagent-particle-envs/multiagent/scenarios/competition_3v3.py","file_name":"competition_3v3.py","file_ext":"py","file_size_in_byte":16279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"547242637","text":"import json\n\nfrom django.shortcuts import get_object_or_404\nfrom django.http import HttpResponseRedirect, HttpResponseNotFound\nfrom django.views.generic import View\n\nfrom django.contrib.auth import authenticate, login\n\nfrom .models import LOT\n\n\nclass LOTLogin(View):\n def get(self, request, uuid):\n next_url = request.GET.get('next', '/')\n lot = get_object_or_404(LOT, uuid=uuid)\n if not lot.verify():\n lot.delete()\n return HttpResponseNotFound()\n\n user = authenticate(lot_uuid=uuid)\n login(request, user)\n\n try:\n session_data = json.loads(lot.session_data)\n request.session.update(session_data)\n except Exception:\n # If not correctly serialized not set the session_data\n pass\n\n if lot.is_one_time():\n lot.delete()\n\n return HttpResponseRedirect(next_url)\n","sub_path":"lot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"372523344","text":"\"\"\"The tests for the Google Actions component.\"\"\"\n# pylint: disable=protected-access\nimport asyncio\n\nfrom homeassistant import const\nfrom homeassistant.components import climate\nfrom homeassistant.components import google_assistant as ga\nfrom homeassistant.util.unit_system import (IMPERIAL_SYSTEM, METRIC_SYSTEM)\n\nDETERMINE_SERVICE_TESTS = [{ # Test light brightness\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_BRIGHTNESS,\n 'params': {\n 'brightness': 95\n },\n 'expected': (\n const.SERVICE_TURN_ON,\n {'entity_id': 'light.test', 'brightness': 242}\n )\n}, { # Test light color temperature\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_COLOR,\n 'params': {\n 'color': {\n 'temperature': 2300,\n 'name': 'warm white'\n }\n },\n 'expected': (\n const.SERVICE_TURN_ON,\n {'entity_id': 'light.test', 'kelvin': 2300}\n )\n}, { # Test light color blue\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_COLOR,\n 'params': {\n 'color': {\n 'spectrumRGB': 255,\n 'name': 'blue'\n }\n },\n 'expected': (\n const.SERVICE_TURN_ON,\n {'entity_id': 'light.test', 'rgb_color': [0, 0, 255]}\n )\n}, { # Test light color yellow\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_COLOR,\n 'params': {\n 'color': {\n 'spectrumRGB': 16776960,\n 'name': 'yellow'\n }\n },\n 'expected': (\n const.SERVICE_TURN_ON,\n {'entity_id': 'light.test', 'rgb_color': [255, 255, 0]}\n )\n}, { # Test unhandled action/service\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_COLOR,\n 'params': {\n 'color': {\n 'unhandled': 2300\n }\n },\n 'expected': (\n None,\n {'entity_id': 'light.test'}\n )\n}, { # Test switch to light custom type\n 'entity_id': 'switch.decorative_lights',\n 'command': ga.const.COMMAND_ONOFF,\n 'params': {\n 'on': True\n },\n 'expected': (\n const.SERVICE_TURN_ON,\n {'entity_id': 'switch.decorative_lights'}\n )\n}, { # Test light on / off\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_ONOFF,\n 'params': {\n 'on': False\n },\n 'expected': (const.SERVICE_TURN_OFF, {'entity_id': 'light.test'})\n}, {\n 'entity_id': 'light.test',\n 'command': ga.const.COMMAND_ONOFF,\n 'params': {\n 'on': True\n },\n 'expected': (const.SERVICE_TURN_ON, {'entity_id': 'light.test'})\n}, { # Test Cover open close\n 'entity_id': 'cover.bedroom',\n 'command': ga.const.COMMAND_ONOFF,\n 'params': {\n 'on': True\n },\n 'expected': (const.SERVICE_OPEN_COVER, {'entity_id': 'cover.bedroom'}),\n}, {\n 'entity_id': 'cover.bedroom',\n 'command': ga.const.COMMAND_ONOFF,\n 'params': {\n 'on': False\n },\n 'expected': (const.SERVICE_CLOSE_COVER, {'entity_id': 'cover.bedroom'}),\n}, { # Test cover position\n 'entity_id': 'cover.bedroom',\n 'command': ga.const.COMMAND_BRIGHTNESS,\n 'params': {\n 'brightness': 50\n },\n 'expected': (\n const.SERVICE_SET_COVER_POSITION,\n {'entity_id': 'cover.bedroom', 'position': 50}\n ),\n}, { # Test media_player volume\n 'entity_id': 'media_player.living_room',\n 'command': ga.const.COMMAND_BRIGHTNESS,\n 'params': {\n 'brightness': 30\n },\n 'expected': (\n const.SERVICE_VOLUME_SET,\n {'entity_id': 'media_player.living_room', 'volume_level': 0.3}\n ),\n}, { # Test climate temperature\n 'entity_id': 'climate.living_room',\n 'command': ga.const.COMMAND_THERMOSTAT_TEMPERATURE_SETPOINT,\n 'params': {'thermostatTemperatureSetpoint': 24.5},\n 'expected': (\n climate.SERVICE_SET_TEMPERATURE,\n {'entity_id': 'climate.living_room', 'temperature': 24.5}\n ),\n}, { # Test climate temperature Fahrenheit\n 'entity_id': 'climate.living_room',\n 'command': ga.const.COMMAND_THERMOSTAT_TEMPERATURE_SETPOINT,\n 'params': {'thermostatTemperatureSetpoint': 24.5},\n 'units': IMPERIAL_SYSTEM,\n 'expected': (\n climate.SERVICE_SET_TEMPERATURE,\n {'entity_id': 'climate.living_room', 'temperature': 76.1}\n ),\n}, { # Test climate temperature range\n 'entity_id': 'climate.living_room',\n 'command': ga.const.COMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE,\n 'params': {\n 'thermostatTemperatureSetpointHigh': 24.5,\n 'thermostatTemperatureSetpointLow': 20.5,\n },\n 'expected': (\n climate.SERVICE_SET_TEMPERATURE,\n {'entity_id': 'climate.living_room',\n 'target_temp_high': 24.5, 'target_temp_low': 20.5}\n ),\n}, { # Test climate temperature range Fahrenheit\n 'entity_id': 'climate.living_room',\n 'command': ga.const.COMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE,\n 'params': {\n 'thermostatTemperatureSetpointHigh': 24.5,\n 'thermostatTemperatureSetpointLow': 20.5,\n },\n 'units': IMPERIAL_SYSTEM,\n 'expected': (\n climate.SERVICE_SET_TEMPERATURE,\n {'entity_id': 'climate.living_room',\n 'target_temp_high': 76.1, 'target_temp_low': 68.9}\n ),\n}, { # Test climate operation mode\n 'entity_id': 'climate.living_room',\n 'command': ga.const.COMMAND_THERMOSTAT_SET_MODE,\n 'params': {'thermostatMode': 'heat'},\n 'expected': (\n climate.SERVICE_SET_OPERATION_MODE,\n {'entity_id': 'climate.living_room', 'operation_mode': 'heat'}\n ),\n}]\n\n\n@asyncio.coroutine\ndef test_determine_service():\n \"\"\"Test all branches of determine service.\"\"\"\n for test in DETERMINE_SERVICE_TESTS:\n result = ga.smart_home.determine_service(\n test['entity_id'],\n test['command'],\n test['params'],\n test.get('units', METRIC_SYSTEM))\n assert result == test['expected']\n","sub_path":"tests/components/google_assistant/test_smart_home.py","file_name":"test_smart_home.py","file_ext":"py","file_size_in_byte":5823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"191784933","text":"\"\"\"Remove obsolete indexes\n\nRevision ID: 20cd91cbf353\nRevises: 1ddbb9093570\nCreate Date: 2016-07-04 14:14:37.873240\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '20cd91cbf353'\ndown_revision = '1ddbb9093570'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n op.drop_index('journey_sections_type_idx', schema='stat')\n op.drop_index('requests_user_name_idx', schema='stat')\n op.drop_index('requests_api_idx', schema='stat')\n\n\ndef downgrade():\n op.create_index('journey_sections_type_idx', 'journey_sections', ['type'], schema='stat')\n op.create_index('requests_user_name_idx', 'requests', ['user_name'], schema='stat')\n op.create_index('requests_api_idx', 'requests', ['api'], schema='stat')\n","sub_path":"migrations/alembic/versions/20cd91cbf353_remove_obsolete_indexes.py","file_name":"20cd91cbf353_remove_obsolete_indexes.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"265127232","text":"from stellar_sdk import Asset\nfrom stellar_sdk.call_builder.call_builder_sync import OrderbookCallBuilder\nfrom tests.call_builder.call_builder_sync import client, horizon_url\n\n\nclass TestOrderbookCallBuilder:\n def test_init(self):\n selling = Asset(\n \"BTC\", \"GATEMHCCKCY67ZUCKTROYN24ZYT5GK4EQZ65JJLDHKHRUZI3EUEKMTCH\"\n )\n buying = Asset.native()\n builder = OrderbookCallBuilder(horizon_url, client, selling, buying)\n assert builder.endpoint == \"order_book\"\n assert builder.params == {\n \"selling_asset_type\": selling.type,\n \"selling_asset_code\": selling.code,\n \"selling_asset_issuer\": selling.issuer,\n \"buying_asset_type\": buying.type,\n }\n","sub_path":"tests/call_builder/call_builder_sync/test_orderbook_call_builder.py","file_name":"test_orderbook_call_builder.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"435821155","text":"\"\"\"\nGiven preorder and inorder traversal of a tree, construct the binary tree.\n Note: You may assume that duplicates do not exist in the tree.\nExample :\nInput :\n Preorder : [1, 2, 3]\n Inorder : [2, 1, 3]\nReturn :\n 1\n / \\\n 2 3\n\"\"\"\n\n\n# Focus on the preorder traversal to begin with.\n# The first element in the traversal will definitely be the root.\n# Based on this information, can you identify the elements in the left subtree and right subtree ?\n# ( Hint : Focus on inorder traversal and root information )\n# Once you do that, your problem has now been reduced to a smaller set. Now you have the inorder and preorder\n# traversal for the left and right subtree and you need to figure them out.\n# Divide and conquer.\n# Bonus points if you can do it without recursion.\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n # @param A : list of integers\n # @param B : list of integers\n # @return the root node in the tree\n def buildTree(self, A, B):\n if not B:\n return None\n root_pos = B.index(A[0])\n new_node = TreeNode(A[0])\n new_node.left = self.buildTree(A[1:root_pos + 1], B[:root_pos])\n new_node.right = self.buildTree(A[root_pos + 1:], B[root_pos + 1:])\n return new_node\n","sub_path":"InterviewBits/tree/binary-tree-from-inorder-and-preorder.py","file_name":"binary-tree-from-inorder-and-preorder.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"132695360","text":"#\n# Copyright 2017 by Delphix\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\n\"\"\"\nPackage \"database.performanceHistory\"\n\"\"\"\nAPI_VERSION = \"1.9.0\"\n\nimport urllib\nfrom delphixpy.v1_9_0 import response_validator\n\ndef get_all(engine, sampling_interval=None, to_date=None, from_date=None):\n \"\"\"\n Reports the utilization of all containers during a particular period of\n time.\n\n :param engine: The Delphix Engine\n :type engine: :py:class:`delphixpy.v1_9_0.delphix_engine.DelphixEngine`\n :param sampling_interval: The interval at which data is to be sampled,\n measured in seconds.\n :type sampling_interval: ``float``\n :param to_date: The latest date for which container utilization statistics\n will be reported.\n :type to_date: ``basestring``\n :param from_date: The earliest date for which container utilization\n statistics will be reported.\n :type from_date: ``basestring``\n :rtype: ``list`` of :py:class:`v1_9_0.web.vo.ContainerUtilization`\n \"\"\"\n assert API_VERSION == engine.API_VERSION, \"Wrong API version (%s) for parameter 'engine' (%s)\" % (API_VERSION, engine.API_VERSION)\n url = \"/resources/json/delphix/database/performanceHistory\"\n query_params = {\"samplingInterval\": sampling_interval, \"toDate\": to_date, \"fromDate\": from_date}\n query_dct = {k: query_params[k] for k in query_params if query_params[k] is not None}\n if query_dct:\n url_params = urllib.urlencode(query_dct)\n url += \"?%s\" % url_params\n response = engine.get(url)\n result = response_validator.validate(response, engine)\n raw_result = getattr(engine, 'raw_result', False)\n return response_validator.parse_result(result, undef_enabled=True, return_types=[u'ContainerUtilization'], returns_list=True, raw_result=raw_result)\n\n","sub_path":"src/main/resources/delphixpy/v1_9_0/web/database/performanceHistory/performanceHistory.py","file_name":"performanceHistory.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"583465116","text":"# -*- coding: utf-8 -*-\n\n__author__ = 'CCCYT'\n\n\"\"\"\ncustomer\n\"\"\"\nimport logging\nimport random\nimport time\n\nimport function as func\nfrom DataBase import DataBase\n\n# 创建logger\nlogger = logging.getLogger()\n\n\nclass Customer(object):\n\n def __init__(self, be_id='', birthday='', pesel='', id_number='', gender='1', email='cheng.yutao@plus.pl',\n first_name='BES', last_name='CCCYT',\n mobile_phone='609067758'):\n self.__beId = be_id\n self.__birthday, self.__pesel = (birthday, pesel) if (\n birthday != '' and pesel != '') else Customer.generate_pesel()\n\n self.__id_number = id_number if id_number != '' else Customer.generate_id_number()\n self.__gender = gender\n self.__email = email\n self.__firstName = first_name\n self.__lastName = last_name\n self.__mobilePhone = mobile_phone\n\n @classmethod\n def generate_pesel(cls, birthday=None):\n def generate_birthday():\n # 在开始时间和结尾时间中随机选择一个日期作为客户的出生时间\n # 开始时间元组\n start = (1980, 1, 1, 0, 0, 0, 0, 0, 0)\n # 开始时间戳\n start_timestamp = time.mktime(start)\n # 结尾时间元组\n end = (2010, 12, 31, 23, 59, 59, 0, 0, 0)\n # 结尾时间戳\n end_timestamp = time.mktime(end)\n timestamp = random.randint(start_timestamp, end_timestamp)\n return time.strftime(\"%Y%m%d\", time.localtime(timestamp))\n\n def cal_pesel(birthday_date):\n # 生成三位随机数\n zzz = func.add_leading_zero(random.randint(0, 999), 3)\n # 性别,奇数是男，偶数是女\n x = str(random.randint(0, 9))\n temp_pesel = birthday_date[2:] + zzz + x\n # 数字元组，用以乘以pesel，生成校验码\n code = (1, 3, 7, 9, 1, 3, 7, 9, 1, 3)\n check_sum = 0\n i = 0\n while i < 10:\n check_sum += code[i] * int(temp_pesel[i])\n i += 1\n # 校验和求模10， 大于0 则被10 减 ， 等于0 则为0\n check_num = (10 - check_sum % 10) if (check_sum % 10 > 0) else 0\n return temp_pesel + str(check_num)\n\n # 客户生日\n birthday = birthday if birthday is not None else generate_birthday()\n pesel = cal_pesel(birthday)\n while DataBase().query_customer_by_pesel(pesel) is not None:\n pesel = cal_pesel(birthday)\n\n return birthday, pesel\n\n @classmethod\n def generate_id_number(cls):\n\n # 26个字母\n letter = ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R',\n 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z')\n # 字母对于的值\n letter_value = {\"A\": 10, \"B\": 11, \"C\": 12, \"D\": 13, \"E\": 14, \"F\": 15, \"G\": 16, \"H\": 17, \"I\": 18, \"J\": 19,\n \"K\": 20, \"L\": 21, \"M\": 22, \"N\": 23, \"O\": 24, \"P\": 25, \"Q\": 26, \"R\": 27, \"S\": 28, \"T\": 29,\n \"U\": 30, \"V\": 31, \"W\": 32, \"X\": 33, \"Y\": 34, \"Z\": 35}\n # 用以乘以字母和数字部分生成校验位\n code = (7, 3, 1, 7, 3, 1, 7, 3)\n\n def cal_id_number():\n # 从26个字母中随机取三位,z作为字母部分\n letter_part = letter[random.randint(0, 25)] + letter[random.randint(0, 25)] + letter[random.randint(0, 25)]\n # 随机取五位以内的数， 不足五位，前面补0， 作为数字部分\n digit_part = func.add_leading_zero(random.randint(0, 99999), 5)\n check_sum = 0\n i = 0\n while i < 8:\n if i < 3:\n check_sum += code[i] * letter_value[letter_part[i]]\n else:\n check_sum += code[i] * int(digit_part[i - 3])\n i += 1\n check_num = str(check_sum % 10)\n return letter_part + check_num + digit_part\n\n id_number = cal_id_number()\n while DataBase().query_customer_by_id_number(id_number) is not None:\n id_number = cal_id_number()\n return id_number\n\n def get_customer(self):\n return {'beid': self.__beId, 'birthday': self.__birthday, 'pesel': self.__pesel, 'idNumber': self.__id_number,\n 'gender': self.__gender, 'email': self.__email, 'firstName': self.__firstName,\n 'lastName': self.__lastName, 'mobilePhone': self.__mobilePhone}\n\n def set_customer(self, customer_code):\n self.__customer_code = customer_code\n\n\nif __name__ == '__main__':\n print(Customer.generate_pesel())\n print(Customer.generate_id_number())\n","sub_path":"BESTool/bestool/Customer.py","file_name":"Customer.py","file_ext":"py","file_size_in_byte":4705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"442545588","text":"from django.contrib import admin\nfrom django.forms.models import modelform_factory\n\nfrom environment.models import Environment\n\nfrom models import Building, Tenure\n\n\nclass EnvironmentInline(admin.TabularInline):\n model = Environment\n\n\nclass TenureInline(admin.TabularInline):\n model = Tenure\n extra = 1\n\n\nclass BuildingAdmin(admin.ModelAdmin):\n inlines = [TenureInline]\n form = modelform_factory(Building, exclude=())\n list_per_page = 30\n\n class Media:\n js = (\n 'js/ubigeo.js',\n )\n\nadmin.site.register(Building, BuildingAdmin)\n","sub_path":"building/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"157064536","text":"from image import ImageCaptcha\nimport random\nimport string\ndef run():\n seed = \"1234567890abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n sa = []\n for i in range(8):\n sa.append(random.choice(seed))\n salt = ''.join(sa)\n#print salt\n img = ImageCaptcha()\n img1 = img.generate_image(salt)\n img1.save(\"captcha.jpg\")\n# with open(\"captcha.jpg\",'wb') as f:\n# f.write(data)\nif __name__ == '__main__':\n run()\n","sub_path":"flask/utils/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"85071262","text":"from Model.Data import *\nimport Utils.Storage as Storage\n\ncomment_handler = CommentedTreeBuilder()\n\nparser = ET.XMLParser(target=comment_handler)\n\nwith open('test_aimls/utils.aiml', 'r') as f:\n tree = ET.parse(f, parser)\n\nroot = tree.getroot()\nprint(root.tag)\n\nfor child in root:\n print(\"child.tag: {}\".format(child.tag))\n print(\"child.text: {}\".format(child.text))\n # print(\"child.tags: {}\".format(child.tags))\n tag_obj = Storage.decode_tag(child.tag.lower())\n print(tag_obj)\n raise exception\n# ET.dump(tree)","sub_path":"Examples/writeout_comments.py","file_name":"writeout_comments.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"129537490","text":"import utils.sys.config\nimport pymongo\n\nfrom utils.db.mongodb.cursor_result import CursorResult\nfrom utils.gadget.general import SysUtils\nfrom utils.const.file_source import FileSource\nfrom utils.const.file_type import FileType\nfrom utils.const.pack_type import PackType\n\n\n# 固件包记录集合\npack_files_coll = utils.sys.config.g_firmware_db_full[\"pack_files\"]\n\n\nclass PackFileDO:\n\n # def __init__(self, pack_id=None):\n # # pack_id 为None时表示新建的pack文件对象\n # pass\n\n @staticmethod\n def save(pack_id, file_id, name=None, description='', pack_type=PackType.REAL,\n source_type=FileSource.REMOTE_DOWNLOAD, file_type=FileType.OTHER_FILE, source_addr=''):\n doc = {'pack_id': pack_id, 'file_id': file_id, 'name': name, 'description': description,\n 'pack_type': pack_type, 'source_type': source_type, 'file_type': file_type, 'source_addr': source_addr,\n 'create_time': SysUtils.get_now_time()}\n # 更新一条函数分析结果，如果没有旧记录，则创建一条新记录\n pack_files_coll.update_one({'pack_id': pack_id}, {'$set': doc}, True)\n\n @staticmethod\n def save_manufacturer(pack_id, manufacturer, model, version):\n doc = {'pack_id': pack_id, 'manufacturer': manufacturer, 'model': model, 'version': version}\n # 更新一条函数分析结果，如果没有旧记录，则创建一条新记录\n pack_files_coll.update_one({'pack_id': pack_id}, {'$set': doc}, True)\n\n @staticmethod\n def savefs(pack_id, filesystem, arch=None):\n doc = {'filesystem': filesystem, 'arch': arch}\n # 更新一条函数分析结果，如果没有旧记录，则创建一条新记录\n pack_files_coll.update_one({'pack_id': pack_id}, {'$set': doc}, True)\n\n @staticmethod\n def updateArch(pack_id, arch):\n doc = {'arch': arch}\n # 更新一条函数分析结果，如果没有旧记录，则创建一条新记录\n pack_files_coll.update_one({'pack_id': pack_id}, {'$set': doc}, True)\n\n @staticmethod\n def analyze_complet(pack_id, flag):\n doc = {'analyze': flag}\n # 更新一条函数分析结果，如果没有旧记录，则创建一条新记录\n pack_files_coll.update_one({'pack_id': pack_id}, {'$set': doc}, True)\n\n @staticmethod\n def fetch_pack(pack_id):\n cursor = pack_files_coll.find({'pack_id': pack_id}, {'_id': 0})\n return CursorResult.one(cursor)\n\n @staticmethod\n def all_packs():\n # cursor = pack_files_coll.find({}, {'_id': 0})\n cursor = pack_files_coll.find({}, {'_id': 0}).sort([(\"_id\", pymongo.DESCENDING)])\n\n return CursorResult.many(cursor)\n\n @staticmethod\n def all_packs_type(pack_type):\n cursor = pack_files_coll.find({'pack_type': pack_type}, {'_id': 0})\n return CursorResult.many(cursor)\n\n @staticmethod\n def delete(pack_id):\n result = pack_files_coll.delete_one({'pack_id': pack_id})\n return result.deleted_count == 1\n\n @staticmethod\n def delete_many(pack_id_list):\n for pack_id in pack_id_list:\n PackFileDO.delete(pack_id)\n","sub_path":"utils/db/mongodb/pack_file.py","file_name":"pack_file.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"184166713","text":"import os\nimport pickle\nimport re\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom src.languange_classifier import code_language_classifier as clc\nfrom src.text_based_similarities import Levenshtein_distance\nfrom src.text_based_similarities import cos_similarity\ndata_path = \"../../data/leetcode_cpp\"\n\n\ndef init_tokenizer():\n cpp_data = clc.load_data(data_path, \"cpp\")\n tokenizer = Tokenizer(filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',)\n tokenizer.fit_on_texts(cpp_data)\n # save\n f = open('tokenizer_lcs_cpp.pkl', 'wb')\n pickle.dump(tokenizer, f)\n f.close()\n\n\ndef code2text(path):\n text = open(path, encoding='UTF-8').read()\n text = re.sub(\"(?:/\\\\*(?:[^*]|(?:\\\\*+[^*/]))*\\\\*+/)|(?://.*)\", '', text)\n return text\n\n\ndef token_lcs(path1, path2):\n text1 = code2text(path1)\n text2 = code2text(path2)\n path = os.path.abspath(os.path.dirname(__file__)) + \"\\\\tokenizer_lcs_cpp.pkl\"\n f1 = open(path, 'rb')\n tokenizer = pickle.load(f1)\n f1.close()\n\n # java_data = clc.load_data(data_path, \"java\")\n # tokenizer = Tokenizer(filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',)\n # tokenizer.fit_on_texts(java_data)\n\n seq1 = tokenizer.texts_to_sequences([text1])[0]\n seq2 = tokenizer.texts_to_sequences([text2])[0]\n return Levenshtein_distance.Levenshtein_distance(seq1,seq2) / max(len(seq1), len(seq2))\n\ndef token_cos(path1, path2):\n text1 = code2text(path1)\n text2 = code2text(path2)\n path = os.path.abspath(os.path.dirname(__file__)) + \"\\\\tokenizer_lcs_cpp.pkl\"\n f1 = open(path, 'rb')\n tokenizer = pickle.load(f1)\n f1.close()\n\n # java_data = clc.load_data(data_path, \"java\")\n # tokenizer = Tokenizer(filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',)\n # tokenizer.fit_on_texts(java_data)\n\n # 这里有一个问题：如果一段text从未在tokenizer中出现，则cos_similarity的除数是0\n seq1 = tokenizer.texts_to_sequences([text1])[0]\n seq2 = tokenizer.texts_to_sequences([text2])[0]\n return cos_similarity.cos_similarity_text(seq1, seq2)\n\nif __name__ == \"__main__\":\n # init_tokenizer()\n path1 = \"../../test/bubblesort.cpp\"\n path2 = \"../../test/heapsort.cpp\"\n similarity = token_cos(path1, path2)\n print(similarity)","sub_path":"src/token_based_similarities/token_lcs_cpp.py","file_name":"token_lcs_cpp.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359686844","text":"from e2 import Board, Piece\r\nfrom typing import List, Tuple\r\nfrom sortedcontainers import SortedList\r\nimport copy\r\n\r\n\r\nclass Backtrack:\r\n def __init__(self, board: Board, pieces: List[Piece]):\r\n \"\"\"\r\n Implementation of the backtrack solver which mutates the board parameter\r\n by placing the pieces in the correct position/rotations.\r\n Starts from bot left going rightwards.\r\n Args:\r\n board: The board\r\n pieces: A list of all available the pieces\r\n \"\"\"\r\n self._board = copy.deepcopy(board)\r\n self._pieces = SortedList(pieces)\r\n\r\n @property\r\n def board(self) -> Board:\r\n \"\"\"\r\n Returns:\r\n The Board instance\r\n \"\"\"\r\n return self._board\r\n\r\n def is_valid(self, piece: Piece, x: int, y: int) -> bool:\r\n \"\"\"\r\n Checks if placing piece at positition (x, y) is a valid move\r\n Args:\r\n piece: The piece to be placed\r\n x: The x-coordinate.\r\n y: The y-coordinate.\r\n\r\n Returns:\r\n True if placement is valid, False otherwise\r\n \"\"\"\r\n # Check top edge\r\n if y == self._board.height:\r\n if piece.top_edge != 0:\r\n return False\r\n else:\r\n if not self._board.piece_at(x, y + 1) is None:\r\n if piece.top_edge != self._board.piece_at(x, y + 1).bot_edge:\r\n return False\r\n\r\n # Check right edge\r\n if x == self._board.width:\r\n if piece.right_edge != 0:\r\n return False\r\n else:\r\n if not self._board.piece_at(x + 1, y) is None:\r\n if piece.right_edge != self._board.piece_at(x + 1, y).left_edge:\r\n return False\r\n\r\n # Check bot edge\r\n if y == 1:\r\n if piece.bot_edge != 0:\r\n return False\r\n else:\r\n if not self._board.piece_at(x, y - 1) is None:\r\n if piece.bot_edge != self._board.piece_at(x, y - 1).top_edge:\r\n return False\r\n\r\n # Check left edge\r\n if x == 1:\r\n if piece.left_edge != 0:\r\n return False\r\n else:\r\n if not self._board.piece_at(x - 1, y) is None:\r\n if piece.left_edge != self._board.piece_at(x - 1, y).right_edge:\r\n return False\r\n\r\n return True\r\n\r\n def next_step(self) -> Tuple[Piece, int, int]:\r\n \"\"\"\r\n Performs the next step in the backtracking process\r\n Returns:\r\n The Piece which was placed and the (x, y) location.\r\n If we backtracked, it returns None and the (x, y) location\r\n of the piece we removed\r\n \"\"\"\r\n row = 1\r\n col = 1\r\n i = 0\r\n while len(self._pieces) != 0:\r\n start_rot = 0\r\n # backtrack if we tried all pieces\r\n if i == len(self._pieces):\r\n if col == 1 and row == 1:\r\n print('Puzzle is unsolvable')\r\n elif col % self._board.width == 1:\r\n row -= 1\r\n col = self._board.width\r\n else:\r\n col -= 1\r\n removed_piece = self._board.remove_piece_at(col, row)\r\n self._pieces.add(removed_piece)\r\n i = self._pieces.index(removed_piece)\r\n start_rot = removed_piece.rotation + 90\r\n piece = copy.deepcopy(self._pieces[i])\r\n piece_placed = False\r\n for rot in range(start_rot, 360, 90):\r\n piece.rotation = rot\r\n if self.is_valid(piece, col, row):\r\n self._board.place_piece_at(piece, col, row)\r\n self._pieces.remove(piece)\r\n piece_placed = True\r\n yield piece, col, row\r\n if col % self._board.width != 0:\r\n col += 1\r\n else:\r\n col = 1\r\n row += 1\r\n break\r\n\r\n if piece_placed:\r\n i = 0\r\n else:\r\n i += 1\r\n\r\n def solve(self):\r\n for _ in self.next_step():\r\n pass\r\n","sub_path":"e2/solvers/backtrack.py","file_name":"backtrack.py","file_ext":"py","file_size_in_byte":4251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"353715337","text":"# Name: Frank Shang\n# OSU Email: shangf@oregonstate.edu\n# Course: CS261 - Data Structures\n# Assignment: 3\n# Due Date: 10/25/2021\n# Description: Implementing a Stack ADT by using a dynamic array.\n\nfrom dynamic_array import *\n\n\nclass StackException(Exception):\n \"\"\"\n Custom exception to be used by Stack class\n DO NOT CHANGE THIS METHOD IN ANY WAY\n \"\"\"\n pass\n\n\nclass Stack:\n def __init__(self):\n \"\"\"\n Init new stack based on Dynamic Array\n DO NOT CHANGE THIS METHOD IN ANY WAY\n \"\"\"\n self._da_val = DynamicArray()\n\n def __str__(self) -> str:\n \"\"\"\n Return content of stack in human-readable form\n DO NOT CHANGE THIS METHOD IN ANY WAY\n \"\"\"\n out = \"STACK: \" + str(self._da_val.length()) + \" elements. [\"\n out += ', '.join([str(self._da_val[i]) for i in range(self._da_val.length())])\n return out + ']'\n\n def is_empty(self) -> bool:\n \"\"\"\n Return True is the stack is empty, False otherwise\n DO NOT CHANGE THIS METHOD IN ANY WAY\n \"\"\"\n return self._da_val.is_empty()\n\n def size(self) -> int:\n \"\"\"\n Return number of elements currently in the stack\n DO NOT CHANGE THIS METHOD IN ANY WAY\n \"\"\"\n return self._da_val.length()\n\n # -----------------------------------------------------------------------\n\n def push(self, value: object) -> None:\n \"\"\"\n This method adds a new element to the top of the stock.\n \"\"\"\n self._da_val.append(value)\n\n def pop(self) -> object:\n \"\"\"\n This method removes the top element from the stack and returns its value.\n If the stack is empty, the method raises a custom \"StackException\".\n \"\"\"\n if self.is_empty():\n raise StackException\n\n # determine if the length of the dynamic array to find the last index\n index = self.size() - 1\n value = self._da_val.get_at_index(index)\n self._da_val.remove_at_index(index)\n return value\n\n def top(self) -> object:\n \"\"\"\n This method returns the value of the top element of the stack without removing it.\n If the stack is empty, the method raises StackException.\n \"\"\"\n if self.is_empty():\n raise StackException\n\n return self._da_val.get_at_index(self.size()-1)\n\n\n# ------------------- BASIC TESTING -----------------------------------------\n\n\nif __name__ == \"__main__\":\n\n print(\"\\n# push example 1\")\n s = Stack()\n print(s)\n for value in [1, 2, 3, 4, 5]:\n s.push(value)\n print(s)\n\n\n print(\"\\n# pop example 1\")\n s = Stack()\n try:\n print(s.pop())\n except Exception as e:\n print(\"Exception:\", type(e))\n for value in [1, 2, 3, 4, 5]:\n s.push(value)\n for i in range(6):\n try:\n print(s.pop())\n except Exception as e:\n print(\"Exception:\", type(e))\n\n\n print(\"\\n# top example 1\")\n s = Stack()\n try:\n s.top()\n except Exception as e:\n print(\"No elements in stack\", type(e))\n s.push(10)\n s.push(20)\n print(s)\n print(s.top())\n print(s.top())\n print(s)\n","sub_path":"stack_da.py","file_name":"stack_da.py","file_ext":"py","file_size_in_byte":3182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"618011077","text":"import boto3\n\ns3_resource = boto3.resource('s3')\nbucket_name = 'pytorch-geometric.com'\nbucket = s3_resource.Bucket(name=bucket_name)\nobjects = bucket.objects.all()\nwheels = [obj.key for obj in objects if obj.key[-3:] == 'whl']\nversions = sorted(list(set([wheel.split('/')[1] for wheel in wheels])))\n\nwheels_dict = {}\nfor torch_version in versions:\n wheels_dict[torch_version] = []\n\nfor wheel in wheels:\n torch_version = wheel.split('/')[1]\n wheels_dict[torch_version].append(\n (torch_version, '/'.join(wheel.split('/')[2:])))\n\nhtml = '\\n\\n\\n{}\\n\\n'\nhref = '{}
'\n\n# Add wheels for PyTorch 1.7.1 and 1.8.1\nfor key, value in list(wheels_dict.items()):\n if '1.7.0' in key:\n wheels_dict[key.replace('1.7.0', '1.7.1')] = value\n if '1.8.0' in key:\n wheels_dict[key.replace('1.8.0', '1.8.1')] = value\n\nindex_html = html.format('\\n'.join(\n [href.format(f'{key}.html', key) for key in wheels_dict.keys()]))\n\nwith open('index.html', 'w') as f:\n f.write(index_html)\n\nbucket.Object('whl/index.html').upload_file(\n Filename='index.html', ExtraArgs={\n 'ContentType': 'text/html',\n 'CacheControl': 'max-age=300',\n 'ACL': 'public-read'\n })\n\nfor key, wheels in wheels_dict.items():\n version_html = html.format('\\n'.join([\n href.format(f'{version}/{wheel}', wheel) for version, wheel in wheels\n ]))\n\n with open('{}.html'.format(key), 'w') as f:\n f.write(version_html)\n\n bucket.Object('whl/{}.html'.format(key)).upload_file(\n Filename='{}.html'.format(key), ExtraArgs={\n 'ContentType': 'text/html',\n 'CacheControl': 'max-age=300',\n 'ACL': 'public-read'\n })\n","sub_path":"wheel.py","file_name":"wheel.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"452891472","text":"my_dict = {}\n\nwith open('my_file_6.txt') as f:\n for line in f:\n content = line.split()\n sum_num = 0\n for el in content[1:]:\n num = el[:el.find('(')]\n if num:\n sum_num += int(num)\n \n my_dict[content[0]] = sum_num\n\n print(my_dict)","sub_path":"lesson05/5.6.py","file_name":"5.6.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648243018","text":"from time import sleep\nfrom BMI160_i2c import Driver\n\nprint('Trying to initialize the sensor...')\nsensor = Driver()\nprint('Initialization done')\n\nwhile True:\n data = sensor.getMotion6()\n # fetch all gyro and acclerometer values\n print({\n 'gx': data[0],\n 'gy': data[1],\n 'gz': data[2],\n 'ax': data[3],\n 'ay': data[4],\n 'az': data[5]\n })\n sleep(0.1)\n\n","sub_path":"examples/all.py","file_name":"all.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"396255243","text":"from socket import socket, AF_INET, SOCK_STREAM\nfrom additionals.settings import DEFAULT_PORT, DEFAULT_IP, ACTION, PRESENCE, TIME, USER, ACCOUNT_NAME, RESPONSE, ERROR\nfrom additionals.utils import send_msg, receive_msg\nfrom logging import getLogger\nfrom log.config import client_log_config\nimport time, json, sys\n\n\nclass Client:\n\n def __init__(self):\n self.logger = getLogger('app.client')\n try:\n self.connection_addr = sys.argv[1]\n self.connection_port = int(sys.argv[2])\n self.logger.debug(f'clients starts with {self.connection_addr}:{self.connection_port}')\n if 65535 < self.connection_port < 1024:\n raise ValueError\n except IndexError:\n self.connection_port = DEFAULT_PORT\n self.connection_addr = DEFAULT_IP\n self.logger.debug(f'clients starts without additional arguments '\n f'with default settings - {DEFAULT_IP}:{DEFAULT_PORT}')\n except ValueError:\n self.logger.critical(f'port is out off range 1024 - 65535')\n sys.exit(1)\n\n def client_presence(self, name='Guest'):\n self.presence = {\n ACTION: PRESENCE,\n TIME: time.time(),\n USER: {\n ACCOUNT_NAME: name\n }\n }\n self.logger.debug(f\"client created presence: {self.presence}\")\n return self.presence\n\n def presence_response(self, message):\n if RESPONSE in message:\n if message[RESPONSE] == 200:\n return '200 : OK'\n return f'400 : {message[ERROR]}'\n raise ValueError\n\n def start(self):\n try:\n self.sock = socket(AF_INET, SOCK_STREAM)\n self.logger.warning(f'client successfully started socket {self.sock}')\n self.sock.connect((self.connection_addr, self.connection_port))\n self.logger.debug(f'client successfully connected to {self.connection_addr}:{self.connection_port}')\n self.presence_msg = self.client_presence()\n send_msg(self.sock, self.presence_msg)\n self.logger.debug(f'client sent message to {self.sock} with {self.presence_msg}')\n\n except ConnectionRefusedError:\n self.logger.critical(f'error - connection refused')\n sys.exit(1)\n\n try:\n self.response = self.presence_response(receive_msg(self.sock))\n self.logger.info(f'client got response - {self.response}')\n print(self.response)\n\n except (ValueError, json.JSONDecodeError):\n self.logger.error(f'JSONDecodeError, can not to encode message')\n\n finally:\n self.logger.warning(f'clients stops')\n sys.exit(0)\n\n\nif __name__ == '__main__':\n try:\n client = Client()\n client.start()\n except SystemError:\n sys.exit(1)","sub_path":"lesson_5/client_oop.py","file_name":"client_oop.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"536490256","text":"import numpy as np\nimport zipfile\n\ndef evaluate(path):\n archive = zipfile.ZipFile(path, 'r')\n\n test_labels = np.array([int(e) for e in open(\"challenges/mnist/test_labels.csv\")])\n pred_labels = np.array([int(e) for e in archive.open(\"predictions.csv\")])\n\n assert len(test_labels) == len(pred_labels), \"Must have correct number of samples\"\n score = 100 * (1 - sum(test_labels == pred_labels)/len(test_labels))\n\n return int(score * 100)/100\n","sub_path":"server/challenges/mnist/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"139228024","text":"\"\"\"\nPrimary module for Alien Invaders\n\nThis module contains the main controller class for the Alien Invaders application. There\nis no need for any additional classes in this module. If you need more classes, 99% of\nthe time they belong in either the wave module or the models module. If you are unsure\nabout where a new class should go, post a question on Piazza.\n\nConner Swenberg, cls364 ; Jay Chand, jpc342\n12/8/17\n\"\"\"\nimport cornell\nfrom consts import *\nfrom game2d import *\nfrom wave import *\n\n\n# PRIMARY RULE: Invaders can only access attributes in wave.py via getters/setters\n# Invaders is NOT allowed to access anything in models.py\n\nclass Invaders(GameApp):\n \"\"\"\n The primary controller class for the Alien Invaders application\n\n This class extends GameApp and implements the various methods necessary for processing\n the player inputs and starting/running a game.\n\n Method start begins the application.\n\n Method update either changes the state or updates the Play object\n\n Method draw displays the Play object and any other elements on screen\n\n Because of some of the weird ways that Kivy works, you SHOULD NOT create an\n initializer __init__ for this class. Any initialization should be done in\n the start method instead. This is only for this class. All other classes\n behave normally.\n\n Most of the work handling the game is actually provided in the class Wave.\n Wave should be modeled after subcontrollers.py from lecture, and will have\n its own update and draw method.\n\n The primary purpose of this class is to manage the game state: which is when the\n game started, paused, completed, etc. It keeps track of that in an attribute\n called _state.\n\n INSTANCE ATTRIBUTES:\n view: the game view, used in drawing (see examples from class)\n [instance of GView; it is inherited from GameApp]\n input: the user input, used to control the ship and change state\n [instance of GInput; it is inherited from GameApp]\n _state: the current state of the game represented as a value from consts.py\n [one of STATE_INACTIVE, STATE_NEWWAVE, STATE_ACTIVE, STATE_PAUSED, STATE_CONTINUE, STATE_COMPLETE]\n _wave: the subcontroller for a single wave, which manages the ships and aliens\n [Wave, or None if there is no wave currently active]\n _text: the currently active message\n [GLabel, or None if there is no message to display]\n\n STATE SPECIFIC INVARIANTS:\n Attribute _wave is only None if _state is STATE_INACTIVE.\n Attribute _text is only None if _state is STATE_ACTIVE.\n\n For a complete description of how the states work, see the specification for the\n method update.\n\n You may have more attributes if you wish (you might want an attribute to store\n any score across multiple waves). If you add new attributes, they need to be\n documented here.\n\n LIST MORE ATTRIBUTES (AND THEIR INVARIANTS) HERE IF NECESSARY\n _lastkeys: the number key(s) pressed in the last frame [int>=0]\n _background: the background image of the game [class: Background]\n _death: used to express if the ship has run out of lives [bool]\n _newpara: string used in STATE_INTRO to create visual and time spacing between writing lines [str]\n _intro: the intro message to display on the screen when starting the game [str]\n _message: message currently displayed on the screen starting as empty and finishing at _intro [str]\n _iwrite: loop vairable in STATE_INTRO that increments each run through [0>=int>=WRITE_SPEED*len(self._message)]\n _jwrite: loop variable in STATE_INTRO that increments every WRITE_SPEED run throughs [0>=int>=len(self._message)]\n \"\"\"\n\n # DO NOT MAKE A NEW INITIALIZER!\n\n # THREE MAIN GAMEAPP METHODS\n def start(self):\n \"\"\"\n Initializes the application.\n\n This method is distinct from the built-in initializer __init__ (which you\n should not override or change). This method is called once the game is running.\n You should use it to initialize any game specific attributes.\n\n This method should make sure that all of the attributes satisfy the given\n invariants. When done, it sets the _state to STATE_INACTIVE and create a message\n (in attribute _text) saying that the user should press to play a game.\n \"\"\"\n self._state=STATE_INACTIVE\n self._text=GLabel(text='Press s to play')\n self.GLabelstuff(self._text,60,'Arcade.ttf',GAME_WIDTH//2,GAME_HEIGHT//2,cornell.RGB(255, 255, 255))\n self._wave=None\n self._background = Background('space.png')\n self.draw()\n self._lastkeys=0\n self._pause=' '\n self._newpara='\\n'+self._pause+'\\n'\n self._intro=(''+self._newpara+'Hello starfighter..'+self._newpara+\n 'The American Galactic Force needs your help\\n'+\n 'to defend our precious home from alien invaders..'+self._newpara+\n 'To engage with the enemy,\\n use leftarrow and rightarrow keys to move\\n'+\n 'and uparrow to fire laser bolts..'+self._newpara+\n 'Powerups will periodically drop from destroyed aliens..'+self._newpara+\n 'Good luck out there starfighter..'+self._pause)\n self._endmessage=''\n self._message=''\n self._iwrite=0\n self._jwrite=0\n def update(self,dt):\n \"\"\"\n Animates a single frame in the game.\n\n It is the method that does most of the work. It is NOT in charge of playing the\n game. That is the purpose of the class Wave. The primary purpose of this\n game is to determine the current state, and -- if the game is active -- pass\n the input to the Wave object _wave to play the game.\n\n As part of the assignment, you are allowed to add your own states. However, at\n a minimum you must support the following states: STATE_INACTIVE, STATE_NEWWAVE,\n STATE_ACTIVE, STATE_PAUSED, STATE_CONTINUE, and STATE_COMPLETE. Each one of these\n does its own thing and might even needs its own helper. We describe these below.\n\n STATE_INACTIVE: This is the state when the application first opens. It is a\n paused state, waiting for the player to start the game. It displays a simple\n message on the screen. The application remains in this state so long as the\n player never presses a key. In addition, this is the state the application\n returns to when the game is over (all lives are lost or all aliens are dead).\n\n STATE_NEWWAVE: This is the state creates a new wave and shows it on the screen.\n The application switches to this state if the state was STATE_INACTIVE in the\n previous frame, and the player pressed a key. This state only lasts one animation\n frame before switching to STATE_ACTIVE.\n\n STATE_ACTIVE: This is a session of normal gameplay. The player can move the\n ship and fire laser bolts. All of this should be handled inside of class Wave\n (NOT in this class). Hence the Wave class should have an update() method, just\n like the subcontroller example in lecture.\n\n STATE_PAUSED: Like STATE_INACTIVE, this is a paused state. However, the game is\n still visible on the screen.\n\n STATE_CONTINUE: This state restores the ship after it was destroyed. The\n application switches to this state if the state was STATE_PAUSED in the\n previous frame, and the player pressed a key. This state only lasts one animation\n frame before switching to STATE_ACTIVE.\n\n STATE_COMPLETE: The wave is over, and is either won or lost.\n\n You are allowed to add more states if you wish. Should you do so, you should\n describe them here.\n\n STATE_INTRO: This state comes immediatly after starting up the game from STATE_INACTIVE.\n it displayes an introduction message to orient the player with the setting and also\n provides the game controls and that powerups are worth grabbing. STATE_INTRO automatically\n enters STATE_ACTIVE after its message is complete, but can also be skipped by pressing\n spacebar.\n\n Parameter dt: The time in seconds since last update\n Precondition: dt is a number (int or float)\n \"\"\"\n # IMPLEMENT ME\n self._stateInfo()\n if self._state==STATE_INTRO:\n self._text=GLabel(text=self._message)\n self.GLabelstuff(self._text,30,'Arcade.ttf',GAME_WIDTH//2,GAME_HEIGHT//2,cornell.RGB(255, 255, 255))\n self.textwrite()\n if self._message==self._intro:\n self._state=STATE_NEWWAVE\n if self._state==STATE_NEWWAVE:\n self._wave=Wave()\n self._state=STATE_ACTIVE\n if self._state==STATE_ACTIVE:\n self.draw()\n self.view.clear()\n self._wave.update(self.view,self._state,self.input,dt)\n if self._state==STATE_PAUSED:\n self._text=GLabel(text='Press s to continue')\n if self._state==STATE_CONTINUE:\n self._wave.update(self.view,self._state,self.input,dt)\n self._state=STATE_ACTIVE\n if self._state==STATE_COMPLETE and self._wave.getLives()>0 and self._wave.getAliens()==0 and self._wave.getWavesleft()==0:\n self.view.clear()\n self._text=GLabel(text='You won!'+self._endmessage)\n if self._state==STATE_COMPLETE and self._wave.getLives()>0 and self._wave.getAliens()>0 and self._wave.getWavesleft()>=0:\n self.view.clear()\n self._text=GLabel(text='You lost :/'+self._endmessage)\n if self._state==STATE_COMPLETE and self._wave.getLives()==0:\n self.view.clear()\n self._text=GLabel(text='You lost :/'+self._endmessage)\n if self._state==STATE_COMPLETE and self._wave.getWavesleft()!=0:\n self.newwave()\n\n def draw(self):\n \"\"\"\n Draws the game objects to the view.\n\n Every single thing you want to draw in this game is a GObject. To draw a GObject\n g, simply use the method g.draw(self.view). It is that easy!\n\n Many of the GObjects (such as the ships, aliens, and bolts) are attributes in\n Wave. In order to draw them, you either need to add getters for these attributes\n or you need to add a draw method to class Wave. We suggest the latter. See\n the example subcontroller.py from class.\n \"\"\"\n self._background.draw(self.view)\n if self._text!=None:\n self._text.draw(self.view)\n if self._state==STATE_NEWWAVE:\n self._wave.draw(self.view)\n if self._state==STATE_ACTIVE:\n self._text.text=('Lives: '+str(self._wave.getLives()) +\n ' '+self._wave.getPowerup()+' '+\n 'Score: '+str(self._wave.getScore()))\n self.GLabelstuff(self._text,40,'Arcade.ttf',GAME_WIDTH//2,GAME_HEIGHT-60,cornell.RGB(255,255,255))\n self._wave.draw(self.view)\n if self._state==STATE_PAUSED or self._state==STATE_COMPLETE:\n self._wave.draw(self.view)\n self.GLabelstuff(self._text,60,'Arcade.ttf',GAME_WIDTH//2,GAME_HEIGHT//2,cornell.RGB(255,255,255))\n\n\n # HELPER METHODS FOR THE STATES GO HERE\n\n def _stateInfo(self):\n \"\"\"\n Called by update(), used to determine useful information depending on self._state.\n For STATE_INACTIVE, changes state from welcome screen to a new wave upon pressing 's'\n For STATE_PAUSED, changes state to STATE_CONTINUE upon pressing 's'\n For STATE_ACTIVE, gathers information on the current state of the wave and aliens left\n For STATE_INTRO, checks key presses to determine if the user wants to skip the intro\n For STATE_COMPLETE, creates the ending message to display and checks key presses\n to determine if user wishes to start over.\n \"\"\"\n key_s=self.input.is_key_down('s')\n curr_keys=self.input.key_count\n change=key_s and curr_keys>0\n key_space=self.input.is_key_down('spacebar')\n change2=key_space and curr_keys>0\n if self._state==STATE_INACTIVE:\n self._text=GLabel(text='Press s to play')\n self.GLabelstuff(self._text,60,'Arcade.ttf',GAME_WIDTH//2,GAME_HEIGHT//2,cornell.RGB(255, 255, 255))\n if change:\n self._state=STATE_INTRO\n self._lastkeys= curr_keys\n if self._state==STATE_PAUSED:\n if change:\n self._state=STATE_CONTINUE\n self._wave.setState(self._state)\n if self._state==STATE_ACTIVE:\n self._state=self._wave.getState()\n if self._state==STATE_INTRO:\n if change2:\n self._state=STATE_NEWWAVE\n if self._state==STATE_COMPLETE:\n self._endmessage='\\nScore: '+str(self._wave.getScore())+'\\n'\n if change2:\n self._state=STATE_INACTIVE\n\n\n def textwrite(self):\n \"\"\"\n Helper method used to write the intro message in STATE_INTRO.\n Utilizes two loop variables to adjust the speed at which text is written.\n Adds to the message on screen by adding each character in the str self._intro\n and stops when the two messages are the same.\n \"\"\"\n if self._jwrite0\n if change:\n self._wave = Wave(lives, waves-1, score)\n self._state = STATE_ACTIVE\n\n def GLabelstuff(self,obj,size,font,x,y,linecolor):\n \"\"\"\n Helper function that changes the other attributes of a created GLabel\n\n Parameter obj: GLabel object to modify\n Precondition: obj is a valid GLabel with text already assigned to it\n\n Parameter size: size of text to set\n Precondition: size is an int>0\n\n Parameter font: choice of font to use for text\n Precondition: font is a valid font_name\n\n Parameter x: center x coordinate for GLabel text\n Precondition: x is an int, 0 OrderListSchema:\n url = f'{self.main_api}/order/{self.get_default_query_parm(page, page_size, last_id, last_tm)}'\n response = requests.get(url, headers=self.headers)\n self.check_status_code(response.status_code)\n return order_parser(response)\n\n def get_measurement(\n self, page: int = 1, page_size: int = 25, last_id: int = 0, last_tm: int = 0\n ) -> MeasurementListSchema:\n\n url = f'{self.main_api}/measurement/{self.get_default_query_parm(page, page_size, last_id, last_tm)}'\n response = requests.get(url, headers=self.headers)\n self.check_status_code(response.status_code)\n return measurement_parser(response)\n\n def get_brand(\n self, page: int = 1, page_size: int = 25, last_id: int = 0, last_tm: int = 0\n ) -> BrandListSchema:\n\n url = f'{self.main_api}/brand/{self.get_default_query_parm(page, page_size, last_id, last_tm)}'\n response = requests.get(url, headers=self.headers)\n self.check_status_code(response.status_code)\n return brand_parser(response)\n\n def create_product(self, product_dto: ProductCreateSchema):\n product_dto = dataclasses.asdict(product_dto)\n url = f'{self.main_api}/product/'\n response = requests.post(url, headers=self.headers, json=product_dto)\n self.check_status_code(response.status_code)\n if response.status_code == 201:\n return True\n return print(response.text)\n\n def delete_product(self, provider_product_id: int):\n url = f'{self.main_api}/product/{provider_product_id}/'\n response = requests.delete(url, headers=self.headers)\n self.check_status_code(response.status_code)\n return True\n\n def upload_file(self, file_path: str) -> int:\n file = open(file_path, 'rb')\n url = f'{self.url}/api/v1/file/'\n response = requests.post(url, files={'file': file})\n self.check_status_code(response.status_code)\n return response.json()['id']\n\n def get_category(self):\n url = f'{self.main_api}/category/'\n response = requests.get(url, headers=self.headers)\n self.check_status_code(response.status_code)\n data = response.json()\n return data\n\n def check_status_code(self, status_code: int):\n if status_code == 401:\n raise AuthenticationError\n elif status_code == 404:\n raise NotFoundError\n\n def get_default_query_parm(self, page: int = 0, page_size: int = 25, last_id: int = 0, last_tm: int = 0) -> str:\n return f'?page={page}&page_size={page_size}&last_id={last_id}&last_tm={last_tm}'\n","sub_path":"setuz/setuz.py","file_name":"setuz.py","file_ext":"py","file_size_in_byte":3153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"14876364","text":"import os\nfrom datetime import datetime\n\nimport xlrd\nfrom xlrd import xldate_as_tuple\nfrom xlutils.copy import copy\n\nDATAPATH = os.path.dirname(os.path.realpath(__file__)) # 获取项目根目录\nclass Excel():\n def __init__(self, filename):\n \"\"\"filename = excel文件名称，row = 从excel表的第几行开始读取\"\"\"\n\n self.filename = filename\n\n self.workbook = xlrd.open_workbook(DATAPATH+\n r\"/{}.xls\".format(\n filename)) #加载EXCLE文件\n\n self.table = self.workbook.sheets()[0] #获取文件sheet\n\n self.nrows = self.table.nrows #excel表格中的行数\n\n self.ncols = self.table.ncols #excel表格中的列数\n\n def read_excel(self, row):\n \"\"\"读取excel表格内的文件并且使用字典表进行储存\"\"\"\n list = []\n for r in range(row, self.nrows):\n app = {}\n for col in range(self.ncols):\n value = self.table.cell(r, col).value\n ctype = self.table.cell(r, col).ctype\n if ctype == 0:\n value = \"\"\n elif ctype == 1:\n value = value\n elif ctype == 2:\n value = int(value)\n elif ctype == 3:\n date = datetime(*xldate_as_tuple(value, 0))\n value = date.strftime(\"%Y/%m/%d %H:%M:%S\")\n elif ctype == 4:\n if value == 0:\n value = False\n if value == 1:\n value = True\n elif ctype == 5:\n value = \"错误~~~~~\"\n app[self.table.cell(row-1, col).value] = value\n list.append(app)\n\n return list\n def write_excel(self,datas,row = 1):\n \"\"\"写入excel表格\"\"\"\n new_excel = copy(self.workbook)\n ws = new_excel.get_sheet(0)\n if len(datas) == 0:\n print(\"错误！！！！\")\n else:\n for col in range(self.ncols):\n print(datas[col], \"datas[col]\")\n if datas[col] != \"\" or datas[col] == None:\n ws.write(row, col, datas[col])\n new_excel.save(DATAPATH+\n r\"\\{}.xls\".format(\n self.filename))\n def write_excel_rol(self,col,row, data):\n new_excel = copy(self.workbook)\n ws = new_excel.get_sheet(0)\n #print('写入中')\n ws.write(col, row, data)\n ws.col(0).width = 5555\n ws.col(1).width = 5555\n ws.col(2).width = 5555\n ws.col(4).width = 5555\n ws.col(5).width = 5555\n ws.col(6).width = 5555\n ws.col(8).width = 5555\n ws.col(9).width = 5555\n ws.col(10).width = 5555\n new_excel.save(DATAPATH +\n r\"/{}.xls\".format(\n self.filename))","sub_path":"function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"288189577","text":"from keyboard import press_and_release, write\nfrom time import sleep, localtime\nimport webbrowser\nfrom pynput.mouse import Button, Controller\n\nmouse = Controller()\n\n\ndef marca():\n press_and_release('shift+2') # @\n write('victorialuquett')\n press_and_release('enter')\n sleep(1)\n press_and_release('shift+2') # @\n write('Quequeh0uve')\n press_and_release('enter')\n sleep(1)\n press_and_release('shift+2') # @\n write('juuldecereja')\n press_and_release('enter')\ndef envia():\n mouse.position = (1007, 458) # envia o cursor para o botao de enviar do twitter\n mouse.click(Button.left, 1)\ndef fechar():\n mouse.position = (1514, 11) # envia o cursor para o botão de fechar o chrome\n mouse.click(Button.left, 1)\n\nwhile True:\n hora = localtime()\n if(hora.tm_hour != 14): # caso não seja 14 horas \n print(\"esperando proxima hora\")\n sleep(3600)\n elif(hora.tm_hour == 14 and hora.tm_min == 30): # caso seja a hora exata do novo post\n webbrowser.open('https://twitter.com/vomaxroupa', new=2) # abre a pagina do twitter no chrome\n sleep(8)\n mouse.position = (739, 803) # Abre a Publicação mais recente\n sleep(0.1)\n mouse.click(Button.left, 1)\n sleep(2)\n press_and_release('r') # Aperta R para responder o tweet\n sleep(2)\n marca()\n sleep(2)\n envia()\n sleep(2)\n fechar()\n sleep(60)\n else:\n print(\"esperando minutagem\") #caso seja 14 horas porém não seja 14:30\n print(hora.tm_min)\n sleep(60)\n","sub_path":"Max.py","file_name":"Max.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"219172506","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom whichsandwich.models import Profile, Sandwich, Ingredient, Comment\nfrom whichsandwich.forms import UserForm, UserProfileForm, SandwichForm, CommentForm\nfrom django.urls import reverse\nimport random\n\ndef index(request):\n #http://127.0.0.1:8000/whichsandwich/\n\n sotd = None\n top_sandwiches = Sandwich.objects.order_by('-likes')\n if top_sandwiches:\n sotd = top_sandwiches[0]\n\n context_dict = {\n 'top_sandwiches': top_sandwiches[1:5],\n 'sotd': sotd,\n }\n\n response = render(request, 'whichsandwich/index.html', context = context_dict)\n return response\n\n #How do we define sandwich of the day\n\ndef browse(request):\n return render(request, 'whichsandwich/browse.html')\n\ndef modal(request):\n context_dict = {}\n if request.method == 'GET':\n sandwich_id = request.GET['sandwich_id']\n sandwich = Sandwich.objects.get(id=sandwich_id)\n context_dict['sandwich'] = sandwich\n try:\n comments = Comment.objects.filter(sandwich=sandwich)\n rand_comment_index = random.randint(0,len(comments) - 1)\n context_dict['comment'] = comments[rand_comment_index]\n except (IndexError, ValueError) as e:\n print(e)\n context_dict['comment'] = None\n return render(request, 'whichsandwich/modal.html', context_dict)\n\ndef browse_filter(request):\n sort_filter = None\n if request.method == 'GET':\n sort_filter = request.GET['sort_filter']\n if sort_filter == 'new':\n return new(request)\n elif sort_filter == 'controversial':\n return controversial(request)\n else:\n # Top by default\n return top(request)\n\ndef show_sandwich(request, sandwich_slug):\n context_dict = {}\n creator = request.user\n\t\n try:\n creator = Profile.objects.get(user=creator)\n context_dict['favourites'] = creator.favourites.all();\n except:\n context_dict['favourites'] = None\n\t\n try:\n sandwich = Sandwich.objects.get(slug=sandwich_slug)\n context_dict['sandwich'] = sandwich\n comments = Comment.objects.filter(sandwich=sandwich)\n context_dict['comments'] = comments\n except Sandwich.DoesNotExist:\n context_dict['sandwich'] = None\n context_dict['comments'] = None\n return render(request, 'whichsandwich/sandwich.html', context_dict)\n\ndef top(request):\n top_sandwiches = Sandwich.objects.order_by('-likes')\n \n context_dict = {'sandwiches': top_sandwiches}\n response = render(request, 'whichsandwich/sandwich_grid.html', context = context_dict)\n return response\n\ndef new(request):\n new_sandwiches = Sandwich.objects.order_by('-created_date')\n \n context_dict = {'sandwiches': new_sandwiches}\n response = render(request, 'whichsandwich/sandwich_grid.html', context = context_dict)\n return response\n\ndef controversial(request):\n # Maximum percentage difference between likes and dislikes for controversy\n max_perc_diff = 25\n\n # After a set number of likes & dislikes, a sandwich becomes elligible for controversy\n sandwiches = Sandwich.objects.filter(likes__gt=10, dislikes__gt=10)\n\n c_sandwiches = []\n\n # Get controversial sandwiches\n for sandwich in sandwiches:\n delta = abs(sandwich.likes - sandwich.dislikes)\n avg = (sandwich.likes + sandwich.dislikes)/2\n c_level = delta/avg*100\n if c_level <= max_perc_diff:\n # Add controversial sandwich to list alongside percentage difference\n # between likes and dislikes\n c_sandwiches.append([c_level, sandwich])\n\n # Sort sandwiches by difference between likes and dislikes\n c_sandwiches = sorted(c_sandwiches, key=lambda s: s[0])\n # Retrieve just the sandwich\n c_sandwiches = [s for c,s in c_sandwiches]\n \n return render(request, 'whichsandwich/sandwich_grid.html', {'sandwiches': c_sandwiches})\n\ndef sandwich_name(request):\n \n context_dict = {}\n try:\n # If we can't, the .get() method raises a DoesNotExist exception.\n names = Sandwich.objects.get('name')\n context_dict['Sandwich Names'] = names\n except Category.DoesNotExist:\n context_dict['Sandwich Names'] = None\n \n response = render(request, 'whichsandwich/browse.html', context = context_dict)\n return response\n\n@login_required\ndef my_account(request):\n best_sandwiches = Sandwich.objects.filter(creator=request.user).order_by('-likes', 'dislikes')\n top_favourites = request.user.profile.favourites.all().order_by('-likes', 'dislikes')[0:5]\n\n context_dict = {\n 'best_sandwiches': best_sandwiches,\n\t\t\t'top_favourites': top_favourites,\n }\n\n return render(request, 'whichsandwich/my_account.html', context = context_dict)\n\n@login_required\ndef my_sandwiches(request):\n sandwiches = Sandwich.objects.filter(creator=request.user)\n context_dict = {'sandwiches': sandwiches}\n return render(request, 'whichsandwich/my_sandwiches.html',\n context = context_dict)\n\n@login_required\ndef my_favourites(request):\n context_dict = {}\n favourites = request.user.profile.favourites.all()\n context_dict['sandwiches'] = favourites\n return render(request, 'whichsandwich/my_favourites.html', context=context_dict)\n\n@login_required\ndef create_sandwich(request):\n form = SandwichForm()\n\n if request.method == 'POST':\n form = SandwichForm(request.POST, request.FILES)\n\n if form.is_valid():\n sandwich = form.save(commit=False)\n sandwich.creator = request.user\n sandwich.save()\n form.save_m2m()\n return show_sandwich(request, sandwich.slug)\n else:\n print(form.errors)\n\n return render(request, 'whichsandwich/create_sandwich.html', {'form':form})\n\ndef about(request):\n\n #No need for context_dict if we do not show user's number of visits.\n return render(request, 'whichsandwich/about.html')\n\n@login_required\ndef comment(request, sandwich_slug):\n creator = request.user.profile\n sandwich = Sandwich.objects.get(slug=sandwich_slug)\n form = CommentForm()\n\n if request.method == 'POST':\n form = CommentForm(request.POST)\n\n if form.is_valid():\n comment = form.save(commit=False)\n comment.user = creator\n comment.sandwich = sandwich\n comment.save()\n form.save_m2m()\n return show_sandwich(request, sandwich.slug)\n else:\n print(form.errors)\n\n return render(request, 'whichsandwich/comment.html', {'form':form, 'sandwich':sandwich})\n\ndef add_to_favourites(request):\n creator = request.user\n creator = Profile.objects.get(user=creator)\n sw_name = None\n if request.method == 'GET':\n sw_name = request.GET['sandwich_name']\n if sw_name:\n sandwich = Sandwich.objects.get(name=sw_name)\n if sandwich:\n creator.favourites.add(sandwich)\n creator.save()\n\t\t\t\n return HttpResponse(\"Added to favourites\")\n\t\ndef like_sandwich(request):\n sw_name = None\n if request.method == 'GET':\n sw_name = request.GET['sandwich_name']\n likes = 0;\n if sw_name:\n sandwich = Sandwich.objects.get(name=sw_name)\n if sandwich:\n likes = sandwich.likes + 1\n sandwich.likes = likes\n sandwich.save()\n return HttpResponse(likes)\n\t\ndef dislike_sandwich(request):\n sw_name = None\n if request.method == 'GET':\n sw_name = request.GET['sandwich_name']\n dislikes = 0;\n if sw_name:\n sandwich = Sandwich.objects.get(name=sw_name)\n if sandwich:\n dislikes = sandwich.dislikes + 1\n sandwich.dislikes = dislikes\n sandwich.save()\n return HttpResponse(dislikes)\n","sub_path":"which_sandwich_project/whichsandwich/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615269469","text":"import numpy as np\nimport numpy.linalg as nla\n\nimport fym\nimport fym.core\nimport fym.agents.LQR\nimport fym.logging as logging\nfrom fym.models.aircraft import MorphingLon\nfrom fym.utils.linearization import jacob_analytic\n\nfrom utils import get_poly\n\n\nclass BaseEnv(fym.core.BaseEnv):\n Q = np.diag([1, 100, 10, 100])\n R = np.diag([50, 1, 1, 1])\n\n def __init__(self, initial_perturb, **kwargs):\n self.system = MorphingLon([0, 0, 0, 0])\n self.IR_system = fym.core.BaseSystem(0, name=\"integral reward\")\n super().__init__(\n systems_dict={\n \"main\": self.system,\n \"IR\": self.IR_system,\n },\n **kwargs\n )\n\n self.initial_perturb = initial_perturb\n trim_x, trim_u = self.system.get_trim()\n self.trim_x = trim_x\n self.trim_u = trim_u\n self.system.initial_state = trim_x + initial_perturb\n\n def reset(self, initial_perturb=None):\n if initial_perturb == \"random\":\n self.system.initial_state = (\n self.trim_x\n + self.initial_perturb\n + [1, 0.05, 0.05, 0.05] * np.random.randn(4)\n )\n\n super().reset()\n return self.observation()\n\n def observation(self):\n return self.system.state - self.trim_x\n\n def step(self, action):\n done = self.clock.time_over()\n time = self.clock.get()\n x = self.observation()\n IR = self.IR_system.state\n u = action + self.trim_u\n\n if np.any(np.abs(x[(1, 3), ]) > np.deg2rad(30)):\n done = True\n\n self.update(u)\n\n next_x = self.observation()\n nIR = self.IR_system.state\n reward = nIR - IR\n\n info = {\n \"time\": time,\n \"state\": x,\n \"action\": action,\n \"reward\": reward,\n \"next_state\": next_x\n }\n\n return next_x, reward, done, info\n\n def set_dot(self, time, u):\n x, _ = self.observe_list()\n\n self.system.dot = self.system.deriv(x, u)\n self.systems_dict[\"IR\"].dot = self.reward(x, u)\n\n def reward(self, x, u):\n tx = x - self.trim_x\n tu = u - self.trim_u\n return tx.dot(self.Q).dot(tx) + tu.dot(self.R).dot(tu)\n\n\nclass AdpEnv(BaseEnv):\n def __init__(self, initial_perturb, W_init, eta, **kwargs):\n super().__init__(initial_perturb, W_init, **kwargs)\n self.eta = eta\n self.grad_u_phi_c = jacob_analytic(self.phi_c, i=1)\n\n def reset(self):\n super().reset()\n return self.observation()\n\n def observation(self):\n return self.observe_list()\n\n def step(self, action):\n done = self.clock.time_over()\n time = self.clock.get()\n x, dIR, W = self.observe_list()\n\n Wb, Wc = action\n bu = self.get_behavior(Wb, x)\n\n if np.any(np.abs(x[(1, 3), ]) > np.deg2rad(30)):\n done = True\n\n if np.abs(W).max() > 100 or np.abs(Wc).max() > 100:\n done = True\n\n self.update(action)\n\n IR = self.IR_system.state\n reward = IR - dIR\n\n info = {\n \"time\": time,\n \"trim_x\": self.trim_x,\n \"trim_u\": self.trim_u,\n \"state\": x,\n \"control\": bu,\n \"W\": W.ravel(),\n \"Wb\": Wb.ravel(),\n \"Wc\": Wc.ravel(),\n \"reward\": reward,\n }\n\n return self.observation(), reward, done, info\n\n def get_behavior(self, Wb, x):\n \"\"\"If ``x = self.trim_x``, then ``del_u = 0``. This is ensured by\n the structure of ``phi`` which has no constant term. Also,\n the behavior policy should always be saturated by the control limits\n defined by the system.\"\"\"\n del_ub = Wb.T.dot(self.phi(x))\n return self.system.saturation(self.trim_u + del_ub)\n\n def get_target(self, Wc, W, x):\n del_u = W.T.dot(self.phi(x))\n return self.system.saturation(self.trim_u + del_u)\n\n def set_dot(self, time, action):\n super().set_dot(time, action)\n x, _ = self.observe_list()\n Wb, Wc = action\n bu = self.get_behavior(Wb, x)\n # us = self.get_behavior(W, x)\n grad_Q = np.outer(\n self.phi(x),\n self.grad_u_phi_c(x, bu).T.dot(Wc)\n )\n\n self.systems_dict[\"W\"].dot = - self.eta * grad_Q\n","sub_path":"envs.py","file_name":"envs.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"77346620","text":"\n\nfrom xai.brain.wordbase.verbs._throb import _THROB\n\n#calss header\nclass _THROBBED(_THROB, ):\n\tdef __init__(self,): \n\t\t_THROB.__init__(self)\n\t\tself.name = \"THROBBED\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"throb\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_throbbed.py","file_name":"_throbbed.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"526718025","text":"import tkinter as tk\nfrom tkinter import ttk\n\n#link\n\n__title__ = \"Statusbar\"\n__version__ = \"1.0.0\"\n__author__ = \"DeflatedPickle\"\n\nclass Statusbar (ttk.Frame):\n \"\"\"\n -----DESCRIPTION-----\n The Statusbar can be used to show certain variables.\n\n -----USAGE-----\n statusbar = Statusbar (parent)\n statusbar.pack ()\n statusbar.add_label (text = [string], textvariable = [string], image = [string], side = [string])\n statusbar.add_separator ()\n\n -----CONTENTS-----\n ---VARIABLES---\n\n ---WIDGETS---\n Self\n\n ---FUNCTIONS---\n add_label () = Adds a label to the statusbar.\n add_separator () = Adds a separator to the statusbar.\n \"\"\"\n def __init__ (self, parent, *args):\n ttk.Frame.__init__ (self, parent, *args)\n\n def add_label (self, text = \"\", textvariable = \"\", image = \"\", side = \"left\"):\n ttk.Label (self, text = text, textvariable = textvariable, image = image).pack (side = side)\n\n def add_sizegrip (self, side = \"left\"):\n ttk.Sizegrip (self).pack (side = side)\n\n def add_separator (self):\n ttk.Separator (self, orient = \"vertical\").pack (side = \"left\", fill = \"y\", padx = 3, pady = 1)\n\n##################################################\n\nif __name__ == \"__main__\":\n root = tk.Tk ()\n sbar = Statusbar (root)\n sbar.pack (expand = True, fill = \"x\", padx = 5, pady = 5)\n sbar.add_label (text = \"A Label\")\n sbar.add_separator ()\n sbar.add_sizegrip (side = \"right\")\n root.mainloop ()\n","sub_path":"pkinter/statusbar.py","file_name":"statusbar.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"397666497","text":"import pygame\n\nfrom Simulation import screen\n\nclass Info():\n def __init__( self, x, y, population ):\n self.x = x\n self.y = y\n self.height = 600\n self.width = 300\n self.color = (20,20,20)\n self.font_color = (229, 231, 233)\n self.font_title = pygame.font.Font(\"freesansbold.ttf\", 25 )\n self.font_normal = pygame.font.Font(\"freesansbold.ttf\", 20 )\n\n self.generation = 0\n self.population = population\n self.best = 0\n\n def Draw( self ):\n pygame.draw.rect(screen, self.color, [self.x, self.y, self.width, self.height])\n title_ = self.font_title.render(\"Genetic Algorithm\", True, self.font_color )\n pop_ = self.font_normal.render(\"Population: {}\".format(self.population ), True, self.font_color )\n gen_ = self.font_normal.render(\"Generation: {}\".format(self.generation), True, self.font_color )\n best = self.font_normal.render(\"Shortest: {}\".format(self.best), True, self.font_color )\n\n screen.blit( title_, (15,20) )\n screen.blit( pop_, (15,80) )\n screen.blit( gen_, (15,110) )\n screen.blit( best, (15,140) )\n","sub_path":"Simulation/classes/information.py","file_name":"information.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"115101274","text":"#!/usr/bin/env python\n#*-*coding:utf-8*-*\n#\n\nimport json\n\nclass ResponseDecorator():\n def __init__(self):\n self.data = {}\n self.fin_rsp = {}\n self.fin_rsp_j = None\n\n def get_images_deco(self, func):\n def get_image_rsp_deco():\n rsp = func()\n self.fin_rsp = {}\n self.data = {}\n self.data['images'] = []\n for i in range(len(rsp['data']['images'])):\n self.data['images'].append({\"id\": rsp['data']['images'][i]['id'], \n \"name\": rsp['data']['images'][i]['name']})\n self.fin_rsp['data'] = self.data\n self.fin_rsp_j = json.dumps(self.fin_rsp)\n return self.fin_rsp_j\n return get_image_rsp_deco\n\n def get_flavors_deco(self, func):\n def get_flavors_rsp_deco():\n rsp = func()\n self.data['success'] = True\n self.data['message'] = \"\"\n self.data['flavors'] = []\n for i in range(len(rsp['data']['flavors'])):\n self.data['flavors'].append({\"id\": rsp['data']['flavors'][i]['id'],\n \"name\": rsp['data']['flavors'][i]['name'],\n \"vcpus\": rsp['data']['flavors'][i]['vcpus'],\n \"memory\": rsp['data']['flavors'][i]['ram'],\n \"disk\": rsp['data']['flavors'][i]['disk']})\n self.fin_rsp['data'] = self.data\n self.fin_rsp_j = json.dumps(self.fin_rsp)\n return self.fin_rsp_j\n return get_flavors_rsp_deco\n\n def create_tenant_deco(self, func):\n def create_tenant_rsp_deco():\n rsp = func()\n self.data['success'] = True\n self.data['message'] = \"\"\n tenant_id = rsp['data']['tenant']['id']\n self.data['tenantId'] = tenant_id\n self.fin_rsp['data'] = self.data\n self.fin_rsp_j = json.dumps(self.fin_rsp)\n return self.fin_rsp_j\n return create_tenant_rsp_deco\n\n def get_quota_deco(self, func):\n def get_quota_rsp_deco():\n rsp = func()\n quota = {}\n self.data['message'] = \"\"\n self.data['quotas'] = {}\n self.data['success'] = True\n\n iaas_quota = rsp['data']['quota_set']\n quota['cores'] = iaas_quota.get('cores', None)\n quota['floatingIps'] = iaas_quota.get('floating_ips', None)\n quota['gigaBytes'] = iaas_quota.get('gigabytes', None)\n quota['injectedFileContentBytes'] = iaas_quota.get('injected_file_content_bytes', None)\n quota['injectedFilePathBytes'] = iaas_quota.get('injected_file_path_bytes', None)\n quota['injectedFiles'] = iaas_quota.get('injected_files', None)\n quota['instances'] = iaas_quota.get('instances', None)\n quota['keyPairs'] = iaas_quota.get('key_pairs', None)\n quota['metadataItems'] = iaas_quota.get('metadata_items', None)\n quota['ram'] = iaas_quota.get('ram', None)\n quota['securityGroupRules'] = iaas_quota.get('cores', None)\n quota['securityGroups'] = iaas_quota.get('security_groups', None)\n quota['volumes'] = iaas_quota.get('volumes', None)\n self.data['quotas'] = quota\n\n if self.data['quotas']:\n self.data['message'] = \"获取配额成功\"\n\n self.fin_rsp['data'] = self.data\n self.fin_rsp_j = json.dumps(self.fin_rsp)\n return self.fin_rsp_j\n return get_quota_rsp_deco\n\n def release_tenant_deco(self, func):\n def release_tenant_rsp_deco():\n rsp = func()\n self.data['message'] = \"\"\n self.data['success'] = rsp['data']['success']\n self.fin_rsp['data'] = self.data\n self.fin_rsp_j = json.dumps(self.fin_rsp)\n return self.fin_rsp_j\n return release_tenant_rsp_deco","sub_path":"decorator.py","file_name":"decorator.py","file_ext":"py","file_size_in_byte":4106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299587183","text":"# -*- coding: utf-8 -*-\n'''\nThis module implements :class:`AnalogSignalArray`, an array of analog signals.\n\n:class:`AnalogSignalArray` derives from :class:`BaseAnalogSignal`, from\n:module:`neo.core.analogsignal`.\n\n:class:`BaseAnalogSignal` inherits from :class:`quantites.Quantity`, which\ninherits from :class:`numpy.array`.\nInheritance from :class:`numpy.array` is explained here:\nhttp://docs.scipy.org/doc/numpy/user/basics.subclassing.html\n\nIn brief:\n* Initialization of a new object from constructor happens in :meth:`__new__`.\nThis is where user-specified attributes are set.\n\n* :meth:`__array_finalize__` is called for all new objects, including those\ncreated by slicing. This is where attributes are copied over from\nthe old object.\n'''\n\n# needed for python 3 compatibility\nfrom __future__ import absolute_import, division, print_function\n\nimport logging\n\nimport numpy as np\nimport quantities as pq\n\nfrom neo.core.analogsignal import (BaseAnalogSignal, AnalogSignal,\n _get_sampling_rate)\nfrom neo.core.baseneo import BaseNeo, merge_annotations\n\nlogger = logging.getLogger(\"Neo\")\n\n\nclass AnalogSignalArray(BaseAnalogSignal):\n '''\n Several continuous analog signals\n\n A representation of several continuous, analog signals that\n have the same duration, sampling rate and start time.\n Basically, it is a 2D array like AnalogSignal: dim 0 is time, dim 1 is\n channel index\n\n Inherits from :class:`quantities.Quantity`, which in turn inherits from\n :class:`numpy.ndarray`.\n\n *Usage*::\n\n >>> from neo.core import AnalogSignalArray\n >>> import quantities as pq\n >>>\n >>> sigarr = AnalogSignalArray([[1, 2, 3], [4, 5, 6]], units='V',\n ... sampling_rate=1*pq.Hz)\n >>>\n >>> sigarr\n \n >>> sigarr[:,1]\n \n >>> sigarr[1, 1]\n array(5) * V\n\n *Required attributes/properties*:\n :signal: (quantity array 2D, numpy array 2D, or list (data, chanel))\n The data itself.\n :units: (quantity units) Required if the signal is a list or NumPy\n array, not if it is a :class:`Quantity`\n :t_start: (quantity scalar) Time when signal begins\n :sampling_rate: *or* :sampling_period: (quantity scalar) Number of\n samples per unit time or\n interval between two samples.\n If both are specified, they are\n checked for consistency.\n\n *Recommended attributes/properties*:\n :name: (str) A label for the dataset.\n :description: (str) Text description.\n :file_origin: (str) Filesystem path or URL of the original data file.\n :channel_index: (numpy array 1D dtype='i') You can use this to order\n the columns of the signal in any way you want. It should have the\n same number of elements as the signal has columns.\n :class:`AnalogSignal` and :class:`Unit` objects can be given\n indexes as well so related objects can be linked together.\n\n *Optional attributes/properties*:\n :dtype: (numpy dtype or str) Override the dtype of the signal array.\n :copy: (bool) True by default.\n\n Note: Any other additional arguments are assumed to be user-specific\n metadata and stored in :attr:`annotations`.\n\n *Properties available on this object*:\n :sampling_rate: (quantity scalar) Number of samples per unit time.\n (1/:attr:`sampling_period`)\n :sampling_period: (quantity scalar) Interval between two samples.\n (1/:attr:`quantity scalar`)\n :duration: (Quantity) Signal duration, read-only.\n (size * :attr:`sampling_period`)\n :t_stop: (quantity scalar) Time when signal ends, read-only.\n (:attr:`t_start` + :attr:`duration`)\n :times: (quantity 1D) The time points of each sample of the signal,\n read-only.\n (:attr:`t_start` + arange(:attr:`shape`[0])/:attr:`sampling_rate`)\n :channel_indexes: (numpy array 1D dtype='i') The same as\n :attr:`channel_index`, read-only.\n\n *Slicing*:\n :class:`AnalogSignalArray` objects can be sliced. When taking a single\n row (dimension 1, e.g. [:, 0]), a :class:`AnalogSignal` is returned.\n When taking a single element, a :class:`~quantities.Quantity` is\n returned. Otherwise a :class:`AnalogSignalArray` (actually a view) is\n returned, with the same metadata, except that :attr:`t_start`\n is changed if the start index along dimension 1 is greater than 1.\n Getting a single item returns a :class:`~quantity.Quantity` scalar.\n\n *Operations available on this object*:\n == != + * /\n\n '''\n\n _single_parent_objects = ('Segment', 'RecordingChannelGroup')\n _quantity_attr = 'signal'\n _necessary_attrs = (('signal', pq.Quantity, 2),\n ('sampling_rate', pq.Quantity, 0),\n ('t_start', pq.Quantity, 0))\n _recommended_attrs = ((('channel_index', np.ndarray, 1, np.dtype('i')),) +\n BaseNeo._recommended_attrs)\n\n def __new__(cls, signal, units=None, dtype=None, copy=True,\n t_start=0 * pq.s, sampling_rate=None, sampling_period=None,\n name=None, file_origin=None, description=None,\n channel_index=None, **annotations):\n '''\n Constructs new :class:`AnalogSignalArray` from data.\n\n This is called whenever a new class:`AnalogSignalArray` is created from\n the constructor, but not when slicing.\n '''\n if (isinstance(signal, pq.Quantity)\n and units is not None\n and units != signal.units):\n signal = signal.rescale(units)\n if not units and hasattr(signal, \"units\"):\n units = signal.units\n obj = pq.Quantity.__new__(cls, signal, units=units, dtype=dtype,\n copy=copy)\n\n obj.t_start = t_start\n obj.sampling_rate = _get_sampling_rate(sampling_rate, sampling_period)\n\n obj.channel_index = channel_index\n obj.segment = None\n obj.recordingchannelgroup = None\n\n return obj\n\n def __init__(self, signal, units=None, dtype=None, copy=True,\n t_start=0 * pq.s, sampling_rate=None, sampling_period=None,\n name=None, file_origin=None, description=None,\n channel_index=None, **annotations):\n '''\n Initializes a newly constructed :class:`AnalogSignalArray` instance.\n '''\n BaseNeo.__init__(self, name=name, file_origin=file_origin,\n description=description, **annotations)\n\n @property\n def channel_indexes(self):\n '''\n The same as :attr:`channel_index`.\n '''\n return self.channel_index\n\n def __getslice__(self, i, j):\n '''\n Get a slice from :attr:`i` to :attr:`j`.\n\n Doesn't get called in Python 3, :meth:`__getitem__` is called instead\n '''\n return self.__getitem__(slice(i, j))\n\n def __getitem__(self, i):\n '''\n Get the item or slice :attr:`i`.\n '''\n obj = super(BaseAnalogSignal, self).__getitem__(i)\n if isinstance(i, int):\n return obj\n elif isinstance(i, tuple):\n j, k = i\n if isinstance(k, int):\n if isinstance(j, slice): # extract an AnalogSignal\n obj = AnalogSignal(obj, sampling_rate=self.sampling_rate)\n if j.start:\n obj.t_start = (self.t_start +\n j.start * self.sampling_period)\n # return a Quantity (for some reason quantities does not\n # return a Quantity in this case)\n elif isinstance(j, int):\n obj = pq.Quantity(obj, units=self.units)\n return obj\n elif isinstance(j, int): # extract a quantity array\n # should be a better way to do this\n obj = pq.Quantity(np.array(obj), units=obj.units)\n return obj\n else:\n return obj\n elif isinstance(i, slice):\n if i.start:\n obj.t_start = self.t_start + i.start * self.sampling_period\n return obj\n else:\n raise IndexError(\"index should be an integer, tuple or slice\")\n\n def time_slice(self, t_start, t_stop):\n '''\n Creates a new AnalogSignal corresponding to the time slice of the\n original AnalogSignal between times t_start, t_stop. Note, that for\n numerical stability reasons if t_start, t_stop do not fall exactly on\n the time bins defined by the sampling_period they will be rounded to\n the nearest sampling bins.\n '''\n\n # checking start time and transforming to start index\n if t_start == None:\n i = 0\n else:\n t_start = t_start.rescale(self.sampling_period.units)\n i = (t_start - self.t_start) / self.sampling_period\n i = int(np.rint(i.magnitude))\n\n # checking stop time and transforming to stop index\n if t_stop == None:\n j = len(self)\n else:\n t_stop = t_stop.rescale(self.sampling_period.units)\n j = (t_stop - self.t_start) / self.sampling_period\n j = int(np.rint(j.magnitude))\n\n if (i < 0) or (j > len(self)):\n raise ValueError('t_start, t_stop have to be withing the analog \\\n signal duration')\n\n # we're going to send the list of indicies so that we get *copy* of the\n # sliced data\n obj = super(BaseAnalogSignal, self).__getitem__(np.arange(i, j, 1))\n obj.t_start = self.t_start + i * self.sampling_period\n\n return obj\n\n def merge(self, other):\n '''\n Merge the another :class:`AnalogSignalArray` into this one.\n\n The :class:`AnalogSignalArray` objects are concatenated horizontally\n (column-wise, :func:`np.hstack`).\n\n If the attributes of the two :class:`AnalogSignalArray` are not\n compatible, and Exception is raised.\n '''\n assert self.sampling_rate == other.sampling_rate\n assert self.t_start == other.t_start\n other.units = self.units\n stack = np.hstack(map(np.array, (self, other)))\n kwargs = {}\n for name in (\"name\", \"description\", \"file_origin\"):\n attr_self = getattr(self, name)\n attr_other = getattr(other, name)\n if attr_self == attr_other:\n kwargs[name] = attr_self\n else:\n kwargs[name] = \"merge(%s, %s)\" % (attr_self, attr_other)\n if self.channel_index is None:\n channel_index = other.channel_index\n elif other.channel_index is None:\n channel_index = self.channel_index\n else:\n channel_index = np.append(self.channel_index,\n other.channel_index)\n merged_annotations = merge_annotations(self.annotations,\n other.annotations)\n kwargs.update(merged_annotations)\n return AnalogSignalArray(stack, units=self.units, dtype=self.dtype,\n copy=False, t_start=self.t_start,\n sampling_rate=self.sampling_rate,\n channel_index=channel_index,\n **kwargs)\n","sub_path":"core/analogsignalarray.py","file_name":"analogsignalarray.py","file_ext":"py","file_size_in_byte":11882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"440623955","text":"import os\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport utils\nfrom keras.models import load_model\nfrom keras import backend as K\nfrom sklearn.neighbors import NearestNeighbors\nfrom preprocess import preprocess\n\nparser = argparse.ArgumentParser(description='Malconv-keras classifier training')\nparser.add_argument('--save_path', type=str, default='../saved/adversarial_samples', help=\"Directory for saving adv samples\")\nparser.add_argument('--model_path', type=str, default='../saved/malconv.h5', help='Path to target model')\nparser.add_argument('--log_path', type=str, default='../saved/adversarial_log.csv', help=\"[csv file] Adv sample generation log\")\nparser.add_argument('--pad_percent', type=float, default=0.1, help=\"padding percentage to origin file\")\nparser.add_argument('--thres', type=float, default=0.5, help=\"generate adv if origin score below threshold\")\nparser.add_argument('--step_size', type=float, default=0.01, help=\"optimiztion step size for fgsm, senitive\")\nparser.add_argument('--limit', type=float, default=0., help=\"limit gpu memory percentage\")\nparser.add_argument('csv', type=str, help=\"[csv file] Filenames\")\n\ndef fgsm(model, inp, pad_idx, pad_len, e, step_size=0.001):\n adv = inp.copy()\n loss = K.mean(model.output[:, 0])\n grads = K.gradients(loss, model.layers[1].output)[0]\n grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-8)\n \n mask = np.zeros(model.layers[1].output.shape[1:]) # embedding layer output shape\n mask[pad_idx:pad_idx+pad_len] = 1\n grads *= K.constant(mask)\n \n iterate = K.function([model.layers[1].output], [loss, grads])\n g = 0.\n step = int(1/step_size)*10\n for _ in range(step):\n loss_value, grads_value = iterate([adv])\n grads_value *= step_size\n g += grads_value\n adv += grads_value\n #print (e, loss_value, end='\\r')\n if loss_value >= 0.9:\n break\n \n return adv, g, loss_value\n\n \ndef gen_adv_samples(model, fn_list, pad_percent=0.1, step_size=0.001, thres=0.5):\n \n ### search for nearest neighbor in embedding space ###\n def emb_search(org, adv, pad_idx, pad_len, neigh):\n out = org.copy()\n for idx in range(pad_idx, pad_idx+pad_len):\n target = adv[idx].reshape(1, -1)\n best_idx = neigh.kneighbors(target, 1, False)[0][0]\n out[0][idx] = best_idx\n return out\n \n \n max_len = int(model.input.shape[1])\n emb_layer = model.layers[1]\n emb_weight = emb_layer.get_weights()[0]\n inp2emb = K.function([model.input]+ [K.learning_phase()], [emb_layer.output]) # [function] Map sequence to embedding \n \n # Build neighbor searches\n neigh = NearestNeighbors(1)\n neigh.fit(emb_weight)\n \n log = utils.logger()\n adv_samples = []\n\n for e, fn in enumerate(fn_list):\n\n ### run one file at a time due to different padding length, [slow]\n inp, len_list = preprocess([fn], max_len)\n inp_emb = np.squeeze(np.array(inp2emb([inp, False])), 0)\n\n pad_idx = len_list[0]\n pad_len = max(min(int(len_list[0]*pad_percent), max_len-pad_idx), 0)\n org_score = model.predict(inp)[0][0] ### origianl score, 0 -> malicious, 1 -> benign\n loss, pred = float('nan'), float('nan')\n \n if pad_len > 0:\n \n if org_score < thres:\n adv_emb, gradient, loss = fgsm(model, inp_emb, pad_idx, pad_len, e, step_size)\n adv = emb_search(inp, adv_emb[0], pad_idx, pad_len, neigh)\n pred = model.predict(adv)[0][0]\n final_adv = adv[0][:pad_idx+pad_len]\n \n else: # use origin file\n final_adv = inp[0][:pad_idx]\n \n \n log.write(fn, org_score, pad_idx, pad_len, loss, pred)\n \n # sequence to bytes\n bin_adv = bytes(list(final_adv))\n adv_samples.append(bin_adv)\n \n return adv_samples, log\n \n\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n \n # limit gpu memory\n if args.limit > 0:\n utils.limit_gpu_memory(args.limit)\n \n df = pd.read_csv(args.csv, header=None)\n fn_list = df[0].values\n model = load_model(args.model_path)\n \n adv_samples, log = gen_adv_samples(model, fn_list, args.pad_percent, args.step_size, args.thres)\n \n # write to file\n log.save(args.log_path)\n for fn, adv in zip(fn_list, adv_samples):\n _fn = fn.split('/')[-1]\n dst = os.path.join(args.save_path, _fn)\n with open(dst, 'wb') as f:\n f.write(adv)\n","sub_path":"deep_learning/src/gen_adversarial.py","file_name":"gen_adversarial.py","file_ext":"py","file_size_in_byte":4742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"396539421","text":"import mtcnn\nfrom PIL import Image\nfrom numpy import asarray\nimport matplotlib.pyplot as plt\nimport os\n\ndef detect_face(path):\n img = Image.open(path,\"r\")\n if img.mode in (\"RGBA\", \"P\"): img = img.convert(\"RGB\")\n img_array = asarray(img)\n detector = mtcnn.MTCNN()\n face = detector.detect_faces(img_array)\n if (face):\n x,y,w,h = face[0]['box']\n x1 = x - w*0.1 if x - w*0.1 > 0 else 0\n y1 = y - h*0.1 if y - h*0.1 > 0 else 0\n x2 = x1 + w*1.2 if x1 + w*1.2 < img_array.shape[1] else img_array.shape[1]\n y2 = y1 + h*1.2 if y1 + w*1.2 < img_array.shape[0] else img_array.shape[0]\n img_new = img_array[int(y1):int(y2), int(x1):int(x2)]\n img_new = Image.fromarray(img_new)\n return img_new\n else :\n return 0\n\ndef detect_all_image(path):\n train_folder = path+\"train/\"\n val_folder = path+\"val/\"\n\n train_img_list = []\n val_img_list = []\n\n list_dir_train = os.listdir(train_folder)\n list_dir_val = os.listdir(val_folder)\n\n for dir in list_dir_train:\n count=0\n for img_dir in os.listdir(train_folder+\"/\" +dir):\n img_path = train_folder+dir+\"/\"+img_dir\n face = detect_face(img_path)\n if (face):\n train_img_list.append(face)\n face.save(\"../dataAfterDetect/train/\"+dir+str(count)+\".jpg\")\n count+=1\n for dir in list_dir_val:\n count=0\n for img_dir in os.listdir(val_folder+\"/\"+dir):\n img_path = val_folder+dir+\"/\"+img_dir\n face = detect_face(img_path)\n if(face):\n val_img_list.append(face)\n face.save(\"../dataAfterDetect/val/\"+dir+str(count)+\".jpg\")\n count+=1\n\n return train_img_list, val_img_list\n\ntrain_img_list, val_img_list = detect_all_image(\"../Data/\")","sub_path":"main/detectFace.py","file_name":"detectFace.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"71028241","text":"from django.utils import translation\n\nclass LocaleMiddleware(object):\n \"\"\"This middleware checks if we come from a Plone site\n that set a language cookie. In that case we use that\n language\"\"\"\n\n def process_request(self, request):\n forced_lang = request.GET.get('set_language', None)\n request.forced_lang = forced_lang\n if forced_lang:\n translation.activate(forced_lang)\n request.LANGUAGE_CODE = translation.get_language()\n if hasattr(request, 'session'):\n request.session['django_language'] = forced_lang","sub_path":"src/classifieds/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"636826570","text":"import tensorflow as tf\nimport numpy as np\nimport pickle\nimport time\n\nStart_time = time.time()\nimport matplotlib.pyplot as plt\nimport warnings\ntf.compat.v1.enable_eager_execution()\nwarnings.filterwarnings(\"ignore\")\n\n\ntrain_features = pickle.load(open(\"X_train.pickle\", \"rb\"))\nprint(\"X\", len(train_features))\ntrain_labels = pickle.load(open(\"y_train.pickle\", \"rb\"))\nprint(\"y\", len(train_labels), \"Shape\", tf.shape(train_labels))\ntest_features = pickle.load(open(\"X_test.pickle\", \"rb\"))\nprint(\"X\", len(test_features))\ntest_labels = pickle.load(open(\"y_test.pickle\", \"rb\"))\nprint(\"y\", len(test_labels))\nprint(\"DATA LOADED\")\ntest_features = test_features / 255.0\n\nbatch_size = 32\n\ntrain_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_labels))\ntrain_dataset = train_dataset.shuffle(1024).batch(batch_size)\ntest_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_labels))\ntest_dataset = test_dataset.shuffle(1024).batch(batch_size)\n\nrelu_alpha = 0.2\ndropout_rate = 0.0\npadding = 'SAME'\n\n\ndef conv2d(inputs, filters, stride_size):\n out = tf.nn.conv2d(inputs, filters, strides=[1, stride_size, stride_size, 1], padding=padding)\n return tf.nn.relu(out)\n\n\ndef maxpool(inputs, pool_size, stride_size):\n return tf.nn.max_pool2d(inputs, ksize=[1, pool_size, pool_size, 1], padding=padding,\n strides=[1, stride_size, stride_size, 1])\n\n\ndef dense(inputs, weights):\n # x = tf.nn.leaky_relu(tf.matmul(inputs, weights), alpha=relu_alpha)\n x = tf.nn.relu(tf.matmul(inputs, weights))\n return tf.nn.dropout(x, rate=dropout_rate)\n\n\ninitializer = tf.initializers.glorot_uniform()\n\n\ndef get_weight(shape, name, is_training):\n return tf.Variable(initializer(shape), name=name, trainable=True)\n\n\noutput_classes = 3\nshapes = [\n [3, 3, 3, 16],\n [3, 3, 16, 16],\n [3, 3, 16, 32],\n [3, 3, 32, 32],\n [3, 3, 32, 64],\n [3, 3, 64, 64],\n [3, 3, 64, 128],\n [3, 3, 128, 128],\n [3, 3, 128, 256],\n [3, 3, 256, 256],\n [3, 3, 256, 512],\n [3, 3, 512, 512],\n [2048, 800], # weight of flattening layer changes according to size of the image\n # [3600, 2400],\n # [2400, 1600],\n # [512, 800],\n [800, 64],\n [64, output_classes]\n]\n\nshapes1 = [\n [3, 3, 3, 8],\n [3, 3, 8, 16],\n [3, 3, 16, 32],\n [3, 3, 32, 64],\n [3, 3, 64, 128],\n [3, 3, 128, 256],\n [3, 3, 256, 512],\n [3, 3, 512, 1024],\n [3, 3, 1024, 1024],\n [3, 3, 1024, 2048],\n [3, 3, 2048, 2048],\n [3, 3, 2048, 4096],\n [16384, 800], # weight of flattening layer changes according to size of the image\n #[3600, 2400],\n #[2400, 1600],\n #[512, 800],\n [800, 64],\n [64, output_classes]\n]\n\nweights = []\n#for i in range(len(shapes1)):\n# weights.append(get_weight(shapes1[i], 'weight{}'.format((i)), True))\n\ndef model(x):\n x = tf.cast(x, dtype=tf.float32)\n c1 = conv2d(x, weights[0], stride_size=1)\n c1 = conv2d(c1, weights[1], stride_size=1)\n p1 = maxpool(c1, pool_size=2, stride_size=2)\n\n c2 = conv2d(p1, weights[2], stride_size=1)\n c2 = conv2d(c2, weights[3], stride_size=1)\n p2 = maxpool(c2, pool_size=2, stride_size=2)\n\n c3 = conv2d(p2, weights[4], stride_size=1)\n c3 = conv2d(c3, weights[5], stride_size=1)\n p3 = maxpool(c3, pool_size=2, stride_size=2)\n\n c4 = conv2d(p3, weights[6], stride_size=1)\n c4 = conv2d(c4, weights[7], stride_size=1)\n p4 = maxpool(c4, pool_size=2, stride_size=2)\n\n c5 = conv2d(p4, weights[8], stride_size=1)\n c5 = conv2d(c5, weights[9], stride_size=1)\n p5 = maxpool(c5, pool_size=2, stride_size=2)\n\n c6 = conv2d(p5, weights[10], stride_size=1)\n c6 = conv2d(c6, weights[11], stride_size=1)\n p6 = maxpool(c6, pool_size=2, stride_size=2)\n\n flatten = tf.reshape(p6, shape=(tf.shape(p6)[0], -1))\n\n # d1 = dense(flatten, weights[12])\n # d2 = dense(d1, weights[13])\n # d3 = dense(d2, weights[14])\n d4 = dense(flatten, weights[12])\n d5 = dense(d4, weights[13])\n logits = tf.matmul(d5, weights[14])\n\n return tf.nn.softmax(logits)\n\n\noptimizer = tf.compat.v2.optimizers.SGD()\ntrain_loss = tf.compat.v2.metrics.Mean()\ntrain_accuracy = tf.compat.v2.metrics.SparseCategoricalAccuracy()\ntest_loss = tf.compat.v2.metrics.Mean()\ntest_accuracy = tf.compat.v2.metrics.SparseCategoricalAccuracy()\nloss_object = tf.compat.v2.losses.SparseCategoricalCrossentropy()\n\ndef train_step(images, labels):\n global weights\n weights = []\n for i in range(len(shapes)):\n weights.append(get_weight(shapes[i], 'weight{}'.format(i), True))\n with tf.GradientTape() as tape:\n predictions = model(images, True)\n loss = loss_object(labels, predictions)\n grads = tape.gradient(loss, weights)\n optimizer.apply_gradients(zip(grads, weights))\n train_loss(loss)\n train_accuracy(labels, predictions)\n\n\ndef test_step(images, labels):\n global weights\n weights = []\n for i in range(len(shapes)):\n weights.append(get_weight(shapes[i], 'weight{}'.format(i), False))\n predictions = model(images, False)\n t_loss = loss_object(labels, predictions)\n test_loss(t_loss)\n test_accuracy(labels, predictions)\n\n\nnum_epochs = 200\n\nTrain_loss =[]\nTrain_accuracy =[]\nTest_loss = []\nTest_accuracy =[]\n\ndef train_and_test(EPOCHS):\n weights = []\n for e in range(EPOCHS):\n print('Epoch {} out of {} {}'.format(e + 1, num_epochs, '--' * 20))\n for images, labels in train_dataset:\n train_step(tf.cast(images, tf.float32), labels)\n for test_images, test_labels in test_dataset:\n test_step(tf.cast(test_images, tf.float32), test_labels)\n\n print(\"Average Loss = {:.4f}\".format(train_loss.result()))\n Train_loss.append(train_loss.result())\n print(\"Avrage Accuracy = {:.3f}%\".format(train_accuracy.result() * 100))\n Train_accuracy.append(train_accuracy.result() * 100)\n print(\"Test Average Loss = {:.4f}\".format(test_loss.result()))\n Test_loss.append(test_loss.result())\n print(\"Test Avrage Accuracy = {:.3f}%\".format(test_accuracy.result() * 100))\n Test_accuracy.append( test_accuracy.result() * 100)\n\ntf.executing_eagerly()\n\nfor i in range(10):\n print(\"TRIAL\", (i + 1), \"----------------------------------------------------------\")\n train_and_test(num_epochs)\n plt.figure(1)\n plt.title('TRAIN ACCURACY')\n plt.plot(Train_accuracy)\n plt.figure(2)\n plt.title('TRAIN LOSS')\n plt.plot(Train_loss)\n plt.figure(3)\n plt.title('TEST ACCURACY')\n plt.plot(Test_accuracy)\n plt.figure(4)\n plt.title('TEST LOSS')\n plt.plot(Test_loss)\n print(\"Maximum Training Accuracy {:.3f}%\".format((np.amax(Train_accuracy))))\n print(\"Maximum Testing Accuracy {:.3f}%\".format((np.amax(Test_accuracy))))\n Train_accuracy = []\n Train_loss = []\n Test_accuracy = []\n Test_loss = []\nprint(\"EXECUTION TIME: \", int((time.time() - Start_time) // 3600), \":\",\n int((time.time() - Start_time) % 3600 // 60), \":\",\n int((time.time() - Start_time) % 3600 % 60))\nplt.xlabel('Epochs')\nplt.ylabel('Accuracy')\nplt.show()\n","sub_path":"CNN_2.py","file_name":"CNN_2.py","file_ext":"py","file_size_in_byte":7092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"357211371","text":"from mlflow import log_metric, log_param, log_artifacts\nimport mlflow.sklearn\nfrom mlops_pipeline.modelisation import get_data, get_params, make_model, preprocess_data, split_data, get_scores\nfrom mlops_pipeline import get_commit\n\n\ndef run_model_safe(file):\n try:\n model, score_cv, accuracy = run_model(file)\n\n except Exception as e:\n with open(\"outputs/exception.txt\", \"w\") as f:\n f.write(f\"Got an error message on the run : {e}\")\n log_artifacts(\"outputs\")\n model = None\n score_cv = None\n accuracy = None\n mlflow.end_run()\n\n return model, score_cv, accuracy\n\n\ndef run_model(file):\n\n mlflow.start_run(run_name=file)\n\n # Get data - log file name\n X, Y = get_data(file)\n X = preprocess_data(X)\n X_train, X_test, Y_train, Y_test, seed = split_data(X, Y)\n log_param(\"seed\", seed)\n\n # Get params - log them\n param_gamma, param_C = get_params()\n log_param(\"gamma\", param_gamma)\n log_param(\"C\", param_C)\n\n # Run model\n model, score_cv = make_model(X_train, Y_train, param_gamma, param_C)\n log_metric(\"score_cv\", score_cv)\n\n accuracy = get_scores(model, X_test, Y_test)\n log_metric(\"accuracy\", accuracy)\n\n # Save model into mlflow / make it accesible\n mlflow.sklearn.log_model(model, \"model\")\n\n r = get_commit('/home/melanie/Documents/code/mlops_pipeline')\n with open(\"outputs/git_info.txt\", \"w\") as f:\n f.write(f\"code used is project MLOPS_PIPELINE, commit {r}\")\n\n with open(\"outputs/test.txt\", \"w\") as f:\n f.write(\"Any kind of log on the the run should end up here to be easily accessible to any one, images, ...\")\n log_artifacts(\"outputs\")\n\n mlflow.end_run()\n\n return model, score_cv, accuracy\n\n\n\n\n","sub_path":"mlops_pipeline/mlflow_follow_up.py","file_name":"mlflow_follow_up.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"624125935","text":"from combination import Combination\nimport random\n\ndef schuiven(combinatie):\n attempts = 0\n maxattempts = 100\n while attempts < maxattempts:\n # kies een willekeurig huis (shuffle array en dan de 1e)\n random.shuffle(combinatie.houses)\n # bereken extra vrijstand van huis\n temp = combinatie.geefVrijstand(combinatie.houses[0], 0)\n if temp > 0:\n huis = combinatie.houses[0]\n hoek = combinatie.houses[0].hoekpunt\n # kies plaats om naar toe te schuiven\n newx = random.randint(hoek.x - temp, hoek.x + temp)\n newy = random.randint(hoek.y - temp, hoek.y + temp)\n while newx - huis.minVrij < 0 or newx + huis.width + huis.minVrij > combinatie.map.width:\n newx = random.randint(hoek.x - temp, hoek.x + temp)\n while newy - huis.minVrij < 0 or newy + huis.length + huis.minVrij > combinatie.map.length:\n newy = random.randint(hoek.y - temp, hoek.y + temp)\n # indien mogelijk: verplaats\n mogelijk = True\n for i in range(1, len(combinatie.houses)):\n xH2 = combinatie.houses[i].hoekpunt.x\n yH2 = combinatie.houses[i].hoekpunt.y\n #check if H2 left and right corners are within H1's x-range\n if (newx - huis.minVrij <= xH2 <= newx + huis.width + huis.minVrij) or (newx - huis.minVrij <= xH2+combinatie.houses[i].width <= newx + huis.width + huis.minVrij):\n #check if H2 top and bottom corners are within H1's y-range\n if (newy - huis.minVrij <= yH2 <= newy + huis.length + huis.minVrij) or (newy - huis.minVrij <= yH2+combinatie.houses[i].length <= newy + huis.length + huis.minVrij):\n mogelijk = False\n #check if H1 left and right corners are within H2's x-range\n if (xH2 - combinatie.houses[i].minVrij <= newx <= xH2 + combinatie.houses[i].width + combinatie.houses[i].minVrij) or (xH2 - combinatie.houses[i].minVrij <= newx+huis.width <= xH2 + combinatie.houses[i].width + combinatie.houses[i].minVrij):\n #check if H1 top and bottom corners are within H2's y-range\n if (yH2 - combinatie.houses[i].minVrij <= newy <= yH2+combinatie.houses[i].length + combinatie.houses[i].minVrij) or (yH2 - combinatie.houses[i].minVrij <= newy+huis.length <= yH2 + combinatie.houses[i].length + combinatie.houses[i].minVrij):\n mogelijk = False\n # checken van water om 'kruisingen' te voorkomen\n # betekent dat beide delen wel in elkaars bereik liggen, maar de punten zelf niet.\n if mogelijk == True:\n huis.hoekpunt.setPoint(newx,newy)\n return True\n else:\n # indien geen: opnieuw huis kiezen\n pass\n attempts += 1\n # bereken waarde; indien hoger, bewaren en opnieuw. Anders verwerpen en opnieuw\n return False\n","sub_path":"schuiven.py","file_name":"schuiven.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"507626606","text":"# coding: utf-8\nimport re\nimport os\nimport logging\nimport requests\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\nimport xml.etree.ElementTree as ET\nimport time\n\nfrom django.conf import settings\n\nfrom ..models import Process, Error\n\n#Initialisation des logs\nlogger = logging.getLogger(__name__)\n\ndef test_localisation(librairies,rcr):\n for library in librairies:\n if rcr == library.attrib['rcr'] :\n return True\n return False\n\ndef exist_in_sudoc(ppns_list,process):\n \"\"\"Teste pour une liste de PPN et un RCR données si une localisation existe dans le SUDOC\n\n Args:\n ppns_list (array): liste de ppn\n process (objec): traitement pour lequel la liste doit être traitée conctient le rcr process.process_library.library_rcr\n \"\"\"\n \n rcr = process.process_library.library_rcr\n logger.info(\"Thread {} début\".format(ppns_list))\n # Préparation et envoie de la requête à l'ABES\n session = requests.Session()\n retry = Retry(connect=3, backoff_factor=0.5)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('https://', adapter)\n r = session.request(\n method='GET',\n headers= {\n \"User-Agent\": \"outils_biblio/0.1.0\",\n \"Accept\": \"application/xml\"\n },\n url= 'https://www.sudoc.fr/services/where/15/{}.xml'.format(','.join(ppns_list)))\n try:\n r.raise_for_status()\n except requests.exceptions.HTTPError:\n logger.error(\"{} :: alma_to_sudoc :: HTTP Status: {} || Method: {} || URL: {} || Response: {}\".format(','.join(ppns_list), r.status_code, r.request.method, r.url, r.text))\n # Si le service ne répond pas pour la requête on créé une erreur pour chaque PPN\n for ppn in ppns_list :\n error = Error( error_ppn = ppn,\n error_type = 'ERREUR_REQUETE',\n error_process = process) \n error.save() \n #Traitement des résultats\n else:\n ppns_requetes = [] \n ppns_connus =[] #Liste des ppns retrouvés par le web service\n results = r.content.decode('utf-8')\n root = ET.fromstring(results)\n #Pour chaque résultat \n for result in root.findall(\".//result\"):\n # On récupère le PPN nettoyé\n ppn = result.attrib['ppn']\n # On l'ajoute à la liste des ppns retrouvés par le web service\n ppns_connus.append(ppn)\n # On regarde si une localisation existe pour le PPN \n is_located = test_localisation(result.findall(\".//library\"),rcr)\n if is_located :\n logger.debug(\"{} :: Existe\".format(ppn))\n else :\n error = Error( error_ppn = ppn,\n error_type = 'LOC_INCONNUE_SUDOC',\n error_process = process)\n error.save()\n logger.debug(\"{} :: N'Existe pas\".format(ppn))\n # On identifie les ppns inconnus du SUDOC\n","sub_path":"sudoc/services/alma_to_sudoc.py","file_name":"alma_to_sudoc.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"190196249","text":"\n\ndef time_limited(timeout=0.5, return_val=np.nan, use_sigalrm=True):\n '\\n Decorator for setting a timeout for pure-Python functions.\\n\\n If the function does not return within `timeout` seconds, the\\n value `return_val` is returned instead.\\n\\n On POSIX this uses SIGALRM by default. On non-POSIX, settrace is\\n used. Do not use this with threads: the SIGALRM implementation\\n does probably not work well. The settrace implementation only\\n traces the current thread.\\n\\n The settrace implementation slows down execution speed. Slowdown\\n by a factor around 10 is probably typical.\\n '\n if (POSIX and use_sigalrm):\n\n def sigalrm_handler(signum, frame):\n raise TimeoutError()\n\n def deco(func):\n\n def wrap(*a, **kw):\n old_handler = signal.signal(signal.SIGALRM, sigalrm_handler)\n signal.setitimer(signal.ITIMER_REAL, timeout)\n try:\n return func(*a, **kw)\n except TimeoutError:\n return return_val\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n signal.signal(signal.SIGALRM, old_handler)\n return wrap\n else:\n\n def deco(func):\n\n def wrap(*a, **kw):\n start_time = time.time()\n\n def trace(frame, event, arg):\n if ((time.time() - start_time) > timeout):\n raise TimeoutError()\n return None\n sys.settrace(trace)\n try:\n return func(*a, **kw)\n except TimeoutError:\n sys.settrace(None)\n return return_val\n finally:\n sys.settrace(None)\n return wrap\n return deco\n","sub_path":"Data Set/bug-fixing-1/0ddd3737f103a8861b74d6e55f5404fd32377a0e--bug.py","file_name":"0ddd3737f103a8861b74d6e55f5404fd32377a0e--bug.py","file_ext":"py","file_size_in_byte":1850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"225543685","text":"\nimport tweepy, time, sys, random\nfrom random import randint\n \nargfile = str(\"liners.txt\")\n \n\n \n#enter the corresponding information from your Twitter application:\nCONSUMER_KEY = ''#keep the quotes, replace this with your consumer key\nCONSUMER_SECRET = ''#keep the quotes, replace this with your consumer secret key\nACCESS_KEY = ''#keep the quotes, replace this with your access token\nACCESS_SECRET = ''#keep the quotes, replace this with your access token secret\nauth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\nauth.set_access_token(ACCESS_KEY, ACCESS_SECRET)\napi = tweepy.API(auth)\n \nfilename=open(argfile,'r')\nf=filename.readlines()\nfilename.close()\n \nwhile True:\n for line in f:\n curtime= time.strftime(\"%H:%M:%S\")\n curdate= time.strftime(\"%x\")\n api.update_status(\"{0}---{1}, {2}\".format(curdate,curtime,line)) \n TimeToSleep = randint(780,1200)\n time.sleep(TimeToSleep)#Tweet every 15 minutes","sub_path":"Twit.py","file_name":"Twit.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"79990907","text":"\"\"\"GateTemplate URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Add an import: from blog import urls as blog_urls\n 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls))\n\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\n\nurlpatterns = [\n\n url(r'^$', 'gate.views.login_page'), #index\n url(r'^login/$', 'gate.views.login_page'), #Login\n url(r'^logout/$', 'gate.views.logout_page'), #Logout\n url(r'^enter/$', 'gate.views.enter'), #EnterScript\n url(r'^main/$', 'gate.views.load', {'page': 'main'}), #MainPage\n url(r'^addtiket/$', 'gate.views.load', {'page': 'addtiket'}), #AddPage\n url(r'^report/$', 'gate.views.load', {'page': 'report'}), #ReportPage\n url(r'^category/$', 'gate.views.load', {'page': 'category'}), #CategoryPage\n url(r'^users/$', 'gate.views.load', {'page': 'users'}), #UsersPage\n\n #Temp\n url(r'^add_tiket/$', 'gate.views.add_tiket'), #AddTiket\n\n #Admin\n url(r'^admin/', include(admin.site.urls)),\n\n #API\n url(r'^(?P\\w+)/api/category/$', 'gate.api.category'),\n url(r'^(?P\\w+)/api/item/$', 'gate.api.item'),\n url(r'^(?P\\w+)/api/report/$', 'gate.api.report'),\n url(r'^(?P\\w+)/api/status/(?P\\d+)/$', 'gate.api.status'),\n]\n","sub_path":"GateTemplate/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"507872619","text":"\n'''\nhttps://www.reddit.com/r/dailyprogrammer/comments/pih8x/easy_challenge_1/\n\nCreate a program that will ask the users name, age, and reddit username. have it tell them the information back, in the format:\n\nYour name is (blank), you are (blank) years old and your username is (blank).\n\nFor extra credit, have the program log this information in a file to be accessed later.\n\n'''\n\nnome = input(\"Qual o seu nome? \")\nidade = input(\"Qual sua idade? \")\nreddit = input(\"Qual o seu nick no Reddit? \")\n\nprint(\"Your name is {}, you are {} years old and your username is {}\".format(nome, idade, reddit))\n","sub_path":"easy/ch001.py","file_name":"ch001.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"364682269","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec 31 12:55:41 2016\n\n@author: gopan\n\"\"\"\n\n\nimport numpy as np\nimport pandas as pd\nimport scipy\nimport statsmodels.api as sm\n\n\"\"\"\nIn this optional exercise, you should complete the function called \npredictions(turnstile_weather). This function takes in our pandas \nturnstile weather dataframe, and returns a set of predicted ridership values,\nbased on the other information in the dataframe. \n\nIn exercise 3.5 we used Gradient Descent in order to compute the coefficients\ntheta used for the ridership prediction. Here you should attempt to implement \nanother way of computing the coeffcients theta. You may also try using a reference implementation such as: \nhttp://statsmodels.sourceforge.net/devel/generated/statsmodels.regression.linear_model.OLS.html\n\nOne of the advantages of the statsmodels implementation is that it gives you\neasy access to the values of the coefficients theta. This can help you infer relationships \nbetween variables in the dataset.\n\nYou may also experiment with polynomial terms as part of the input variables. \n\nThe following links might be useful: \nhttp://en.wikipedia.org/wiki/Ordinary_least_squares\nhttp://en.wikipedia.org/w/index.php?title=Linear_least_squares_(mathematics)\nhttp://en.wikipedia.org/wiki/Polynomial_regression\n\nThis is your playground. Go wild!\n\nHow does your choice of linear regression compare to linear regression\nwith gradient descent computed in Exercise 3.5?\n\nYou can look at the information contained in the turnstile_weather dataframe below:\nhttps://s3.amazonaws.com/content.udacity-data.com/courses/ud359/turnstile_data_master_with_weather.csv\n\ndef predictions(weather_turnstile):\n #\n # Your implementation goes here. Feel free to write additional\n # helper functions\n # \n # R2 is too low 0.035 in place of 0.4. Try get_dummies \n features = weather_turnstile[['rain', 'precipi', 'Hour', 'fog', 'meanwindspdi', 'mintempi','meantempi']]\n features = np.array(features)\n features = sm.add_constant(features) \n features = (features - np.mean(features))/ np.std(features)\n values = weather_turnstile['ENTRIESn_hourly']\n model = sm.OLS(values, features)\n fit = model.fit()\n prediction = np.dot(features, fit.params)\n return prediction\n\n\"\"\"\ndef calc_r2(prediction, values):\n sse = ((values - prediction)**2).sum()\n ssto = ((values - np.mean(values))**2).sum()\n r_squared = 1 - sse / ssto\n return r_squared\n\nweather_turnstile = pd.read_csv('turnstile_data_master_with_weather.csv')\nfeatures = weather_turnstile[['rain', 'precipi', 'Hour', \n 'fog', 'meanwindspdi', 'meantempi']]\nfeatures['rain2'] = weather_turnstile['rain']**2\n#features = weather_turnstile[['rain', 'precipi', 'Hour']] #0.029\n\ndummy_units = pd.get_dummies(weather_turnstile['UNIT'], prefix='unit')\nfeatures = features.join(dummy_units)\n\nsigma = features.std()\nfeatures = (features - features.mean())/ sigma\nif (sigma == 0).any():\n raise Exception(\"featue(s) have same values => std = 0\")\nvalues = weather_turnstile['ENTRIESn_hourly']\n\nfeatures = sm.add_constant(features) \n# features['ones'] = np.ones(len(values))\nfeatures = np.array(features)\nvalues = np.array(values)\n\nmodel = sm.OLS(values, features)\nfit = model.fit()\nprediction = np.dot(features, fit.params)\n\nr_squared = calc_r2(prediction, values)\nprint('R^2', r_squared)\n\n\nX = features\nbetaHat = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(values)\nprediction2 = np.dot(features, betaHat)\nr_squared = calc_r2(prediction2, values)\nprint('HAT:R^2', r_squared)\n\n'''\nin betHat [(X'X)^-1 X'] is the Moore–Penrose pseudoinverse of X. \nAlthough this equation is correct, and can work in many applications, \nit is not computationally efficient to invert the \nnormal equations matrix (the Gramian matrix). An exception occurs \nin numerical smoothing and differentiation where an analytical \nexpression is required. \nUse Cholesky decomposition, QR decomposition or SVD.\nOrthogonal decomposition methods of solving the least squares problem \nare slower than the normal equations method but are more \nnumerically stable because they avoid forming the product X'X.\n\nSVD method is the most computationally intensive, but is particularly \nuseful if the normal equations matrix, X'X, is very ill-conditioned \n(i.e. if its condition number multiplied by the machine's relative \nround-off error is appreciably large). In that case, including the \nsmallest singular values in the inversion merely adds numerical noise\nto the solution. This can be cured with the truncated SVD approach, \ngiving a more stable and exact answer, by explicitly setting to zero\nall singular values below a certain threshold and so ignoring them,\na process closely related to factor analysis.\n'''\n##################################\nimport numpy as np\nimport matplotlib.pyplot as plt\ninp = np.array([\n [1, 6],\n [2, 5],\n [3, 7],\n [4, 10]\n])\nm = len(inp)\nX = np.array([np.ones(m), inp[:, 0]]).T\ny = np.array(inp[:, 1]).reshape(-1, 1)\nbetaHat = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\nprint(betaHat)\nplt.figure(1)\nxx = np.linspace(0, 5, 2)\nyy = np.array(betaHat[0] + betaHat[1] * xx)\nplt.plot(xx, yy.T, color='b')\nplt.scatter(inp[:, 0], inp[:, 1], color='r')\nplt.show()\n\n\n","sub_path":"statsmodel1.py","file_name":"statsmodel1.py","file_ext":"py","file_size_in_byte":5271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"571262795","text":"# -*- coding: utf-8 -*-\n# https://bottlepy.org/docs/dev/tutorial.html\n# https://habrahabr.ru/post/221659/\n# https://habrahabr.ru/post/250831/\n# https://github.com/MicrosoftArchive/redis/releases\n\n\n# Базовая защита от дурака - всё, что в ключах не англоязычные буквы/цифры - заменяем на \"_\", что при обращении, что при записи. \n# Лучше вынести в отдельную функцию\n# 1. Реализовать на главной счетчик обращений, который будет храниться в Redis и увеличиваться при каждом заходе на главную\n# 2. Реализовать добавление значения\n# 3. Реализовать получение значения\n# 4. Реализовать вывод списка ключей\n# 5. Реализовать по аналогии удаление ключей - по ссылке /del/, в index() добавлены header, footer - куда можно будет писать что-то своё\n\n\nimport webbrowser\nfrom bottle import Bottle, run, request, template, get, post\nfrom redis import Redis\nimport re \n\nHOST = '127.0.0.1'\nPORT = '54321'\n\napp = Bottle()\nr = Redis(decode_responses=True) #перевод значений в код строки\n\ndef protection(key):\n return re.sub(r'\\W', '_', key, flags=re.A) \n\n@app.get('/')\ndef index():\n counter = r.incr(\"visitors\")\n vars = {'counter':counter,\n 'header':'Welcome!',\n 'footer':'Good Luck!'}\n return template('static/index.html', vars)\n\n@app.post('/set/')\ndef set_key():\n key = request.forms.get('key')\n value = request.forms.get('value')\n key = protection(key)\n r.set(key, value)\n response = \"added key: %s
value: %s\" % (key,value)\n return template('% rebase(\"static/index.html\")\\n'+response)\n\n@app.get('/get/')\ndef get_key(key):\n value = r.get(key)\n response = 'key: %s
value: %s'%(key,value)\n return template('% rebase(\"static/index.html\")\\n'+response)\n\n@app.get('/list')\ndef list_keys():\n keys_list = r.keys()\n response = ''\n for key in keys_list:\n response += '

%s
'%(key,key)\n return template('% rebase(\"static/index.html\")\\n'+response)\n\n@app.get('/del/')\ndef delete_key(key):\n key = protection(key)\n if r.exists(key):\n r.delete(key)\n response = \"deleted key: {}\".format(key)\n else:\n response = \"not key\"\n return template('% rebase(\"static/index.html\")\\n'+response)\n\nif __name__ == \"__main__\":\n webbrowser.open('http://%s:%s'%(HOST, PORT))\n run(app, host=HOST, port=PORT, reloader=True, debug=True)\n","sub_path":"2018/04/04_Kudrevatykh.py","file_name":"04_Kudrevatykh.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"411360491","text":"\"\"\"Jasper Yun - Student ID: 1731131\nExercise 2: Modular Arithmetic\nThis program is used to calculate the quotients and remainders of two integers given to the program by the user.\nThere is a menu option to choose different methods to calculate a mod n, or to exit the program.\"\"\"\n\n#Question 1:\n\"\"\"This function takes an input of two integers, a and d, and divides them to find the\nquotient and remainder. The division is performed by subtracting d from a until a < d. If a < 0,\nthen the division uses a + d until a > 0.\nThe output of the function is the quotient and remainder of the division given by q, r.\"\"\"\n\ndef Quotient(a,d):\n r=a\n q=0\n while (r>=d): ##if r >= d, we can keep subtracting r - d until r < d, at which point we will be finished the division\n r=r-d\n q=q+1\n while (r<0): ##if r < 0, then we need to keep adding r + d until r > 0, at which point we will necessarily have 0 <= r < d, and we will be done dividing\n r=r+d\n q=q-1\n return q,r ##I chose not to return a tuple so that I could make the remainder (result of menu option 3) more salient\n\n\n\n#Question 2:\n\"\"\"This function takes an input of integers a, d, and q. It divides a and d to find the\nquotient and remainder recursively. The input q is the quotient, which is set to zero initially.\nIf the input is a >= 0, then there are two cases:\n (1) a < d, then the function will return (q, a);\n (2) a >= d, then the function will call itself and perform the division using recursive subtraction.\nIf the input is a < 0, then there are also two cases:\n (1) a > d, then the function will return (q, a);\n (2) a <= d, then the function will call itself and perform the division using recursive addition.\nThe condition is that d > 0, since d != 0 and my function will not return the proper result if d < 0.\nThe outut is the quotient and remainder, given by q, a.\"\"\"\n\ndef RecursiveQuotient(a,d, q=0):\n if a >= 0:##for positive integer inputs\n if a < d:\n return (q,a) ##I return a tuple here because I only need the recursive function for option 2 of the menu, which needs to output quotient and remainder, (q, r)\n else:\n return RecursiveQuotient(a - d, d, q + 1)\n elif a < 0: ##for negative integer inputs\n if a > 0: ##a>0 will only occur once a + d is performed enough times \n return (q, a)\n else:\n return RecursiveQuotient(a + d, d, q - 1)\n\n\n\n###Extra function that I defined to make question 3 slightly easier\n\"\"\"This function is used to loop back the menu options. It technically does not have any parameters.\nIt provides the user with an option to return to the menu or exit the program based on user input.\nEntering 1 calls the Menu() function. Inputting 2 exits the program.\"\"\"\n\ndef Loop():\n print(\"\")\n print(\"Alright! That was easy, wasn't it?\")\n print(\"Here are two more options for you now:\")\n ans = int(input(\"Enter 1 if you would like to return to the menu. Enter 2 if you would like to exit the program. \"))\n while not ((ans==1) or (ans==2)): ##To prevent an input that is not 1 or 2\n ans = int(input(\"You must choose an option by entering 1 or 2. \"))\n if (ans == 1):\n Menu()\n if (ans == 2):\n print(\"\")\n print(\"Have a nice day! Goodbye!\")\n\n\n\n#Question 3:\n\"\"\"This function is a menu to choose how divide two integers, a and d. It does not have any parameters, but to continue the program the user needs to enter a value for \"choice\", either 1, 2, 3, or 4. These choices will lead the program to using\neither the standard quotient method defined in question 1, the recursive quotient defined in question 2, reducing\nan integer into a modulo n, or exiting the program. This function combines the two functions above, and the choices commence\none of the above functions.\"\"\"\n\ndef Menu():\n print(\"\") #Some blank space\n print(\"Here are your menu options:\")\n print(\"Enter 1 if you would like to divide two integers using the standard quotient.\")\n print(\"Enter 2 if you would like to use the recursive quotient.\")\n print(\"Enter 3 if you would like to reduce an integer a modulo n.\")\n print(\"Enter 4 if you would like to exit the program.\")\n choice = input(\"Please choose an option now. \")\n \n while not ((choice==\"1\") or (choice==\"2\") or (choice==\"3\") or (choice==\"4\")): ##To prevent the program from crashing if the input is not 1,2,3, or 4.\n choice = input(\"You must choose an option by inputting 1, 2, 3, or 4. \")\n choice = int(choice) ##change the input into an integer since I did not specify that d was an integer above so that I could properly deal with non-integer inputs for \"choice\" and prevent the program from crashing\n\n if (choice == 1): \n print(\"\")\n print(\"Great choice. Let's begin.\")\n a = int(input(\"Please choose an integer for a. \"))\n d = int(input(\"Please choose a nonzero integer for d to divide a. \"))\n while (d == 0 or d<0): ##to prevent errors: (1) we cannot divide by 0; (2) my function will not run properly if d < 0; (3) the assignment instructions also indicate that d > 0\n if d == 0:\n d = int(input(\"Hey! You should know that we do not divide by zero. Please choose another integer for d: \"))\n if d<0:\n d = int(input(\"Unfortunately, dividing by a negative number is not currently supported. Please choose another integer for d: \"))\n q,r = Quotient(a,d)\n print(\"The quotient and remainder of\", a,\"and\", d, \"are given by (q,r) = (\", q,\",\",r,\").\")\n Loop() ##to allow the user to loop back to the menu or quit\n\n if (choice == 2):\n print(\"\")\n print(\"Ahh, a bold move that is, using recursion. I like it!\")\n print(\"Let's begin with choosing the values.\")\n a = int(input(\"Please choose an integer for a. \"))\n d = int(input(\"Please choose a nonzero positive integer for d to divide a. \"))\n while (d==0 or d<0): ##to prevent errors: (1) we cannot divide by zero; (2) the function will not run properly if d < 0; (3) the assignment only requires that a, d > 0\n if d==0:\n d = int(input(\"Hey! You should remember that we cannot divide by zero. Please choose a different integer for d. \"))\n if d<0:\n d = int(input(\"Unfortunately, dividing by a negative integer is not currently supported by this program. Please choose a positive integer for d. \"))\n q,r = RecursiveQuotient(a,d)\n print(\"The quotient and remainder of\", a,\"and\", d, \"are given by (q,r) = (\", q,\",\",r,\").\")\n Loop()\n\n if (choice == 3):\n print(\"\")\n print(\"Alright! Let me do the work for you.\")\n a = int(input(\"Please choose the integer that you would like to reduce. \"))\n d = int(input(\"Please choose the modulus you would like to reduce in. \"))\n while (d==0 or d<0): ##to prevent errors: (1) we cannot divide by zero; (2) the function will not run properly if d < 0\n if d==0:\n d = int(input(\"Hey! You should remember that we cannot divide by zero. Please choose a different integer for d. \"))\n if d<0:\n d = int(input(\"Unfortunately, negative moduli are not currently supported by this program. Please choose a positive integer for d. \"))\n q,r = Quotient(a,d)\n print(\"The quotient and remainder of\", a,\"and\", d, \"are given by (q,r) = (\", q,\",\",r,\").\")\n print(\"Therefore,\", a, \"mod\", d, \"reduces to\", r,\".\")\n Loop()\n\n if (choice == 4):\n print(\"\")\n print(\"Have a nice day! Goodbye!\")\n\n\n\n##Some text to introduce the program before the menu options; they are placed here rather than in the Loop() so that they do not keep appearing every time we loop\nprint(\"Hello! Welcome to my modular arithmetic calculator.\")\nprint(\"This program will allow you to calculate the quotient and remainder of two values, a and d, when dividing a by d. You will choose a and d.\")\nprint(\"You may choose two different methods to calculate the quotient, or reduce an integer a modulo n.\")\nprint(\"\") #provide a line break before the menu options\n \nMenu() #to run the program\n","sub_path":"Assignments/A2-ModularArithmetic.py","file_name":"A2-ModularArithmetic.py","file_ext":"py","file_size_in_byte":8189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"197817302","text":"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport json\nimport pickle\nfrom PIL import Image\nimport streamlit.components.v1 as components\nimport xgboost\nimport shap\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LinearSegmentedColormap\nimport os\n\n#Setting the page configuration\nsquare_icon = Image.open(os.path.abspath('Project/streamlit/images/skincare_square.jpeg'))\nlong_icon = Image.open(os.path.abspath('Project/streamlit/images/top_banner.png')) \nlong_bw = Image.open(os.path.abspath(\"Project/streamlit/images/bw_long.jpeg\"))\nsquare_logo = Image.open(os.path.abspath(\"Project/streamlit/images//teen_beauty.png\"))\nlogo = Image.open(os.path.abspath(\"Project/streamlit/images//logo_trans.png\"))\nend_icon = Image.open(os.path.abspath(\"Project/streamlit/images//lower_banner.png\"))\nst.set_page_config(\n page_title=\"Product and Ingredient Analysis\",\n page_icon=square_logo,\n layout=\"centered\",\n initial_sidebar_state=\"auto\")\n\n#loading necessary files\n@st.cache\ndef fetch_data(path):\n df = pd.read_json(path)\n return df\n\nprod_ingr_matrix1 = fetch_data('Project/data/processed_data/product_ingr_inventory.json')\ndot_prod= fetch_data('../data/processed_data/common_ingr.json')\nsim_df= fetch_data('../data/processed_data/cos_sim.json')\ndf_new = fetch_data('../data/processed_data/combined_data.json')\nx_complete = fetch_data('../data/train_test/x_complete.json')\ndf1 = fetch_data('../data/processed_data/pre_modelling_data.json')\n\n#st.write(df1.head())\n\n#functions \n@st.cache\ndef similar_prod(item, n=10, all_types = True):\n '''\n return the n most similar products\n '''\n tp = sim_df[item].sort_values(axis=0, ascending=False)\n tp = pd.DataFrame(tp)[0:n+1]\n tp.rename(columns={item:'cos_score'}, inplace=True)\n tp['cos_score']= np.round(tp['cos_score']*100,2)\n for i in list(tp.index):\n tp.loc[i, 'jac_score'] = np.round(jaccard_binary(np.array(prod_ingr_matrix1.loc[item]), np.array(prod_ingr_matrix1.loc[i]))*100,2)\n tp.loc[i, 'num_ingr'] = str(dot_prod[item][i])+'/'+ str(dot_prod[item][item])\n tp.loc[i, 'pricepervol'] = np.round(df_new[df_new['product_name']==i]['pricepervol'].values[0],2)\n tp.loc[i, 'product_type'] = df_new[df_new['product_name']==i]['product_type'].values[0]\n \n if all_types==True:\n return tp \n else:\n ptype = tp.loc[item, 'product_type']\n return tp.loc[tp['product_type']==ptype]\n\n@st.cache\ndef compare_ingr(i, item):\n '''\n Returns similarity between two products\n '''\n print(f'Similarity of {i} to {item}')\n tp =pd.DataFrame()\n tp.loc[i, 'cos_score']= np.round(sim_df[item][i]*100,2)\n tp.loc[i, 'jac_score'] = np.round(jaccard_binary(np.array(prod_ingr_matrix1.loc[item]), np.array(prod_ingr_matrix1.loc[i]))*100,2)\n #tp.loc[i, 'num_ingr'] = str(dot_prod[item][i])+'/'+ str(dot_prod[item][item])\n #tp.loc[i, 'pricepervol'] = np.round(df_new[df_new['product_name']==i]['pricepervol'].values[0],2)\n #tp.loc[i, 'product_type'] = df_new[df_new['product_name']==i]['product_type'].values[0]\n return tp\n\n@st.cache\ndef jaccard_binary(x,y):\n \"\"\"A function for finding the similarity between two binary vectors\"\"\"\n intersection = np.logical_and(x, y)\n union = np.logical_or(x, y)\n similarity = intersection.sum() / float(union.sum())\n return similarity\n\n# loading the trained model\npickle_in = open('../notebooks/xgb_final.pkl', 'rb') \nmodel = pickle.load(pickle_in)\n\ndef st_shap(plot, height=None):\n shap_html = f\"{shap.getjs()}{plot.html()}\"\n components.html(shap_html, height=height)\n\ndef explain_instance(prod, model, test_set):\n '''\n df1 - getting index \n '''\n idx= df1.loc[df1.product_name==prod].index[0]\n X = test_set.loc[[idx]]\n rand_pred = model.predict(X)\n rand_proba = list(model.predict_proba(X))\n st.markdown('***Model\\'s prediction***')\n st.write(f'{rand_pred[0]} ({np.round(max(rand_proba[0])*100,2)}% probability)')\n st.markdown('***Actual:***')\n st.write(f'{df1.price_category.loc[idx]} (${np.round(df1.pricepervol.loc[idx],2)} per oz)')\n\n\ndef show_shap(prod, model, test_set):\n '''\n \n '''\n idx= df1.loc[df1.product_name==prod].index[0]\n X = test_set.loc[[idx]]\n rand_pred = model.predict(X)\n rand_proba = list(model.predict_proba(X))\n\n #shap.initjs()\n explainer2 = shap.TreeExplainer(model)\n shap_values2 = explainer2.shap_values(test_set.loc[[idx]])\n shap_values = explainer2(X)\n class_names= ['average', 'cheap', 'expensive']\n for which_class in (1,0,2):\n st.write(f'{np.round(rand_proba[0][which_class]*100,2)}% likelihood of being {class_names[which_class]}')\n p= shap.force_plot(explainer2.expected_value[which_class], shap_values2[which_class], test_set.loc[[idx]])\n st_shap(p)\n\n\nproducts_list = list(set((prod_ingr_matrix1.index)))\n\n#Sidebar\nst.sidebar.image(logo, width= 325)\nst.sidebar.markdown('''\n## About\n*A simple user interface to allow you to explore the results of my machine learning model more easily*\n''')\nst.sidebar.markdown('This is still a work in progress but if you have any suggestions or comments, feel free to connect with me on LinkedIn or drop me an email.

Enjoy exploring the model!

', unsafe_allow_html=True)\n\nst.image(long_icon)\nst.markdown('''\n## Skincare Product Analysis\n### **What would you like to do?**\n''')\naction= st.radio('',['Analyse pricing','Find similar products', 'Compare to another product'])\nproduct = st.selectbox(\"Choose your product\", sorted(products_list), key='main')\n\n#must add explanations for each column\n\nif action=='Find similar products':\n num = st.slider(label='Number of products to return', min_value=3, max_value=25, value=10)\n restriction = st.checkbox(label='Show all product types', value=True)\n st.header('Similar products')\n st.table(similar_prod(product, num, restriction))\n\nif action=='Compare to another product':\n prod2 = st.selectbox(\"Compare to\", sorted(products_list), key='secondary')\n st.header('Comparison')\n st.table(compare_ingr(prod2, product))\n\nif action=='Analyse pricing':\n explain_instance(product, model, x_complete)\n my_expander = st.beta_expander(label='How did the model make this prediction?', expanded =True)\n with my_expander:\n '''**SHAP VALUES**'''\n show_shap(product, model, x_complete)\n\n \n expander = st.beta_expander(label='About the classification')\n with expander: \n '''**Classification criteria**'''\n expander.markdown('''\n - *cheap*: under $15 per oz \n - *average*: $15 - $56 per oz \n - *expensive*: over $56 per oz \n ''')\n#df1.product_name==prod\n\nst.image(end_icon)\n","sub_path":"Project/streamlit/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"432706549","text":"from django.shortcuts import get_object_or_404, render\nfrom .models import Listing\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom .choices import bedroom_choices,states_choices,price_choices\n# Create your views here.\ndef index(request):\n #listings = Listing.objects.all()\n listings= Listing.objects.order_by('-price').filter(is_published=True)\n paginator= Paginator(listings,6)\n page= request.GET.get('page')\n listing_paginator= paginator.get_page(page)\n context={\n 'listings': listing_paginator\n \n \n }\n \n return render(request,'listings/listings.html',context)\n \n \n'''\nreturn render(request,'listings/listings.html',{\n \n 'name':'filipe'## this part will be substitute by context variable!!!\n })\n\n''' \n\n \ndef listing(request,listing_id):\n listing = get_object_or_404(Listing, pk = listing_id)\n \n context={'listing':listing}\n return render(request,'listings/listing.html',context)\n\ndef search(request):\n query_list= Listing.objects.order_by('-date_published')\n \n #for keywords coming from form;\n if 'keywords' in request.GET:\n keywords= request.GET['keywords']\n if keywords:\n print(keywords)\n query_list=query_list.filter(description__icontains=keywords)\n #for city coming from form;\n if 'city' in request.GET:\n city =request.GET['city']\n if city:\n query_list= query_list.filter(city__iexact=city)### exact(without 'i' in the beginning) if we want case sensitive!!!!\n #for state coming from form;\n if 'state' in request.GET:\n state =request.GET['state']\n if state:\n query_list= query_list.filter(state__iexact=state)\n #for state coming from form;\n if 'bedrooms' in request.GET:\n bedrooms =request.GET['bedrooms']\n if bedrooms:\n query_list= query_list.filter(bedrooms__lte=bedrooms)\n #for price coming from form;\n if 'price' in request.GET:\n price= request.GET['price']\n if price:\n query_list= query_list.filter(price__lte=price)\n context={\n 'bedroom_choices' : bedroom_choices,\n 'state_choices': states_choices,\n 'price_choices': price_choices,\n 'listings':query_list,\n 'values': request.GET ## we are saving all our request values !\n }\n return render(request,'listings/search.html',context)\n ","sub_path":"listings/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"225271016","text":"# many rules (your puzzle input) are being enforced about bags and their contents;\r\n# bags must be color-coded and must contain specific quantities of other color-coded bags\r\n# You have a shiny gold bag. If you wanted to carry it in at least one other bag,\r\n# how many different bag colors would be valid for the outermost bag?\r\n# (In other words: how many colors can, eventually, contain at least one shiny gold bag?)\r\n\r\n# grab defaultdict so we can have absent keys as empty lists []\r\nfrom collections import defaultdict\r\n\r\n# input\r\nwith open('7.txt', 'r') as file:\r\n input = file.read()\r\n\r\n# turn the input into a list, one element is one rule\r\ninput_list = list(input.split('\\n'))\r\n\r\n# make an empty defaultdict to contain e.g. 'light olive' : [(2, 'drab blue'), (1, 'plaid purple')]\r\n# futureproofing bc part 2 will probably involve the numbers\r\n# if a key is missing, the list of bags it contains is []\r\nrules = defaultdict(list)\r\n\r\n# need to split up properly\r\nfor rule0 in input_list:\r\n # rule0 = 'light olive bags contain 2 drab blue bags, 1 plaid purple bag.'\r\n rule1 = rule0[:-1].split(' bags contain ')\r\n # ('light olive', '2 drab blue bags, 1 plaid purple bag')\r\n outer_col = rule1[0]\r\n # 'light olive'\r\n rule2 = rule1[1].split(', ')\r\n # ('2 drab blue bags', '1 plaid purple bag')\r\n numcol_list = []\r\n for numcol in rule2:\r\n if numcol == 'no other bags':\r\n break # nothing to add to numcol_list\r\n rule3 = numcol.split(' ')\r\n # ('2', 'drab', 'blue', 'bags')\r\n rule4 = (int(rule3[0]), rule3[1] + ' ' + rule3[2])\r\n # (2, 'drab blue')\r\n numcol_list.append(rule4)\r\n # [(2, 'drab blue')]\r\n # rule3 = [(2, 'drab blue'), (1, 'plaid purple')]\r\n rules[outer_col] = numcol_list\r\n # { ... 'light olive': [(2, 'drab blue'), (1, 'plaid purple')], ...}\r\n\r\n# gets list of all colours of bags in the dict\r\n# ['light chartreuse', 'dotted silver', ...]\r\ncols = list(rules)\r\n# will contain list of colours containing shiny gold\r\nhas_shiny_gold = []\r\n# will contain list of colours not containing shiny gold\r\nno_shiny_gold = []\r\n\r\n# gets colours the next level down\r\n# 'light olive' -> ['drab blue', 'plaid purple']\r\ndef contains_cols(col: str):\r\n numcols = rules[col]\r\n cols = []\r\n for numcol in numcols:\r\n cols.append(numcol[1])\r\n return cols\r\n\r\n# we will gradually remove colours\r\nwhile len(cols) > 0:\r\n outer_col = cols.pop(0) # pop off the first col in the list\r\n cols_inside = contains_cols(outer_col) # get the cols one level down\r\n while len(cols_inside) > 0: # while we still have cols inside\r\n if 'shiny gold' in cols_inside: # if we have shiny gold inside\r\n has_shiny_gold.append(outer_col) # add to output list\r\n break\r\n inner_col = cols_inside.pop(0) # pop first element from list\r\n if inner_col in no_shiny_gold: # if we've already confirmed it has no shiny gold\r\n continue # go to next element in list\r\n elif inner_col in has_shiny_gold: # if we've already confirmed it has a shiny gold\r\n has_shiny_gold.append(outer_col) # add to output list\r\n break # don't need to continue the while\r\n cols_inside = cols_inside + contains_cols(inner_col) # get the cols inside that\r\n else:\r\n # no break statement hit, cols_inside = 0\r\n # => does not contain shiny gold\r\n no_shiny_gold.append(outer_col)\r\n\r\nprint(len(has_shiny_gold))","sub_path":"7a.py","file_name":"7a.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"382484335","text":"\"\"\"\nParses and validates inputs for command line use\n\"\"\"\n\nimport random\nimport argparse\nimport time\nimport keylights\n\n\nkeyboard = {}\nkeyboard[\"ESC\"] = 0\nkeyboard[\"F1\"] = 1\nkeyboard[\"F2\"] = 2\nkeyboard[\"F3\"] = 3\nkeyboard[\"F4\"] =4\nkeyboard[\"F5\"] = 5\nkeyboard[\"F6\"] =6\nkeyboard[\"F7\"] =7\nkeyboard[\"F8\"] =8\nkeyboard[\"F9\"] =9\nkeyboard[\"F0\"] =10\nkeyboard[\"F\"] =11\nkeyboard[\"F2\"] =12\nkeyboard[\"DEL\"] =13\nkeyboard[\"TAB\"] =14\nkeyboard[\"Q\"] =15\nkeyboard[\"W\"] =16\nkeyboard[\"E\"] =17\nkeyboard[\"R\"] =18\nkeyboard[\"T\"] =19\nkeyboard[\"Y\"] =20\nkeyboard[\"U\"] =21\nkeyboard[\"I\"] =22\nkeyboard[\"O\"] =23\nkeyboard[\"P\"] =24\nkeyboard[\"[\"] =25\nkeyboard[\"]\"] =26\nkeyboard[\"BACKSLASH\"] =27\nkeyboard[\"CAPS\"] =28\nkeyboard[\"A\"] =29\nkeyboard[\"S\"] =30\nkeyboard[\"D\"] =31\nkeyboard[\"F\"] =32\nkeyboard[\"G\"] =33\nkeyboard[\"H\"] =34\nkeyboard[\"J\"] =35\nkeyboard[\"K\"] =36\nkeyboard[\"L\"] =37\nkeyboard[\";\"] =38\nkeyboard[\"'\"] =39\nkeyboard[\"ENTER\"] =40\nkeyboard[\"SHIFT\"] =41\nkeyboard[\"Z\"] =42\nkeyboard[\"X\"] =43\nkeyboard[\"C\"] =44\nkeyboard[\"V\"] =45\nkeyboard[\"B\"] =46\nkeyboard[\"N\"] =47\nkeyboard[\"M\"] =48\nkeyboard[\",\"] =49\nkeyboard[\".\"] =50\nkeyboard[\"/\"] =51\nkeyboard[\"SHIFT2\"] =52\nkeyboard[\"CTRL\"] =53\nkeyboard[\"WIN\"] =54\nkeyboard[\"ALT\"] =55\nkeyboard[\"SPACEBAR\"] =56\nkeyboard[\"ALT\"] =57\nkeyboard[\"FN\"] =58\nkeyboard[\"CTX\"] =59\nkeyboard[\"CTRL2\"] =60\nkeyboard[\"INS\"] =61\nkeyboard[\"HM\"] =62\nkeyboard[\"PU\"] =63\nkeyboard[\"DEL\"] =64\nkeyboard[\"END\"] =65\nkeyboard[\"PD\"] =66\nkeyboard[\"UP\"] =67\nkeyboard[\"LEFT\"] =68\nkeyboard[\"DOWN\"] =69\nkeyboard[\"RIGHT\"] =70\n\ndef main():\n \"\"\"\n Parse arguments and start application appropriately\n \"\"\"\n # TODO: Add parameter for a json object mapping key -> color\n # TODO: Add parameter to change only a single key\n parse = argparse.ArgumentParser(\n description='Change light color for switches of the Drevo Calibur keyboard')\n lightctl = keylights.Keylights()\n\n while True:\n lightctl.setkey(keyboard[\"E\"], (255,0,0))\n time.sleep(0.2);\n lightctl.setkey(keyboard[\"R\"], (255,255,0))\n time.sleep(0.2);\n lightctl.setkey(keyboard[\"W\"], (255,0,255))\n time.sleep(0.2);\n lightctl.setkey(keyboard[\"A\"], (25,0,255))\n time.sleep(0.2);\n lightctl.setkey(keyboard[\"N\"], (0,255,0))\n time.sleep(0.2);\n\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"drevo/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"27379766","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.widgets import Slider\n\n\ndef plot_raw_data(data,chans=None,bad_coords= []):\n if chans is None:\n chans = range(data.shape[0])\n fig, ax = plt.subplots(figsize=(10,10))\n plt.subplots_adjust(bottom=0.25)\n for ch in chans:\n plt.plot(data[ch])\n plt.axis([0, 100000, -1000, 1000])\n for c in bad_coords:\n ax.axvspan(c[0],c[1],color='red',alpha=.5)\n axcolor = 'lightgoldenrodyellow'\n axpos = plt.axes([0.2, 0.1, 0.65, 0.03], facecolor=axcolor)\n spos = Slider(axpos, 'Pos', 0.1, len(data[0]))\n def update(val): #needed for slider function of plot_raw_data\n pos = spos.val\n ax.axis([pos,pos+50000,-500,500])\n fig.canvas.draw_idle()\n spos.on_changed(update)\n plt.show();\n\n \ndef plot_features(data):\n plts = data.shape[0]//20 +1 #we want 20 per plot\n xsize=10\n ysize=5\n fig=plt.figure()\n for k in range (0,plts):\n ax=fig.add_subplot(xsize,ysize,k+1)\n l = ax.plot(data[k*20:(k+1)*20])\n plt.axis([0, 1000, 0, 10])\n sframe = Slider(fig.add_subplot(50,1,50), 's', 0, len(data[0])-1, valinit=0)\n def update(val):\n frame = np.around(sframe.val)\n #l.set_data(readlist[k][frame,:,:])\n ax.axis([pos,pos+1000,0,10])\n sframe.on_changed(update)\n plt.show()\n\n\ndef plot_pc(pca,data):\n for p in range(pca.n_components):\n plt.plot(pca.transform(data)[:,p])\n plt.xlabel('Time (in w_size)')\n plt.ylabel('PC Value')\n plt.title('First %d principal components' % pca.n_components)\n plt.show()\n\n \n#get elbow curve. This also outputs the optimal n_components for the given desired explained variancce.\ndef __elbow_curve(datapart,expl_var_lim):\n components = range(1, datapart.shape[1] + 1)\n explained_variance = []\n #till where?\n lim=min(100, datapart.shape[1])\n count=0\n for component in tqdm(components[:lim]):\n pca = PCA(n_components=component)\n pca.fit(datapart)\n expl_var=sum(pca.explained_variance_ratio_)\n explained_variance.append(expl_var)\n count+=1\n if(expl_var>(expl_var_lim/100.)):\n optimal_no_comps=count\n break\n if(explained_variance[-1:][0]<(expl_var_lim/100.)):\n print('Could not explain more than %d %% of the variance. n_comps is set to match this. Consider increasing data range or lowering demanded explained variance' % expl_var*100)\n optimal_no_comps=components[-1:]\n sns_plot = sns.regplot(\n x=np.array(components[:count]), y=explained_variance,\n fit_reg=False).get_figure()\n return optimal_no_comps\n\n\n","sub_path":"vis/feature_vis.py","file_name":"feature_vis.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"61624782","text":"import pymysql.cursors\n\ndef sqlGet(path):\n\tif (path == 'getName'):\n\t\treturn getName();\n\treturn \"{}\";\n\ndef getName():\n\tconnection = pymysql.connect(host='192.168.1.110',\n\t\t\t\t\t\t\tuser='Preston',\n\t\t\t\t\t\t\tpasswd='password',\n\t\t\t\t\t\t\tdb='dbTest',\n\t\t\t\t\t\t\tcharset='utf8mb4',\n\t\t\t\t\t\t\tcursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith connection.cursor() as cursor:\n\t\t\tsql = \"SELECT name FROM Test\"\n\t\t\tcursor.execute(sql, )\n\t\t\tresult = cursor.fetchone()\n\t\t\treturn result\n\tfinally:\n\t\tconnection.close();\n","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"55430667","text":"from pathlib import Path\nimport pickle\n\nfrom asyncbots.bot import SlackBot, register\nfrom asyncbots.command import MessageCommand\nfrom asyncbots.parsing import symbols\nfrom nltk import word_tokenize\nfrom pyparsing import CaselessLiteral, Optional, Word\n\nfrom lda.topics import Topics, copy_hyperparams\n\nTOPIC_PICKLE = Path('lda/topics.pkl')\nSAVE_PERIOD = 100\n\nclass TopicBot(SlackBot):\n def __init__(self, slack=None):\n self.name = 'Topics'\n self.expr = CaselessLiteral('topics') + Optional(symbols.channel_name.setResultsName('channel'))\n self.doc = 'Print a list of topics optionally restricted to a channel:\\n\\ttopics []'\n\n self._update_count = 0\n\n if TOPIC_PICKLE.exists():\n with TOPIC_PICKLE.open('rb') as f:\n self._global_topics, self._channel_topics = pickle.load(f)\n else:\n raise FileNotFoundError('Topics pickle does not exist')\n\n @register()\n async def command_topics(self, in_channel, user, parsed):\n if 'channel' in parsed and parsed['channel'] not in self._channel_topics:\n return MessageCommand(channel=in_channel, user=user, text='Channel {} not found'.format(parsed['channel']))\n\n topics = self._channel_topics[parsed['channel']] if 'channel' in parsed else self._global_topics\n return [MessageCommand(\n user=user,\n channel=in_channel,\n text='{}. {}'.format(i, ' '.join(t)))\n for i, t in enumerate(topics.top_words())\n ]\n\n @register(unfiltered=True)\n async def topic_update(self, in_channel, user, message):\n if in_channel not in self._channel_topics:\n self._channel_topics[in_channel] = copy_hyperparams(self._global_topics)\n\n tokenized = word_tokenize(message.strip().lower())\n self._global_topics.submit_message(tokenized)\n self._channel_topics[in_channel].submit_message(tokenized)\n\n if self._update_count % SAVE_PERIOD == 0:\n with TOPIC_PICKLE.open('wb') as f:\n pickle.dump((self._global_topics, self._channel_topics), f)\n","sub_path":"topic_bot.py","file_name":"topic_bot.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"536515287","text":"import os, argparse\nimport torch\nfrom torchvision import models\nimport torch.nn as nn\nfrom scipy import signal\nimport logging\nimport torch.optim as optim\nimport numpy as np\nimport random\nfrom sklearn.metrics import roc_auc_score\n\nrandom.seed(1)\nnp.random.seed(1)\ntorch.manual_seed(1)\n\nfrom train_utils import AgeDataHandler, init_visdom\nfrom base_utils import Params, set_logger, parse_soundfile\n\ndef compute_loss(batch, backward = False):\n if backward:\n model.train()\n else:\n model.eval()\n\n observations = []\n targets = torch.zeros(len(batch))\n for i, (soundfile, category) in enumerate(batch):\n Sxx = parse_soundfile(soundfile, timeframe, window_fn, features)\n observations.append(Sxx)\n targets[i] = 2.0*(category < 6) - 1 if category is not None else 0\n\n observations = torch.stack(observations)\n if cuda:\n observations = observations.cuda()\n targets = targets.cuda()\n\n outputs = model(observations)\n ## refer https://github.com/lukasruff/Deep-SVDD-PyTorch\n dist = torch.sum((outputs - _C_) ** 2, dim=1)\n losses = torch.where(targets == 0, dist, ETA * ((dist + eps) ** targets.float()) )\n loss = torch.mean(losses)\n if backward:\n loss.backward()\n return loss.detach().item() / len(batch)\n else:\n return loss.detach().item() / len(batch), dist.cpu().data.numpy().tolist(), (-1*targets).cpu().data.numpy().tolist()\n\ndef eval_model(val_data):\n total_loss, total_obs = 0, 0\n scores, targets = [], []\n for i, batch in enumerate(val_data):\n batch_loss, x,y = compute_loss(batch, backward = False)\n total_loss += batch_loss * len(batch)\n total_obs += len(batch)\n scores += x\n targets += y\n\n auc = -1.0\n if len(set(targets)) == 2:\n auc = roc_auc_score(targets, scores)\n return total_loss/total_obs, auc\n\ndef train(model, train_data, optimizer):\n best_auc, last_update= 0.0 , 0\n epoch, batch_seen = 0, 0\n auc, val_loss, norm = 0, np.finfo(np.float).max, -1.0\n continue_train = True\n while continue_train:\n epoch += 1\n scheduler.step(auc)\n avg_loss, avg_accuracy = 0.0, 0.0\n for i, batch in enumerate(train_data):\n optimizer.zero_grad()\n batch_loss = compute_loss(batch, backward = True)\n optimizer.step()\n\n print(batch_loss)\n avg_loss += batch_loss\n update_metrics(batch_loss, 0, key = 'train')\n\n batch_seen += 1\n if batch_seen % x_batches == 0:\n val_loss, auc = eval_model(val_data)\n update_metrics(val_loss, auc, key = 'val')\n log_metrics()\n logging.info(\"@Validation round:{}, auc:{:.5} val_loss:{:.5}\".format(batch_seen/x_batches, auc, val_loss))\n\n norm = 0\n for param in model.parameters():\n if param.requires_grad:\n norm += param.norm(2)\n plot_norm(torch.sqrt(norm))\n\n # save model\n if auc <= best_auc:\n last_update += 1\n else:\n best_auc = auc\n last_update = 0\n\n torch.save(model.state_dict(), os.path.join(MODELDIR, \"model.torch\"))\n model_state_tmp = dict(config=config, optimizer=optimizer.state_dict(), auc=auc, val_loss=val_loss, finished= not continue_train,\\\n train_acc=avg_accuracy/(batch_seen+1), train_loss= avg_loss/(batch_seen+1), epoch=epoch, batch_seen=batch_seen)\n\n model_state = {}\n if os.path.isfile(os.path.join(MODELDIR, \"model_training.state\")):\n model_state = torch.load(os.path.join(MODELDIR, \"model_training.state\"), map_location='cpu')\n model_state.update(model_state_tmp)\n torch.save(model_state, os.path.join(MODELDIR, \"model_training.state\"))\n\n if last_update > 1.0* params.lastupdate or np.isnan(val_loss) or epoch > epoch_limit:\n continue_train = False\n break\n\n logging.info(\"@epoch:{}, train loss:{:.2}, train accuracy:{:.2}, val loss:{:.2}, \\\n auc:{:.2}, param norm:{:.2}\".format(epoch,avg_loss/(batch_seen+1), avg_accuracy/(batch_seen+1), val_loss, auc, norm))\n\n model_state = torch.load(os.path.join(MODELDIR, \"model_training.state\"), map_location='cpu')\n model_state[\"curr_epoch\"] = epoch\n model_state[\"curr_train_loss\"], model_state[\"curr_train_acc\"], = avg_loss/(batch_seen+1), avg_accuracy/(batch_seen+1)\n model_state[\"last_update\"], model_state[\"finished\"] = last_update, not continue_train\n torch.save(model_state, os.path.join(MODELDIR, \"model_training.state\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-train_datadir\", help=\"training directory containing folders of soundfiles grouped in their classes e.g. .../age/\", required=True)\n parser.add_argument(\"-val_datadir\", help=\"validation directory containing folders of soundfiles grouped in their classes e.g. .../age/\", required=True)\n parser.add_argument(\"-cuda\", help=\"to run on cuda\", default = False)\n parser.add_argument(\"-pretrained\", help=\"to reload the preexisting model\", default = False)\n parser.add_argument(\"-modeldir\", help=\"directory to store the model. Should contain params.json\", default = \"\")\n args = parser.parse_args()\n\n cuda = eval(args.cuda) and torch.cuda.is_available() if args.cuda else False\n pretrained = args.pretrained\n MODELDIR = args.modeldir\n torch.save(dict(), os.path.join(MODELDIR, \"model_training.state\"))\n\n json_path = os.path.join(MODELDIR, \"params.json\")\n assert os.path.isfile(json_path), \"No cofiguration found at {}\".format(json_path)\n params = Params(json_path)\n\n set_logger(os.path.join(MODELDIR, \"train.log\"))\n\n def get_weight_vector():\n if params.dict.get(\"weightedloss\", False):\n return torch.Tensor([5,1,1,1,5,5,10])\n return None\n\n timeframe = params.timeframe\n windowfn = params.windowfn\n model_arch = params.modelarch\n lr = params.lr\n batch_size = params.batchsize\n x_batches = params.xbatches\n factor = params.schedulerfactor\n patience = params.schedulerpatience\n weight_decay = params.l2\n epoch_limit = params.epoch\n features=params.dict.get(\"features\", \"fft\")\n\n window_fn = signal.tukey(51, 0.5)\n if windowfn == \"gaussian\":\n window_fn = signal.gaussian(51, std=1)\n\n # load data\n train_data, val_data = AgeDataHandler(args.train_datadir, batch_size).train_val_split()\n logging.info(\"Number of training observations: {}\".format(len(train_data)))\n # import pdb; pdb.set_trace()\n # val_data = AgeDataHandler(args.val_datadir, batch_size)\n logging.info(\"Number of validation observations: {}\".format(len(val_data)))\n\n epoch_size = int(1.0*len(train_data)/batch_size) + 1\n env_name = params.job_name\n config = dict(lr= lr, batch_size=batch_size, cuda=cuda, \\\n epoch_size=epoch_size, train_data=len(train_data), val_data=len(val_data))\n config.update(params.dict)\n\n CLASSES = 2 # 0 normal; 1 abnormal\n FINAL_DIM=256\n if params.modelarch == \"resnet18\":\n model = models.resnet18(pretrained=False)\n model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False)\n model.fc = torch.nn.Linear(512, FINAL_DIM, bias = True)\n elif params.modelarch == \"resnet34\":\n model = models.resnet34(pretrained=False)\n model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,bias=False)\n model.fc = torch.nn.Linear(512, FINAL_DIM, bias = True)\n else:\n raise\n\n model.droprate=0.7\n if params.dict.get('optim',\"adam\") == \"adam\":\n optimizer = optim.Adam(model.parameters(), lr = lr, weight_decay=weight_decay)\n patience = 50\n elif params.optim == \"adamams\":\n optimizer = optim.Adam(model.parameters(), lr = lr, weight_decay=weight_decay, amsgrad=True)\n elif params.optim == \"sgd\":\n optimizer = optim.SGD(model.parameters(), lr = lr, momentum=0,weight_decay=weight_decay, nesterov=False)\n patience = 50\n elif params.optim == \"sgdnest\":\n optimizer = optim.SGD(model.parameters(), lr = lr, momentum=0.9,weight_decay=weight_decay, nesterov=True)\n patience = 50\n elif params.optim == \"rmsmom\":\n optimizer = optim.RMSprop(model.parameters(), lr = lr, weight_decay=weight_decay, momentum=0.9)\n elif params.optim == \"rms\":\n optimizer = optim.RMSprop(model.parameters(), lr = lr, weight_decay=weight_decay)\n\n if pretrained:\n assert os.path.isfile(\"{}/model.torch\".format(MODELDIR)), \"model not found\"\n model.load_state_dict(torch.load(\"{}/model.torch\".format(MODELDIR)))\n optimizer_state = torch.load(\"{}/model_training.state\".format(MODELDIR))['optimizer']\n optimizer.load_state_dict(optimizer_state)\n logging.info(\"Loading the pre-existing model at {}/model.torch\".format(MODELDIR))\n else:\n if os.path.exists(MODELDIR):\n logging.info(\"Writing in existing directory: {}\".format(MODELDIR))\n else:\n raise\n\n _C_ = torch.zeros(FINAL_DIM)\n if cuda:\n model = model.cuda()\n _C_ = _C_.cuda()\n\n # compute C\n eps = 1e-3\n ETA = 1.0\n n_samples = 0\n model.eval()\n with torch.no_grad():\n for c,batch in enumerate(train_data):\n observations = []\n for i, (soundfile, category) in enumerate(batch):\n Sxx = parse_soundfile(soundfile, timeframe, window_fn, features)\n observations.append(Sxx)\n\n observations = torch.stack(observations)\n if cuda:\n observations = observations.cuda()\n\n outputs = model(observations)\n n_samples += outputs.shape[0]\n _C_ += torch.sum(outputs, dim = 0)\n\n _C_ /= n_samples\n # If c_i is too close to 0, set to +-eps. Reason: a zero unit can be trivially matched with zero weights.\n _C_[(abs(_C_) < eps) & (_C_ < 0)] = -eps\n _C_[(abs(_C_) < eps) & (_C_ > 0)] = eps\n\n scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"max\", factor=factor, patience=patience, verbose=True)\n update_metrics, log_metrics, plot_norm = init_visdom(env_name, config)\n\n train(model, train_data, optimizer)\n","sub_path":"train_anom.py","file_name":"train_anom.py","file_ext":"py","file_size_in_byte":10468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"314060003","text":"import numpy as np\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dropout, Flatten, Dense\nfrom keras import applications\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport matplotlib.pyplot as plt\nimport requests\nimport pandas as pd\nimport pickle\n\ndata = pd.read_pickle('pickels/16k_apperal_data_preprocessed')\n#data.head()\n\n# some of the brand values are empty. \n# Need to replace Null with string \"NULL\"\ndata['brand'].fillna(value=\"Not given\", inplace=True )\n\n# replace spaces with hypen\nbrands = [x.replace(\" \", \"-\") for x in data['brand'].values]\ntypes = [x.replace(\" \", \"-\") for x in data['product_type_name'].values]\ncolors = [x.replace(\" \", \"-\") for x in data['color'].values]\n\nbrand_vectorizer = CountVectorizer()\nbrand_features = brand_vectorizer.fit_transform(brands).tocsr()\n\ntype_vectorizer = CountVectorizer()\ntype_features = type_vectorizer.fit_transform(types).tocsr()\n\ncolor_vectorizer = CountVectorizer()\ncolor_features = color_vectorizer.fit_transform(colors).tocsr()\n\nfrom IPython.display import display, Image, SVG, Math, YouTubeVideo\n\ndef idf_w2v_brand_color_img(doc_id, w1, w2, w3, w4, num_results):\n bottleneck_features_train = np.load('16k_data_cnn_features.npy')\n asins = np.load('16k_data_cnn_feature_asins.npy')\n asins = list(asins)\n\n# load the original 16K dataset\n img_data = pd.read_pickle('pickels/16k_apperal_data_preprocessed')\n df_asins = list(img_data['asin'])\n\n # doc_id = asins.index(df_asins[doc_id])\n \n idf_w2v_dist = pairwise_distances(w2v_title_weight, w2v_title_weight[doc_id].reshape(1,-1))\n brand_dist = pairwise_distances(brand_features, brand_features[doc_id])\n type_dist = pairwise_distances(type_features, type_features[doc_id])\n color_dist = pairwise_distances(color_features, color_features[doc_id])\n img_dist = pairwise_distances(bottleneck_features_train, bottleneck_features_train[doc_id].reshape(1,-1))\n pairwise_dist = (w1 * idf_w2v_dist + w2 * (brand_dist + type_dist) + w3 * color_dist + w4 * img_dist)/float(w1 + w2 + w3 + w4)\n\n # np.argsort will return indices of 9 smallest distances\n indices = np.argsort(pairwise_dist.flatten())[0:num_results]\n #pdists will store the 9 smallest distances\n pdists = np.sort(pairwise_dist.flatten())[0:num_results]\n\n #data frame indices of the 9 smallest distace's\n df_indices = list(data.index[indices])\n \n\n for i in range(0, len(indices)):\n heat_map_w2v_brand(data['title'].loc[df_indices[0]],data['title'].loc[df_indices[i]], data['medium_image_url'].loc[df_indices[i]], indices[0], indices[i],df_indices[0], df_indices[i], 'weighted')\n print('ASIN :',data['asin'].loc[df_indices[i]])\n print('Brand :',data['brand'].loc[df_indices[i]])\n print('euclidean distance from input :', pdists[i])\n print('='*125)\n\nidf_w2v_brand_color_img(12566, 5, 4, 2, 1, 20) \n#Here 12566 is the product and 5, 4, 2, 1 are the weights given and 20 is num results\n# in the give heat map, each cell contains the euclidean distance between words i, j\n","sub_path":"exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":3158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"334022225","text":"# Libraries I have not created...\nfrom tkinter import *\nfrom tkinter import ttk\nfrom PIL import ImageTk, Image\nimport numpy as np\nimport pdb\nimport matplotlib\nmatplotlib.use('TkAgg')\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nimport matplotlib.pyplot as plt\nimport os\nfrom subprocess import check_output\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nimport datetime as DT\n# Libraries I have created...\nfrom initializations import * #all directories and constants defined here\nfrom read_routines import *\nfrom mutable_data_structs import define_MSC_structure\nfrom GUI_function import *\n\n\ndef close_program():\n root.quit()\n root.destroy()\n \ndef save_curtain_plot():\n if (os.name != 'nt'): # Linux/Unix\n cmd = 'cp '+ source_dir+'current_curtain.png ' + out_dir + \\\n 'curtain_chan_' + chan_sel.get() + '_' + str(DT.datetime.now().time())\n cmd_feedback = check_output(cmd, shell=True)\n print(cmd_feedback)\n print('Curtain should have been saved in '+out_dir)\n else: # Windows\n print('Nothing yet. Working on it...')\n pdb.set_trace()\n\ndef load_and_plot(*args):\n \n # Check user channel input for error\n\n selected_chan = int(chan_sel.get())\n if selected_chan not in range(1,6):\n print('Channel entered is outside selected range.')\n print('Please re-enter a channel number.')\n return None\n \n # Some declarations\n\n MCS_file_list = config_dir + 'MCS_file_list.txt'\n\n # Create file list using system commands.\n # Send different sys commands depending on OS.\n # At end of this block, a list should contain the full-path file names\n # of all MCS data files to loop thru.\n\n if (os.name != 'nt'): # Linux/Unix\n\t cmd = 'touch ' + MCS_file_list\n\t cmd_feedback = check_output(cmd, shell=True)\n\t cmd = 'rm ' + MCS_file_list\n\t cmd_feedback = check_output(cmd, shell=True)\n\t cmd = './make_file_list_unix ' + raw_dir + ' > ' + MCS_file_list\n\t cmd_feedback = check_output(cmd, shell=True)\n else: # Windows\n print('Nothing yet. Working on it...')\n pdb.set_trace()\t\n\n with open(MCS_file_list) as MCS_list_fobj:\n all_MCS_files = MCS_list_fobj.readlines()\n nMCS_files = len(all_MCS_files)\n\n # Read in the MCS (science) data\n\n first_read = 1\n r=0\n for MCS_file in all_MCS_files:\n MCS_file = MCS_file.rstrip()\n MCS_data_1file = read_in_raw_data(MCS_file)\n if first_read:\n first_read = 0\t\n # Put the parameters that won't change during 1 flight into variables\n nc = MCS_data_1file['meta']['nchans'][0]\n nb = MCS_data_1file['meta']['nbins'][0]\n nshots = MCS_data_1file['meta']['nshots'][0]\n vrT = MCS_data_1file['meta']['binwid'][0]\n vrZ = (vrT * c) / 2.0\n # declare data structure to hold all data. estimate tot # recs first\n tot_est_recs = int(rep_rate/nshots)*file_len_secs*nMCS_files\n MCS_struct = define_MSC_structure(nc,nb)\n MCS_data = np.zeros(tot_est_recs, dtype=MCS_struct)\n nr_1file = MCS_data_1file.shape[0]\n MCS_data[r:r+nr_1file] = MCS_data_1file\n r += nr_1file \n #NOTE: You could put conditional break\n # statement in this loop to read-in\n # data from time segment only.\n \n \n MCS_data = MCS_data[0:r]\n print('All MCS data are loaded.')\n\n # Prepare data for a plot\n\n samp_chan = MCS_data['counts'][:,selected_chan-1,:]\n if nhori not in range(1,100):\n print('Set a more reasonable averaging # in initialization file.')\n return None\n elif (nhori > 1):\n samp_chan = average_lidar_data(samp_chan,nhori,0)\n print('Finished averaging channel '+chan_sel.get()+' to '+ \\\n str(nhori) + ' profiles.')\n \n samp_chan = samp_chan.transpose()\n samp_chan = np.flipud(samp_chan) # reverse the order in columns (not rows)\n z = np.flipud(np.arange(0,nb*vrZ,vrZ))\n\n # Plot the data\n\n plt.clf()\n CPlot = make_curtain_plot_update_fig(samp_chan,nb,vrZ,z)\n canvas.show()\n \nroot = Tk()\nroot.title(\"Experiments\")\n\n# Frame to hold all button on RHS\nRHSframe = ttk.Frame(root, borderwidth=2)\nRHSframe.grid(column=0,row=0,stick=(N, W, E, S))\nRHSframe.rowconfigure(0,weight=1)\n\n# Image Canvas\nfig99 = plt.figure(99,figsize=(figW,figL))\ncanvas =FigureCanvasTkAgg(fig99,master=root)\ncanvas.show()\ncanvas.get_tk_widget().grid(column=1,row=0)\n\n# Buttons in RHS frame\nload_b = Button(RHSframe,text='Load & plot',command=load_and_plot)\nload_b.grid(column=0, row=0, sticky=W)\nsavcurt_b = Button(RHSframe,text='Save curtain',command=save_curtain_plot).grid(column=0, row=3, sticky=W)\nclose_b = Button(RHSframe,text='Exit',command=close_program).grid(column=0, row=4, sticky=W)\n\n# Text entry boxes in RHS frame\nchan_sel_l = ttk.Label(RHSframe, text='Selected channel').grid(column=0, row=1, sticky=S)\nchan_sel = StringVar()\nchan_entry = ttk.Entry(RHSframe, width=2, textvariable=chan_sel)\nchan_entry.insert(0,'1') # put in a default value\nchan_entry.grid(column=0, row=2, sticky=N)\n\n\n# The following commands tell the \"RHSframe\" how to change the shape of its\n# columns if the user resizes the window.\nfor child in RHSframe.winfo_children(): child.grid_configure(padx=5,pady=5)\n\n\nroot.mainloop()\n","sub_path":"L1A/GUI_exp.py","file_name":"GUI_exp.py","file_ext":"py","file_size_in_byte":5511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"489772246","text":"\n\nfrom xai.brain.wordbase.adjectives._borderline import _BORDERLINE\n\n#calss header\nclass _BORDERLINES(_BORDERLINE, ):\n\tdef __init__(self,): \n\t\t_BORDERLINE.__init__(self)\n\t\tself.name = \"BORDERLINES\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"borderline\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_borderlines.py","file_name":"_borderlines.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"268880418","text":"import os\nimport sys\nimport unittest\n\nsys.path.insert(1, os.path.abspath(os.path.join(__file__, \"../..\")))\nimport base_test\nfrom selenium.common import exceptions\n\nclass BasicKeyboardInterfaceTest(base_test.WebDriverBaseTest):\n\n def test_ShouldAllowBasicKeyboardInput(self):\n self.driver.get(self.webserver.where_is(\"user_input/res/javascriptPage.html\"))\n\n keyReporter = self.driver.find_element_by_id(\"keyReporter\")\n\n self.driver.find_element_by_id(\"body\").send_keys(\"abc def\")\n\n self.assertEquals(\"abc def\", keyReporter.get_attribute(\"value\"))\n\n## def test_ShouldAllowSendingKeyDownOnly(self):\n## self.driver.get(self.webserver.where_is(\"user_input/res/javascriptPage.html\"))\n##\n## keysEventInput = self.driver.find_element_by_id(\"theworks\")\n##\n## IAction pressShift = actionProvider.KeyDown(keysEventInput, Keys.Shift).Build()\n##\n## keyLoggingElement = self.driver.find_element_by_id(\"result\")\n## logText = keyLoggingElement.text\n##\n## IAction releaseShift = actionProvider.KeyDown(keysEventInput, Keys.Shift).Build()\n##\n## self.assertTrue(logText.EndsWith(\"keydown\"), \"Key down event not isolated. Log text should end with 'keydown', got: \" + logText)\n\n## def test_ShouldAllowSendingKeyUp(self):\n## self.driver.get(self.webserver.where_is(\"user_input/res/javascriptPage.html\"))\n## keysEventInput = self.driver.find_element_by_id(\"theworks\")\n##\n## Actions actionProvider = new Actions(driver)\n## IAction pressShift = actionProvider.KeyDown(keysEventInput, Keys.Shift).Build()\n## pressShift.Perform()\n##\n## keyLoggingElement = self.driver.find_element_by_id(\"result\")\n##\n## eventsText = keyLoggingElement.Text\n## self.assertTrue(keyLoggingElement.Text.EndsWith(\"keydown\"), \"Key down should be isolated for this test to be meaningful. Event text should end with 'keydown', got events: \" + eventsText)\n##\n## IAction releaseShift = actionProvider.KeyUp(keysEventInput, Keys.Shift).Build()\n##\n## releaseShift.Perform()\n##\n## eventsText = keyLoggingElement.Text\n## self.assertTrue(keyLoggingElement.Text.EndsWith(\"keyup\"), \"Key up event not isolated. Event text should end with 'keyup', got: \" + eventsText)\n\n## def test_ShouldAllowSendingKeysWithShiftPressed(self):\n## self.driver.get(self.webserver.where_is(\"user_input/res/javascriptPage.html\"))\n##\n## keysEventInput = self.driver.find_element_by_id(\"theworks\")\n##\n## keysEventInput.click()\n##\n## Actions actionProvider = new Actions(driver)\n## IAction pressShift = actionProvider.KeyDown(keysEventInput, Keys.Shift).Build()\n## pressShift.Perform()\n##\n## IAction sendLowercase = actionProvider.send_keys(keysEventInput, \"ab\").Build()\n## sendLowercase.Perform()\n##\n## IAction releaseShift = actionProvider.KeyUp(keysEventInput, Keys.Shift).Build()\n## releaseShift.Perform()\n##\n## AssertThatFormEventsFiredAreExactly(\"focus keydown keydown keypress keyup keydown keypress keyup keyup\") \n##\n## self.assertEqual(\"AB\", keysEventInput.get_attribute(\"value\"))\n\n def test_ShouldAllowSendingKeysToActiveElement(self):\n self.driver.get(self.webserver.where_is(\"user_input/res/bodyTypingPage.html\"))\n\n self.driver.find_element_by_id(\"body\").send_keys(\"ab\")\n\n self.assertEqual(\"keypress keypress\", self.driver.find_element_by_id(\"body_result\").text.strip())\n self.assertEqual(\"\", self.driver.find_element_by_id(\"result\").text.strip())\n\n def test_ShouldAllowBasicKeyboardInputOnActiveElement(self):\n self.driver.get(self.webserver.where_is(\"user_input/res/javascriptPage.html\"))\n\n keyReporter = self.driver.find_element_by_id(\"keyReporter\")\n\n keyReporter.click()\n\n self.driver.find_element_by_id(\"body\").send_keys(\"abc def\")\n\n self.assertEqual(\"abc def\", keyReporter.get_attribute(\"value\"))\n \nif __name__ == \"__main__\":\n unittest.main()\n\n","sub_path":"webdriver/user_input/basic_keyboard_interface_test.py","file_name":"basic_keyboard_interface_test.py","file_ext":"py","file_size_in_byte":4025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"39192563","text":"\"\"\"\nThis module contains the methods and classes to produce the encryption of messages.\n\"\"\"\n\nimport os\n\nfrom Crypto.Cipher import AES\nfrom secrets import token_bytes\n\nfrom parameters import N, ROOT_DIR\n\n\n# Size in bytes of each block supported by the cipher\nBLOCK_SIZE = AES.block_size\n\n# Size in bytes of the common Broadcast Key\nBROADCAST_KEY_SIZE = N * BLOCK_SIZE\n\n# File the common Broadcast Key is stored in\nBROADCAST_KEY_FILE = \"broadcast_key.pem\"\n\n\ndef _read_broadcast_key():\n \"\"\"Reads the common Broadcast Key from the proper file.\n :raises FileNotFoundError if the file doesn't exist\n :raises ValueError if the the length of the content of the file does not match BROADCAST_KEY_SIZE\n :return broadcast_key: the bytes sequence representing the common Broadcast Key\"\"\"\n with open(os.path.join(ROOT_DIR, BROADCAST_KEY_FILE), \"rb\") as f:\n broadcast_key = f.read()\n\n if not len(broadcast_key) == BROADCAST_KEY_SIZE:\n raise ValueError('Broadcast Key incorrent size')\n\n return broadcast_key\n\n\ndef _pad(msg):\n \"\"\"Pads a message with empty bytes in order to fit it as a proper input for the cipher.\n For example, if the message is 41 bytes long and the block size is 16 bytes, it will be padded with 7 bytes\"\"\"\n if not len(msg) % BLOCK_SIZE == 0:\n return msg + b'\\0' * (BLOCK_SIZE - len(msg) % BLOCK_SIZE)\n return msg\n\n\nclass Encryptor:\n \"\"\"Class containing parameters and methods to produce the encryption of messages\n :param key: the bytes sequence representing the private encryption key\n :param broadcast_key: the bytes sequence representing the common Broadcast Key\"\"\"\n\n __slots__ = ['__key', '__broadcast_key']\n\n def __init__(self, key):\n \"\"\"Class constructor\n :param key: the bytes sequence representing the private encryption key\"\"\"\n self.__key = key\n self.__broadcast_key = _read_broadcast_key()\n\n def encrypt(self, iv=None, msg=None):\n \"\"\"Produces the encryption of a message.\n :param iv: (Optional) the bytes sequence representing the Initialization Vector of the block cipher;\n if not specified, a new IV is created as a random bytes sequence\n :param msg: (Optional) the bytes sequence representing the message to encrypt;\n if not specified, the common Broadcast Key will be encrypted\n (note that the protocol states to always encrypt the Broadcast Key, with different private keys)\n :returns the concatenation of the IV and the ciphertext corresponding to the plaintext\"\"\"\n if msg is None:\n msg = self.__broadcast_key\n msg = _pad(msg)\n\n if iv is None:\n iv = token_bytes(AES.block_size)\n\n cipher = AES.new(self.__key, AES.MODE_CBC, iv)\n ciphertext = cipher.encrypt(msg)\n\n return iv + ciphertext\n\n\nclass Decryptor:\n \"\"\"Class containing parameters and methods to produce the decryption of ciphertexts\n :param key: the bytes sequence representing the private decryption key\n :param broadcast_key: the bytes sequence representing the common Broadcast Key\"\"\"\n\n __slots__ = ['__key', '__broadcast_key']\n\n def __init__(self, key):\n \"\"\"Class constructor\n :param key: the bytes sequence representing the private decryption key\"\"\"\n self.__key = key\n self.__broadcast_key = _read_broadcast_key()\n\n def decrypt(self, ciphertext):\n \"\"\"Produces the decryption of a ciphertext.\n :param ciphertext the bytes sequence representing the ciphertext to decrypt\n :returns the plaintext corresponding to the ciphertext\n :raises ValueError if the plaintext does not correspond to the common Broadcast Key\n (as the protocol states to always encrypt the Broadcast Key, with different private keys\"\"\"\n iv = ciphertext[:AES.block_size]\n cipher = AES.new(self.__key, AES.MODE_CBC, iv)\n plaintext = cipher.decrypt(ciphertext[AES.block_size:])\n\n if not plaintext == self.__broadcast_key:\n raise ValueError('Ciphertext not valid')\n\n return plaintext.rstrip(b'\\0')\n","sub_path":"TestCrypto/cipher.py","file_name":"cipher.py","file_ext":"py","file_size_in_byte":4100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"260797158","text":"from lxml import html, etree\nfrom multiprocessing import Pool\nfrom requests import get as req\nimport time, json, logging\n\nlogging.basicConfig(\n # this logging stuff MIGHT go into a different file\n filename='pytor.log',\n level=logging.DEBUG,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n)\nlogger = logging.getLogger(__name__)\n\ndef eztv (q):\n results = []\n htmlstr = html.fromstring\n url = f'https://eztv.ag/search/{q}'\n tree = htmlstr(req(url, timeout=3).content)\n x = tree.xpath\n add = results.append\n items = list(zip(\n x('//tr[@name=\"hover\"]//a[@class=\"epinfo\"]/@title'),\n x('//tr[@name=\"hover\"]/td[4]/text()'),\n x('//tr[@name=\"hover\"]/td[6]//text()'),\n x('//tr[@name=\"hover\"]/td[3]/a[1]/@href'),\n x('//tr[@name=\"hover\"]/td[1]/a/@title')\n ))\n for item in items:\n seed, link, show = item[2:]\n if 'Other' not in show and seed != '-' and int(seed) > 0 and link[0:6] == 'magnet':\n add([\n item[0][:-(len(item[1]) + 3)],\n item[1],\n seed,\n '-',\n link,\n link[20:60]\n ])\n print('eztv processed successfully...')\n return results\n\ndef lime (q, cat):\n htmlstr = html.fromstring\n url = f'https://www.limetorrents.cc/search/{cat}/{q}/seeds/1/'\n tree = htmlstr(req(url, timeout=3).content)\n x = tree.xpath\n items = list(zip(\n x('//div[@class=\"tt-name\"]//a[2]/text()'),\n x('//td[@class=\"tdnormal\"][2]/text()'),\n x('//table[2]//td[@class=\"tdseed\"]/text()'),\n x('//table[2]//td[@class=\"tdleech\"]/text()'),\n x('//a[@class=\"csprite_dl14\"]/@href')\n ))\n print('lime processed successfully...')\n return [list(item) + [item[4][29:69]] for item in items if int(item[2]) > 0]\n\ndef rarbg (q, cat, token):\n url = 'https://torrentapi.org/pubapi_v2.php'\n payload = {\n 'mode': 'search',\n 'search_string': q,\n 'category': cat,\n 'limit': '100',\n 'sort': 'seeders',\n 'min_seeders': '1',\n 'format': 'json_extended',\n 'token': token\n }\n items = json.loads(req(url, timeout=3, params=payload).text)['torrent_results']\n print('rarbg processed successfully...')\n keys = ['title', 'size', 'seeders', 'leechers', 'download']\n return [[item[key] for key in keys] + [item['download'][20:60]] for item in items]\n\ndef tpb (q, cat):\n results = []\n htmlstr = html.fromstring\n url = f'https://thepiratebay.org/search/{q}/0/7/200/'\n tree = htmlstr(req(url, timeout=3).content)\n x = tree.xpath\n add = results.append\n sep = str.split\n items = list(zip(\n x('//a[@class=\"detLink\"]/text()'),\n x('//font[@class=\"detDesc\"]/text()'),\n x('//td[@align=\"right\"][1]/text()'),\n x('//td[@align=\"right\"][2]/text()'),\n x('//a[contains(@href, \"magnet\")]/@href'),\n x('//td[@class=\"vertTh\"]//a[2]/text()')\n ))\n low = str.lower\n for item in items:\n if cat in low(item[5]) and int(item[2]) > 0:\n add([\n item[0],\n sep(item[1], ', ')[1][5:-2] + 'B',\n item[2],\n item[3],\n item[4],\n item[4][20:60]\n ])\n print('tpb processed successfully...')\n return results\n\ndef zoo (q, cat):\n xmlstr = etree.fromstring\n url = f'https://zooqle.com/search?q={q}+category%3A{cat}&sd=d&fmt=rss&pg='\n pages = [req(url + str(p), timeout=3).content for p in [1, 2, 3]]\n trees = [xmlstr(page)[0] for page in pages]\n items = list(zip(\n [item[0].text for tree in trees for item in tree[8:]],\n [int(item[6].text) for tree in trees for item in tree[8:]],\n [item[9].text for tree in trees for item in tree[8:]],\n [item[10].text for tree in trees for item in tree[8:]],\n [item[8].text for tree in trees for item in tree[8:]],\n [item[7].text for tree in trees for item in tree[8:]]\n ))\n print('zoo processed successfully...')\n return [list(item) for item in items]\n\ndef filtor (tors, q):\n # if ascii(s) !== s: proceed || all(ord(char) < 128 for char in tname)\n flags = ['rus', 'fuck', 'anal', 'xxx']\n sep = str.split\n qs = sep(q, '+')\n low = str.lower\n seen = {}\n for tor in tors:\n tname, tseed, thash = low(tor[0]), tor[2], tor[5]\n try:\n tname.encode('ascii')\n words = sep(tname.replace('.', ' '), ' ')\n if thash not in seen or int(tseed) > int(seen[thash][2]):\n if all(flag not in words for flag in flags) and all(term in tname for term in qs):\n seen[thash] = tor\n except UnicodeEncodeError:\n continue\n print('filtor processed successfully...')\n return seen.values()\n\ndef run_func (f):\n func, args = f\n try:\n if type(args) != tuple:\n return func(args)\n return func(*args)\n except Exception as err:\n logging.debug(err, exc_info=True)\n return []\n\ndef search (q, cat):\n logger.info('beginning search')\n torlist = []\n ext = torlist.extend\n jtkn = req('https://torrentapi.org/pubapi_v2.php?get_token=get_token')\n token = json.loads(jtkn.text)['token']\n\n funcs = [\n (eztv, q),\n (lime, (q, cat)),\n (rarbg, (q, cat, token)),\n (tpb, (q, cat)),\n (zoo, (q, cat))\n ]\n pool = Pool(5)\n tormaps = pool.map(run_func, funcs)\n pool.close()\n pool.join()\n [ext(tormap) for tormap in tormaps]\n print('# of results (pre-filtor) = ' + str(len(torlist)))\n logger.info('search complete, attempting to run filtor...')\n try:\n results = filtor(torlist, q)\n print('# of results (post-filtor) = ' + str(len(results)))\n return results\n except Exception as e:\n logging.exception(e)\n\nt = time.time()\ntorrents = search('the+librarians', 'tv')\nprint(time.time() - t)\n\n# Torrent = [name, size, seed, peer, link, hash]\n#\n# sort by seeders\n# do byte conversions when putting in html\n# from math import floor, pow, log\n# if type(tor[1]) is int:\n# i = int(floor(log(int(b), 1024)))\n# str(round(int(b) / pow(1024, i), 2)) + ' ' + ('KB', 'MB', 'GB')[i]\n# torlock has a good selection, cats, sorting, and 75 per page all 0 seeds\n# leetx no magnets\n# magnetdl has sorting, cats, magnets, 40 per page, but results are iffy correct seeds\n# http://www.magnetdl.com/t/the-librarians/se/desc/","sub_path":"Python/pytor_multiprocessing.py","file_name":"pytor_multiprocessing.py","file_ext":"py","file_size_in_byte":6462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"22492228","text":"import bisect\nimport os\nimport pickle\n\n\npage_size = 512\n\nclass _NodeInTree(object):\n __buckets__ = [\"tree\", \"value\", \"children\"]\n\n def __init__(self, tree, value=None, children=None):\n self.tree = tree\n self.value = value or []\n \n self.children = children or []\n if self.children:\n assert len(self.value) + 1 == len(self.children)\n\n def __repr__(self):\n name = getattr(self, \"children\", 0) and \"Branch\" or \"Leaf\"\n return \"<%s %s>\" % (name, \", \".join(map(str, self.value)))\n\n def lateral(self, parent, parent_ind, dest, dest_ind):\n if parent_ind > dest_ind:\n dest.value.append(parent.value[dest_ind])\n if self.children:\n dest.children.append(self.children.pop(0))\n else:\n dest.value.insert(0, parent.value[parent_ind])\n parent.value[parent_ind] = self.value.pop()\n if self.children:\n dest.children.insert(0, self.children.pop())\n\n def contract(self, predecessor):\n parent = None\n if predecessor:\n parent, parent_ind = predecessor.pop()\n # try to lend to the left neighboring sibling\n if parent_ind:\n left_sib = parent.children[parent_ind - 1]\n if len(left_sib.value) < self.tree.order:\n self.lateral(\n parent, parent_ind, left_sib, parent_ind - 1)\n return\n\n # try the right neighbor\n if parent_ind + 1 < len(parent.children):\n right_sib = parent.children[parent_ind + 1]\n if len(right_sib.value) < self.tree.order:\n self.lateral(\n parent, parent_ind, right_sib, parent_ind + 1)\n return\n\n middle = len(self.value) // 2\n sibling, push = self.split()\n\n if not parent:\n parent, parent_ind = self.tree.BRANCH(\n self.tree, children=[self]), 0\n self.tree._root = parent\n\n # pass the median up to the parent\n parent.value.insert(parent_ind, push)\n parent.children.insert(parent_ind + 1, sibling)\n if len(parent.value) > parent.tree.order:\n parent.contract(predecessor)\n\n def expand(self, predecessor):\n parent, parent_ind = predecessor.pop()\n minm = self.tree.order // 2\n left_sib = right_sib = None\n\n # try to borrow from the right sibling\n if parent_ind + 1 < len(parent.children):\n right_sib = parent.children[parent_ind + 1]\n if len(right_sib.value) > minm:\n right_sib.lateral(parent, parent_ind + 1, self, parent_ind)\n return\n\n # try to borrow from the left sibling\n if parent_ind:\n left_sib = parent.children[parent_ind - 1]\n if len(left_sib.value) > minm:\n left_sib.lateral(parent, parent_ind - 1, self, parent_ind)\n return\n\n # consolidate with a sibling - try left first\n if left_sib:\n left_sib.value.append(parent.value[parent_ind - 1])\n left_sib.value.extend(self.value)\n if self.children:\n left_sib.children.extend(self.children)\n parent.value.pop(parent_ind - 1)\n parent.children.pop(parent_ind)\n else:\n self.value.append(parent.value[parent_ind])\n self.value.extend(right_sib.value)\n if self.children:\n self.children.extend(right_sib.children)\n parent.value.pop(parent_ind)\n parent.children.pop(parent_ind + 1)\n\n if len(parent.value) < minm:\n if predecessor:\n # parent is not the root\n parent.expand(predecessor)\n elif not parent.value:\n # parent is root, and its now empty\n self.tree._root = left_sib or self\n\n def split(self):\n middle = len(self.value) // 2\n median = self.value[middle]\n sibling = type(self)(\n self.tree,\n self.value[middle + 1:],\n self.children[middle + 1:])\n self.value = self.value[:middle]\n self.children = self.children[:middle + 1]\n return sibling, median\n\n def insert(self, ind, element, predecessor):\n self.value.insert(ind, element)\n if len(self.value) > self.tree.order:\n self.contract(predecessor)\n\n def remove(self, ind, predecessor):\n minm = self.tree.order // 2\n\n if self.children:\n # try promoting from the right subtree first,\n # but only if it won't have to resize\n add_ancestors = [(self, ind + 1)]\n descendent = self.children[ind + 1]\n while descendent.children:\n add_ancestors.append((descendent, 0))\n descendent = descendent.children[0]\n if len(descendent.value) > minm:\n predecessor.extend(add_ancestors)\n self.value[ind] = descendent.value[0]\n descendent.remove(0, predecessor)\n return\n\n # fall back to the left child\n add_ancestors = [(self, ind)]\n descendent = self.children[ind]\n while descendent.children:\n add_ancestors.append(\n (descendent, len(descendent.children) - 1))\n descendent = descendent.children[-1]\n predecessor.extend(add_ancestors)\n self.value[ind] = descendent.value[-1]\n descendent.remove(len(descendent.children) - 1, predecessor)\n else:\n self.value.pop(ind)\n if len(self.value) < minm and predecessor:\n self.expand(predecessor)\n\n\nclass Index_Btree(object):\n BRANCH = LEAF = _NodeInTree\n\n def __init__(self, order):\n self.order = order\n self._root = self._bottom = self.LEAF(self)\n\n def _path_to(self, element):\n curr = self._root\n ancestry = []\n\n while getattr(curr, \"children\", None):\n ind = bisect.bisect_left(curr.value, element)\n ancestry.append((curr, ind))\n if ind < len(curr.value) \\\n and curr.value[ind] == element:\n return ancestry\n curr = curr.children[ind]\n\n ind = bisect.bisect_left(curr.value, element)\n ancestry.append((curr, ind))\n present = ind < len(curr.value)\n present = present and curr.value[ind] == element\n\n return ancestry\n\n def _current(self, element, predecessor):\n last, ind = predecessor[-1]\n return ind < len(last.value) and last.value[ind] == element\n\n def insert(self, element, ):\n curr = self._root\n predecessor = self._path_to(element)\n node, ind = predecessor[-1]\n while getattr(node, \"children\", None):\n node = node.children[ind]\n ind = bisect.bisect_left(node.value, element)\n predecessor.append((node, ind))\n node, ind = predecessor.pop()\n node.insert(ind, element, predecessor)\n\n def search(self, element):\n curr = self._root\n if element in dict(self):\n return dict(self)[element]\n return None\n\n def remove(self, element):\n if self.search(element):\n element = [element, (self.search(element))]\n else:\n element = [element, None]\n curr = self._root\n predecessor = self._path_to(element)\n if self._current(element, predecessor):\n node, ind = predecessor.pop()\n node.remove(ind, predecessor)\n else:\n raise ValueError(\"%r not in %s\" % (element, self.__class__.__name__))\n\n \n\n def __contains__(self, element):\n return self._current(element, self._path_to(element))\n\n def __iter__(self):\n def _recurse(node):\n if node.children:\n for child, element in zip(node.children, node.value):\n for child_item in _recurse(child):\n yield child_item\n yield element\n for child_item in _recurse(node.children[-1]):\n yield child_item\n else:\n for element in node.value:\n yield element\n\n for element in _recurse(self._root):\n yield element\n\n \n\n def __repr__(self):\n def recurse(node, accum, depth):\n accum.append((\" \" * depth) + repr(node))\n for node in getattr(node, \"children\", []):\n recurse(node, accum, depth + 1)\n\n accum = []\n recurse(self._root, accum, 0)\n return \"\\n\".join(accum)\n\n\ndef insert_index_entry(table_name, column_name, key, value):\n file_list = os.listdir(data_dir)\n filename = str(table_name) + \"_\" + str(column_name) + \".ndx\"\n\n if filename not in file_list:\n \tdicti = {}\n \tdicti[key] = value\n \tinitialize_tree(table_name, column_name, dicti)\n else:\n tree = read_tree_from_file(table_name, column_name)\n tree.insert([key,value])\n write_tree_to_file(filename, tree)\n return \n\ndef remove_index_entry(table_name, column_name, key):\n file_list = os.listdir(data_dir)\n filename = str(table_name) + \"_\" + str(column_name) + \".ndx\"\n\n if filename not in file_list:\n \treturn False\n else:\n tree = read_tree_from_file(table_name, column_name)\n tree.remove(key)\n write_tree_to_file(filename, tree)\n return True\n\ndef initialize_tree(table_name, column_name, tree_values):\n\tnew_tree = Index_Btree(5)\n\tfor key, value in tree_values:\n\t\tnew_tree.insert([key, value])\n\tfilename = str(table_name) + \"_\" + str(column_name) + \".ndx\"\n\twrite_tree_to_file(filename, new_tree)\n\treturn\n\ndef write_tree_to_file(filename, new_tree):\n with open(filename, \"wb\") as f:\n pickle.dump(new_tree, f)\n return\n\n\ndef read_tree_from_file(table_name, column_name):\n filename = str(table_name) + \"_\" + str(column_name) + \".ndx\"\n with open(filename, \"rb\") as f:\n tree = pickle.load(f)\n return tree\n\ndef search(table_name, column_name, key):\n tree = read_tree_from_file(table_name, column_name)\n value = tree.search(key)\n return value\n\n# b = Index_Btree(5)\n# b.insert(['2', '5'])\n# b.insert(['7', '5'])\n# b.insert(['9', '5'])\n# b.insert(['3', '4'])\n# b.insert(['5', '4'])\n# b.insert(['1', '4'])\n# b.insert(['18', '4'])\n# b.insert(['11', '5'])\n# b.insert(['12', '5'])\n# b.insert(['13', '5'])\n# b.insert(['14', '4'])\n# b.insert(['15', '5'])\n# b.insert(['16', '5'])\n# b.insert(['17', '5'])\n# b.insert(['19', '4'])\n# print(b)\n# b.remove('17')\n# print(b)\n# print('\\n\\n')\n# value = b.search('11')\n# print(value)\n# print('\\n\\n\\n')\n# initialize_tree('test', 'test', b)\n# tree = read_tree_from_file('test', 'test')\n# tree.insert(['22','22'])\n# print(tree)\n","sub_path":"Index.py","file_name":"Index.py","file_ext":"py","file_size_in_byte":10895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"317289595","text":"#!/usr/bin/env python\nfrom __future__ import division, unicode_literals\n\nimport atexit\nimport base64\nimport httplib\nimport inspect\nimport json\nimport os\nimport re\nimport select\nimport signal\nimport socket\nimport ssl\nimport struct\nimport sys\nimport tempfile\nimport threading\nimport time\nimport zlib\n\nALL_MODULES = []\nALL_MODULES_CODE = '${ALL_CODE_LIST}'\nSCRIPT_NAME = b'${SCRIPT_NAME}'\nEXERCISE_DURATION = \"${EXERCISE_DURATION}\"\nMODULE_DELAYS = '${MODULE_DELAYS}'\n\n\nclass ModuleBase(object):\n VERSION = '0.1.0'\n\n def __init__(self, identification_banner):\n self._started = False\n self._finished = False\n self._banner = identification_banner\n\n @property\n def tags(self):\n return []\n\n @property\n def module_name(self):\n return self.__class__.__name__\n\n @property\n def needs_root(self):\n return False\n\n @property\n def finished(self):\n return self._finished\n\n @property\n def started(self):\n return self._started\n\n @property\n def relative_delay(self):\n # On a scale of 1 (least) to 100 (most) likely to get caught\n raise NotImplementedError('Specify an integer 0 <= relative_delay <= 100')\n\n @property\n def absolute_duration(self):\n # Number of seconds the module should wait\n # NOTE: This only affects computation of minimum test duration! You're responsible for sleeping!\n raise NotImplementedError('Specify an duration that the module expects to run')\n\n def util_childproc(self, fname=None, func=None, args=()):\n (r, w) = os.pipe()\n if os.fork() == 0:\n pid = os.fork()\n if pid == 0:\n if fname is not None:\n os.execv(fname, args)\n elif func is not None:\n func(*args)\n else:\n os.write(w, str(pid))\n sys.exit(0)\n os.wait()\n pid = int(os.read(r, 8))\n os.close(r)\n os.close(w)\n self.hec_logger('Created a new process', severity='info', process_id=pid)\n return pid\n\n def util_netconnect(self, host, timeout=60):\n def proxy(_sock, _host, _timeout):\n s = socket.socket()\n s.settimeout(_timeout)\n try:\n s.connect(_host)\n while True:\n r, _, _ = select.select([_sock, s], [], [], _timeout)\n if not r:\n raise Exception # reached timeout\n if s in r:\n if _sock.send(s.recv(1024)) <= 0:\n raise Exception # a socket closed\n if _sock in r:\n if s.send(_sock.recv(1024)) <= 0:\n raise Exception # a socket closed\n except:\n s.close()\n _sock.close()\n sys.exit()\n self.hec_logger('Creating outbound connection', severity='info', host=host)\n parent_sock, child_sock = socket.socketpair(socket.AF_UNIX)\n self.util_childproc(func=proxy, args=(child_sock, host, timeout))\n return parent_sock\n\n def util_orphanwait(self, pid, timeout=0):\n start = time.time()\n while True:\n if (time.time() - start > timeout) and timeout != 0:\n try:\n self.hec_logger('Timeout reached, killing process', severity='info', process_id=pid)\n os.kill(pid, signal.SIGINT)\n except Exception as e:\n self.hec_logger('Error killing process', process_id=pid, error=str(e))\n break\n try:\n os.kill(pid, 0)\n except OSError:\n self.hec_logger('Process no longer exists, exiting waitloop', severity='debug', pid=pid)\n break\n time.sleep(1)\n return\n\n def hec_logger(self, message, action='', severity='info', **kwargs):\n event = {\n 'project': '${PROJECT_NAME}',\n 'severity': severity,\n 'action': action,\n 'message': message,\n 'local_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())\n }\n event.update(kwargs)\n data = {\n 'source': self.module_name,\n 'host': socket.getfqdn(),\n 'sourcetype': 'smokeyjab:' + self.module_name,\n 'event': json.dumps(event)\n }\n headers = {'Authorization': 'Splunk ${SPLUNK_TOKEN}', 'Content-Type': 'application/json'}\n try:\n https = httplib.HTTPSConnection('${SPLUNK_HOST}', context=ssl._create_unverified_context())\n except:\n # \"context\" key must not be recognized (should be v2.7.9 but that doesn't seem to be strictly true)\n try:\n https = httplib.HTTPSConnection('${SPLUNK_HOST}')\n except:\n return\n https.request('POST', '/services/collector', body=json.dumps(data), headers=headers)\n\n def finish(self):\n self.hec_logger('', action='finish')\n self._finished = True\n\n def start(self):\n self.hec_logger('', action='start')\n self._started = True\n\n def run(self):\n # Your module functionality here\n raise NotImplementedError('Module functionality undefined')\n\nclass Utils(object):\n @staticmethod\n def routes():\n \"\"\" returns (iface, net address, subnet, gateway) tuple \"\"\"\n def lehex2ip(x):\n return socket.inet_ntoa(x.decode('hex')[::-1])\n with open('/proc/net/route') as f:\n for i, line in enumerate(f):\n if i == 0:\n continue\n line = line.split()\n yield (line[0], lehex2ip(line[1]), lehex2ip(line[7]), lehex2ip(line[2]))\n\n @staticmethod\n def subnet2list(ip, subnet):\n ipi, = struct.unpack(b'!I', socket.inet_aton(ip))\n maski, = struct.unpack(b'!I', socket.inet_aton(subnet))\n for i in range((ipi & maski) + 1, ipi | (maski ^ 0xffffffff)):\n yield socket.inet_ntoa(struct.pack(b'!I', i))\n\n @staticmethod\n def cidr2list(ip, cidr):\n ipi, = struct.unpack(b'!I', socket.inet_aton(ip))\n maski = (0xffffffff << (32-cidr)) & 0xffffffff\n for i in range((ipi & maski) + 1, ipi | (maski ^ 0xffffffff)):\n yield socket.inet_ntoa(struct.pack(b'!I', i))\n\n @staticmethod\n def iface2ip(iface):\n import socket, struct, fcntl\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n return socket.inet_ntoa(fcntl.ioctl(\n s.fileno(),\n 0x8915, # SIOCGIFADDR\n struct.pack(b'256s', iface[:15])\n )[20:24])\n\n\ndef hide():\n with open('/proc/self/cmdline', 'rb') as f:\n cmdline = f.read()\n with open('/proc/self/maps') as f:\n maps = f.read()\n stack_start, stack_end = re.search(b'([0-9a-f]+)-([0-9a-f]+).*\\[stack\\]', maps).groups()\n with open('/proc/self/mem', 'rb+') as mem:\n mem.seek(int(stack_start, 16))\n stack = mem.read(int(stack_end, 16) - int(stack_start, 16))\n cmd_index = stack.find(cmdline)\n arg1_index = stack.find(b'\\x00', cmd_index) + 1\n newargs = SCRIPT_NAME + b'\\x00' * (len(cmdline) - len(SCRIPT_NAME))\n mem.seek(int(stack_start, 16) + arg1_index)\n mem.write(newargs)\n os.unlink(__file__)\n return\n\n\ndef load_modules(modules_string):\n exec (zlib.decompress(base64.b64decode(modules_string)), globals(), locals())\n is_root = (os.getuid() == 0) or (os.geteuid() == 0)\n for name, item in locals().items():\n if inspect.isclass(item) and issubclass(item, ModuleBase) and getattr(item, 'VERSION', -1) >= 0:\n plugin = item('${REDTEAM_TAG}')\n if plugin.needs_root and not is_root:\n plugin.hec_logger('Module requires root and we are not root: {0}'.format(plugin.module_name),\n severity='warning')\n continue\n ALL_MODULES.append(plugin)\n\n\ndef get_all_status():\n statuses = []\n for module in ALL_MODULES:\n statuses.append(module.finished)\n return statuses\n\n\ndef time_breakdown(_s):\n _s, s = divmod(int(_s), 60)\n _s, m = divmod(_s, 60)\n _s, h = divmod(_s, 24)\n return (_s, h, m, s)\n\n\ndef __start__():\n hide()\n load_modules(ALL_MODULES_CODE)\n main = ModuleBase('core')\n main.hec_logger('Starting framework', action='start', severity='info', num_modules=len(ALL_MODULES),\n pid=os.getpid())\n atexit.register(main.hec_logger, 'Framework is exiting', action='exit', severity='info', pid=os.getpid())\n for module in ALL_MODULES:\n wait_time = int(json.loads(MODULE_DELAYS).get(module.module_name, module.relative_delay/100.0*int(EXERCISE_DURATION)))\n threading.Timer(wait_time, module.run).start()\n main.hec_logger('Spawned a module thread'.format(module.module_name), severity='debug', ioc=module.module_name,\n delay='{0:>02}:{1:>02}:{2:>02}:{3:>02}'.format(*time_breakdown(wait_time)))\n while not all(get_all_status()):\n # reap/report zombies created by lazy coding... ;-)\n try:\n pid, ret, res = os.wait3(os.WNOHANG)\n if pid != 0:\n main.hec_logger('Cleaned up a zombie process', severity='warning', pid=pid)\n except OSError:\n pass\n # Sleep before polling to keep CPU usage down\n time.sleep(1)\n main.hec_logger('Terminating framework normally', action='finish', severity='info', pid=os.getpid())\n\n\nif __name__ == '__main__':\n __start__()\n","sub_path":"framework/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"257373866","text":"from time import strftime\nimport time\n\n# Read parameter file\n\n# File name and time\nexperimentName = raw_input('Enter file name: ')\nfileDate = strftime('%d-%m-%y')\nstart = time.time()\nstarttime = str(time.asctime(time.localtime(start)))\nfileName = ('/home/pi/ExperimentData/' + fileName + '--' + fileDate + '.csv')\n# Put file headers\nf = open(fileName , 'a')\nf.write('Experiment Start Time = ' + starttime + '\\n' )\nf.write('Experiment time' + ',' + 'Temp' + ',' + 'Ice Thickness' + ',' + 'Light Level' + ',' + 'Windspeed' + ',' + 'Freezer ON/OFF' + ',' + 'Lights ON/OFF' + ',' + 'Fans ON/OFF' + '\\n')\nf.close\n\n# Experiment loop\nfor x in range (0, 1):\n\t# Sensor inputs\n\t\n\t# Process\n\t\n\t# Set output\n\t\n\t# Save readings\n\telapsedTime = str(datetime.timedelta(seconds = int(\"%.0f\" % (time.time() - start))))\n\tf = open(fileName , 'a')\n\tf.write(elapsedTime + ',' + temp + ',' + iceThickness + ',' + lightlevel + ',' + windspeed + ',' + freezerPow + ',' + lightPow + ',' + fanPow + ',' + '\\n')\n\tf.close","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"411937767","text":"#!/usr/bin/env python\n\ndef is_prime(num, list):\n for item in list:\n if (num % item == 0):\n return False\n return True\n\nlist = []\ncurr = 1\nwhile(len(list) < 10001):\n curr = curr + 1\n if is_prime(curr, list):\n list.append(curr)\n\nprint (list[10000])\n","sub_path":"07/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"589415604","text":"balance = 0\n\n\ndef setBalance(amt):\n global balance\n balance = amt\n\n\ndef printBalance(): # Displays current balance as a money value with a heading\n print(balance)\ndef printLedgerLine(date, details, amount): # with items (and the balance) spaced and formatted\n print(\"{0:<15s}{1:<15s}{2:<15f}\".format(date,details,amount))\n\ndef deposit (date, details, amount): # Alter the balance and print ledger line\n global balance\n balance+=amount\n printLedgerLine(date, details, amount)\n\ndef withdraw(date, details, amount): # Alter the balance and print ledger line\n global balance\n balance -= amount\n printLedgerLine(date, details, amount)\n\nsetBalance(500)\nprintBalance()\nwithdraw(\"17-12-2012\", \"BP - petrol\", 72.50)\nwithdraw(\"19-12-2012\", \"Countdown\", 55.50)\nwithdraw(\"20-12-2012\", \"munchies\", 1.99)\nwithdraw(\"22-12-2012\", \"Vodafone\", 20)\ndeposit (\"23-12-2012\", \"Income\", 225)\nwithdraw(\"24-12-2012\", \"Presents\", 99.02)\nprintBalance()","sub_path":"pycharm/159171/workshop 9/banker.py","file_name":"banker.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"238813183","text":"from flask import Flask, request, g, jsonify, json\nfrom flask.ext.cors import CORS\nfrom flask_mail import Mail, Message\nfrom elasticsearch import Elasticsearch\nfrom datetime import datetime, timezone\n\nfrom error import APIError\n\napp = Flask(__name__)\n\n# TODO: test the behaviour over POST (with no doc_id) -- should just work\n# TODO: do server-side validation (JSON schema?)\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-put-mapping.html\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-percolate.html\n@app.route(\"/alerts/\", methods=[\"POST\", \"PUT\"])\n@app.route(\"/alerts//\", methods=[\"PUT\"])\ndef index_alert(doc_id=None):\n # grab the doc body if it exists\n body = request.get_json()\n\n # check required xavier fields (elastic fields will be checked downstream)\n # TODO: test when not sending a body, and when sending a body with a non-JSON content-type\n if body is None:\n raise APIError(\"Missing payload body\", status_code=400)\n\n if body[\"query\"] is None:\n raise APIError(\"Missing alert query\", status_code=400)\n if body[\"enabled\"] is None:\n raise APIError(\"Missing alert enabled flag\", status_code=400)\n if body[\"title\"] is None:\n raise APIError(\"Missing alert title\", status_code=400)\n\n alert_body = {\n \"query\": body[\"query\"],\n \"xavierAlertObjectMeta\": {\n \"title\": body[\"title\"],\n \"description\": body[\"description\"],\n \"enabled\": True,\n \"output\": body[\"output\"]\n }\n }\n\n try:\n index_res = _index(index=app.config[\"XAVIER_INDEX\"], doc_id=doc_id, doc_type=\".percolator\", body=alert_body, **g.params)\n except:\n # pass through any exceptions\n raise\n\n if index_res[\"created\"] == True:\n response_status = 201\n elif index_res[\"created\"] == False and index_res[\"_version\"] > 1:\n response_status = 200\n\n return jsonify(index_res), response_status\n\n# TODO: test the behaviour over POST (with no doc_id) -- should just work\n# this function allows you to create generic docs in elastic, with an option to percolate\n# as the doc is being created (ie. in real time). this is a convenience/helper function\n# so you can do a 2-in-1, create + percolate, instead of using the elastic\n# HTTP REST API to create and then percolate\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-index_.html\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-put-mapping.html\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-percolate.html\n@app.route(\"/docs/\", methods=[\"POST\", \"PUT\"])\n@app.route(\"/docs////\", methods=[\"PUT\"])\ndef index_doc(index=None, doc_id=None, doc_type=None):\n response = None\n\n # grab the doc body if it exists\n body = request.get_json()\n\n # pop the percolate flag if it exists\n percolate = g.params.pop(\"percolate\", False)\n\n # pop the trigger flag if it exists\n trigger = g.params.pop(\"trigger\", False)\n\n try:\n index_res = _index(index=index, doc_id=doc_id, doc_type=doc_type, body=body, **g.params)\n except:\n # pass through any exceptions\n raise\n\n if index_res[\"created\"] == True:\n response_status = 201\n elif index_res[\"created\"] == False and index_res[\"_version\"] > 1:\n response_status = 200\n\n if percolate:\n percolate_res = _percolate(index=index, doc_id=doc_id, doc_type=doc_type, body=body, trigger=trigger, **g.params)\n response[\"percolate\"] = percolate_res\n response[\"index\"] = index_res\n else:\n response = index_res\n\n return jsonify(response), response_status\n\ndef _index(index, doc_id, doc_type, body, **params):\n # check required fields\n if index is None:\n raise APIError(\"Missing param 'index'\", status_code=400)\n if doc_id is None:\n raise APIError(\"Missing param 'doc_id'\", status_code=400)\n if doc_type is None:\n raise APIError(\"Missing param 'doc_type'\", status_code=400)\n if body is None:\n raise APIError(\"Missing payload body\", status_code=400)\n\n log_details = {}\n log_details[\"action\"] = \"indexing\"\n log_details[\"phase\"] = \"pre\"\n log_details[\"index\"] = index\n log_details[\"doc_id\"] = doc_id\n log_details[\"doc_type\"] = doc_type\n log_details[\"body\"] = body\n _log(log_details, level=\"info\")\n\n try:\n es_index_res = g.es_conn.index(index=index, id=doc_id, doc_type=doc_type, body=body, **params)\n except Exception as error:\n _handle_es_error(error)\n\n log_details = {}\n log_details[\"action\"] = \"indexing\"\n log_details[\"phase\"] = \"post\"\n log_details[\"es_res\"] = es_index_res\n _log(log_details, level=\"debug\")\n\n return es_index_res\n\n@app.route(\"/alerts/\", methods=[\"GET\"])\n@app.route(\"/alerts//\", methods=[\"GET\"])\ndef read_alert(doc_id=None):\n # grab the doc body if it exists\n body = request.get_json()\n\n # pop the count flag if it exists\n count = g.params.pop(\"count\", False)\n\n if count:\n # count all the percolator docs within the xavier index\n try:\n count = _count(index=app.config[\"XAVIER_INDEX\"], doc_type=\".percolator\", body=body, **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(count)\n\n else:\n # search for all percolator docs within the associated index that match the query/search body\n try:\n matches = _search(index=app.config[\"XAVIER_INDEX\"], doc_id=doc_id, doc_type=\".percolator\", body=body, **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(matches)\n\n@app.route(\"/docs/\", methods=[\"GET\"])\n@app.route(\"/docs///\", methods=[\"GET\"])\ndef read_doc(index=None, doc_id=None):\n # grab the doc body if it exists\n body = request.get_json()\n\n # search for all percolator docs within the associated index that match the query/search body\n try:\n matches = _search(index=index, doc_id=doc_id, body=body, **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(matches)\n\n# a wrapper around search for general queries\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search.html\n@app.route(\"/search/\", methods=[\"GET\"])\n@app.route(\"/search//\", methods=[\"GET\"])\n@app.route(\"/search///\", methods=[\"GET\"])\n@app.route(\"/search////\", methods=[\"GET\"])\ndef search(index=\"_all\", doc_id=None, doc_type=None):\n # grab the doc body if it exists\n body = request.get_json()\n\n # search for all docs within the associated index that match the query/search body\n try:\n matches = _search(index=index, doc_id=doc_id, doc_type=doc_type, body=body, **g.params)\n except Exception:\n # pass through any exceptions\n raise\n\n return jsonify(matches)\n\ndef _search(index, doc_id, doc_type, body, **params):\n # check required fields\n if index is None:\n raise APIError(\"Missing param 'index'\", status_code=400)\n\n log_details = {}\n log_details[\"action\"] = \"searching\"\n log_details[\"phase\"] = \"pre\"\n log_details[\"index\"] = index\n log_details[\"doc_id\"] = doc_id\n log_details[\"doc_type\"] = doc_type\n log_details[\"body\"] = body\n _log(log_details, level=\"info\")\n\n # search by doc_id\n if doc_id is not None:\n try:\n es_res = g.es_conn.get(index=index, id=doc_id, **params)\n except Exception as error:\n _handle_es_error(error)\n\n # search by potential body, potential q, or return all docs\n elif doc_id is None:\n # grab the doc body if it exists\n if body is not None:\n params[\"body\"] = body\n\n try:\n es_res = g.es_conn.search(index=index, doc_type=doc_type, **params)\n except Exception as error:\n _handle_es_error(error)\n\n log_details = {}\n log_details[\"action\"] = \"searching\"\n log_details[\"phase\"] = \"post\"\n # log_details[\"es_res\"] = es_res\n _log(log_details, level=\"debug\")\n\n return es_res\n\ndef _count(index, doc_type, body, **params):\n # check required fields\n if index is None:\n raise APIError(\"Missing param 'index'\", status_code=400)\n\n log_details = {}\n log_details[\"action\"] = \"counting\"\n log_details[\"phase\"] = \"pre\"\n log_details[\"index\"] = index\n log_details[\"doc_type\"] = doc_type\n _log(log_details, level=\"info\")\n\n try:\n es_count_res = g.es_conn.count(index=index, doc_type=doc_type, source=body, **params)\n except Exception as error:\n _handle_es_error(error)\n\n log_details = {}\n log_details[\"action\"] = \"counting\"\n log_details[\"phase\"] = \"post\"\n log_details[\"es_res\"] = es_count_res\n _log(log_details, level=\"debug\")\n\n return es_count_res\n\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-delete.html\n@app.route(\"/alerts/\", methods=[\"DELETE\"])\n@app.route(\"/alerts//\", methods=[\"DELETE\"])\ndef delete_alert(doc_id=None):\n try:\n delete_res = _delete(index=app.config[\"XAVIER_INDEX\"], doc_id=doc_id, doc_type=\".percolator\", **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(delete_res)\n\n# this deletes a specific doc by its id. supporting bulk deletes or delete by query is risky.\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-delete.html\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/docs-delete-by-query.html\n@app.route(\"/docs/\", methods=[\"DELETE\"])\n@app.route(\"/docs////\", methods=[\"DELETE\"])\ndef delete_doc(index=None, doc_id=None, doc_type=None):\n try:\n delete_res = _delete(index=index, doc_id=doc_id, doc_type=doc_type, **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(delete_res)\n\ndef _delete(index, doc_id, doc_type, **params):\n # check required fields\n if index is None:\n raise APIError(\"Missing param 'index'\", status_code=400)\n if doc_id is None:\n raise APIError(\"Missing param 'doc_id'\", status_code=400)\n if doc_type is None:\n raise APIError(\"Missing param 'doc_type'\", status_code=400)\n\n log_details = {}\n log_details[\"action\"] = \"deleting\"\n log_details[\"phase\"] = \"pre\"\n log_details[\"index\"] = index\n log_details[\"doc_id\"] = doc_id\n log_details[\"doc_type\"] = doc_type\n _log(log_details, level=\"info\")\n\n try:\n es_delete_res = g.es_conn.delete(index=index, doc_type=doc_type, id=doc_id, **params)\n except Exception as error:\n _handle_es_error(error)\n\n log_details = {}\n log_details[\"action\"] = \"deleting\"\n log_details[\"phase\"] = \"post\"\n log_details[\"es_res\"] = es_delete_res\n _log(log_details, level=\"debug\")\n\n return es_delete_res\n\n# send docs to percolate and get back the queries that match the doc\n# http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-percolate.html\n@app.route(\"/percolate/\", methods=[\"GET\", \"POST\"])\n# this route is for percolating by doc in the body\n@app.route(\"/percolate///\", methods=[\"POST\"])\n# this route is for percolating by doc id\n@app.route(\"/percolate////\", methods=[\"GET\"])\n# in both cases, we need to provide the index and the doc type\ndef percolate(index=None, doc_id=None, doc_type=None):\n # grab the doc body if it exists\n body = request.get_json()\n\n # pop the trigger flag if it exists\n trigger = g.params.pop(\"trigger\", False)\n\n # pop the percolate_index if it exists\n # by default, xavier will assume that clients are percolating against\n # the xavier index. clients can override this behaviour by providing\n # their own percolate_index param. the default elastic behaviour is\n # to percolate against the supplied document's index.\n percolate_index = g.params.pop(\"percolate_index\", app.config[\"XAVIER_INDEX\"])\n\n try:\n matches = _percolate(index=index, doc_id=doc_id, doc_type=doc_type, body=body, percolate_index=percolate_index, trigger=trigger, **g.params)\n except:\n # pass through any exceptions\n raise\n\n return jsonify(matches)\n\ndef _percolate(index, doc_id, doc_type, body, percolate_index=None, trigger=False, **params):\n # check required fields\n if index is None:\n raise APIError(\"Missing param 'index'\", status_code=400)\n # one of doc_id or body should exist, but not both\n if doc_id is None and body is None:\n raise APIError(\"Missing param 'doc_id' or payload body\", status_code=400)\n elif doc_id is not None and body is not None:\n raise APIError(\"Found param 'doc_id' and payload body (only one must exist)\", status_code=400)\n\n # TODO: try to comment these out and see what happens if you just provide None to the API\n # if percolate_index is None:\n # percolate_index = index\n\n log_details = {}\n log_details[\"action\"] = \"percolating\"\n log_details[\"phase\"] = \"pre\"\n log_details[\"index\"] = index\n log_details[\"doc_id\"] = doc_id\n log_details[\"doc_type\"] = doc_type\n log_details[\"body\"] = body\n log_details[\"percolate_index\"] = percolate_index\n log_details[\"trigger\"] = trigger\n _log(log_details, level=\"info\")\n\n # percolate by doc_id\n if doc_id is not None and body is None:\n try:\n es_percolate_res = g.es_conn.percolate(index=index, id=doc_id, doc_type=doc_type, percolate_index=percolate_index, **params)\n except Exception as error:\n _handle_es_error(error)\n\n # percolate by doc_type and body\n elif doc_id is None and body is not None:\n doc_body = {\"doc\": body}\n try:\n es_percolate_res = g.es_conn.percolate(index=index, body=doc_body, doc_type=doc_type, **params)\n except Exception as error:\n _handle_es_error(error)\n\n log_details = {}\n log_details[\"action\"] = \"percolating\"\n log_details[\"phase\"] = \"post\"\n log_details[\"es_res\"] = es_percolate_res\n _log(log_details, level=\"debug\")\n\n if trigger:\n _trigger(matches=es_percolate_res[\"matches\"], doc_id=doc_id, body=body)\n\n return es_percolate_res\n\ndef _trigger(matches, doc_id, body):\n # fetch the percolator doc for each match\n for match in matches:\n # search for all percolator docs within the associated index that match the query/search body\n try:\n percolator_doc = _search(index=match[\"_index\"], doc_id=match[\"_id\"], doc_type=\".percolator\", body=body)\n except:\n # pass through any exceptions\n raise\n\n query = percolator_doc[\"_source\"][\"query\"]\n meta = percolator_doc[\"_source\"][\"xavierAlertObjectMeta\"]\n\n if meta[\"enabled\"]:\n log_details = {}\n log_details[\"action\"] = \"triggering\"\n log_details[\"percolator_id\"] = percolator_doc[\"_id\"]\n log_details[\"enabled\"] = meta[\"enabled\"]\n log_details[\"query\"] = query\n _log(log_details)\n\n # take action on the percolator doc outputs\n # to begin with, we are only doing emails (there will only be one)\n email_output = meta[\"output\"][0].get(\"email\", None)\n if email_output is not None:\n _email(email=email_output, percolator_id=percolator_doc[\"_id\"], query=query, meta=meta, doc_id=doc_id, body=body)\n\ndef _email(email, percolator_id, query, meta, doc_id, body):\n log_details = {}\n log_details[\"action\"] = \"emailing\"\n _log(log_details)\n\n msg = Message()\n msg.subject = _expand(email[\"subject\"], percolator_id=percolator_id, query=query, meta=meta, doc_id=doc_id, body=body)\n msg.recipients = email[\"to\"]\n msg.sender = email.get(\"from\", app.config[\"MAIL_DEFAULT_SENDER\"])\n msg.body = _expand(email[\"body\"], percolator_id=percolator_id, query=query, meta=meta, doc_id=doc_id, body=body)\n mail.send(msg)\n\n# expands the placeholders in the output fields to their actual values\ndef _expand(content, percolator_id, query, meta, doc_id, body):\n try:\n content = content.replace(\"$percolator_id$\", percolator_id)\n content = content.replace(\"$title$\", meta[\"title\"])\n content = content.replace(\"$description$\", meta[\"description\"])\n content = content.replace(\"$query$\", json.dumps(query, indent=2))\n\n # if either of these is null, consider looking up the doc to get the values\n if doc_id is not None:\n content = content.replace(\"$doc_id$\", doc_id)\n if body is not None:\n content = content.replace(\"$body$\", str(body))\n\n except Exception as e:\n log_details = {}\n log_details[\"error\"] = \"unable to expand placeholder\"\n log_details[\"detail\"] = str(e)\n _log(log_details, level=\"error\")\n pass\n\n return content\n\n# api ping to support heartbeat checking\n@app.route(\"/ping/\", methods=[\"GET\"])\ndef ping():\n ping_details = {\"status\": \"OK\"}\n return jsonify(ping_details)\n\n# detailed api health, elastic nodes and health\n@app.route(\"/\", methods=[\"GET\"])\n@app.route(\"/health/\", methods=[\"GET\"])\ndef health():\n # TODO: add more health details\n health_details = {\n \"xavier\": {\n \"api\": {\n \"health\": \"OK\"\n }\n }\n }\n\n try:\n # get the elastic cluster details from the standard elastic query params\n es_url = request.args.get(\"es_url\", None)\n es_conn = _get_elasticsearch_conn(es_url)\n\n es_health = es_conn.cluster.health()\n es_nodes = es_conn.nodes.info()\n\n # add elastic health details\n health_details[\"elastic\"] = {\n \"health\": es_health,\n \"nodes\": es_nodes\n }\n except TypeError:\n # the es_url query param has probably not been specified, so ignore the elastic health\n pass\n except Exception as es_error:\n _handle_es_error(es_error)\n\n return jsonify(health_details)\n\n@app.before_request\ndef _pre_process_request():\n # save the initial timestamp\n g.timestamp_start = datetime.now(timezone.utc)\n\n try:\n # get the request id from heroku\n g.request_id = request.headers[\"X-Request-ID\"]\n except KeyError:\n # the app is local or not on heroku, generate a random request_id instead\n import uuid\n random_id = str(uuid.uuid4()).replace(\"-\", \"\").lower()[:8]\n g.request_id = random_id\n\n # save a copy of the query params\n g.params = request.args.copy()\n\n # get the elastic cluster details from the standard elastic query params\n g.es_url = g.params.pop(\"es_url\", None)\n\n # log default/common details\n log_details = {}\n log_details[\"method\"] = request.method\n # TODO: fix, the headers are not serializeable to string\n # log_details[\"headers\"] = json.loads(request.headers)\n log_details[\"remote_addr\"] = request.remote_addr\n log_details[\"url\"] = request.url\n log_details[\"path\"] = request.path\n _log(log_details, category=\"traffic\")\n\n # ensure we have es_url for backend-related services\n if request.path != \"/\" and request.path != \"/ping/\":\n if g.es_url is None:\n raise APIError(\"Missing param 'es_url' for the Elastic server\", status_code=400)\n\n try:\n g.es_conn = _get_elasticsearch_conn(g.es_url)\n except:\n raise APIError(\"Could not connect to Elastic server'\", status_code=502)\n\n # ensure that the xavier index exists\n try:\n _ensure_index()\n except:\n raise APIError(\"Could not find or create Xavier index in Elastic server\", status_code=500)\n\n@app.after_request\ndef _post_process_request(response):\n log_details = {}\n log_details[\"duration\"] = _get_duration()\n log_details[\"response_status\"] = int(json.dumps(response.status_code))\n # response_status = int(json.dumps(response.status_code))\n # log_details[\"response_status\"] = response_status\n # if response_status >= 400:\n # log_details[\"response\"] = str(response)\n\n _log(log_details, category=\"traffic\")\n return response\n\n@app.errorhandler(APIError)\ndef _handle_api_error(error):\n log_details = {}\n log_details[\"response_status\"] = error.status_code\n\n # pass through error from elastic (upstream), as is\n if error.passthrough:\n log_details[\"error\"] = error.msg\n log_details[\"source\"] = \"elastic\"\n\n pt_error = error.to_dict()\n pt_error[\"error\"] = error.msg\n pt_error[\"source\"] = \"elastic\"\n response = jsonify(pt_error)\n else:\n log_details[\"error\"] = error.msg\n log_details[\"source\"] = \"xavier\"\n response = jsonify(error.to_dict())\n\n _log(log_details, level=\"error\", category=\"audit\")\n\n response.status_code = error.status_code\n return response\n\ndef _log(log_details, level=\"info\", category=\"audit\"):\n import os\n\n log_details[\"@timestamp\"] = datetime.now(timezone.utc).isoformat()\n log_details[\"type\"] = app.config[\"APP_NAME\"]\n log_details[\"category\"] = category\n log_details[\"pid\"] = os.getpid()\n log_details[\"request_id\"] = g.request_id\n log_details[\"es_url\"] = g.es_url\n\n # get the dyno id from heroku\n dyno = os.environ.get(\"DYNO\", None)\n if dyno is not None:\n log_details[\"dyno\"] = os.environ.get(\"DYNO\", None)\n\n # NOTE: SysLogHandler doesn't send messages in RFC3164 format, which is the\n # only format that logstash's syslog input supports. as such, the facility\n # and priority will be lost, and we will add an explicit log level field.\n if level == \"critical\":\n log_details[\"level\"] = \"critical\"\n app.logger.critical(json.dumps(log_details))\n elif level == \"error\":\n log_details[\"level\"] = \"error\"\n app.logger.error(json.dumps(log_details))\n elif level == \"warning\":\n log_details[\"level\"] = \"warning\"\n app.logger.warning(json.dumps(log_details))\n elif level == \"info\":\n log_details[\"level\"] = \"info\"\n app.logger.info(json.dumps(log_details))\n elif level == \"debug\":\n log_details[\"level\"] = \"debug\"\n app.logger.debug(json.dumps(log_details))\n\n# return processing duration in milliseconds\ndef _get_duration():\n now = datetime.now(timezone.utc)\n difference = (now - g.timestamp_start).microseconds / 1000\n return difference\n\ndef _get_elasticsearch_conn(es_url):\n # use certifi for CA certificates\n # import certifi\n\n # TODO: regex break up the es_url to get the individual attributes for secure audit logging, then:\n # example:\n # es = Elasticsearch(\n # ['localhost', 'otherhost'],\n # http_auth=('user', 'secret'),\n # port=443,\n # use_ssl=True,\n # verify_certs=True,\n # ca_certs=certifi.where(),\n # )\n\n es_conn = Elasticsearch([es_url])\n\n log_details = {}\n # log_details[\"es_host\"] = es_host\n # log_details[\"es_port\"] = es_port\n # log_details[\"es_url_prefix\"] = es_url_prefix\n # log_details[\"es_username\"] = es_username\n # log_details[\"es_use_ssl\"] = es_use_ssl\n # log_details[\"msg\"] = \"connected to '%s'\" % es_host\n log_details[\"es_url\"] = es_url\n log_details[\"action\"] = \"connecting\"\n _log(log_details, level=\"debug\")\n\n return es_conn\n\ndef _handle_es_error(es_error):\n # set the full error message for the audit log\n try:\n error_full = json.loads(str(es_error))\n except:\n error_full = str(es_error)\n\n # set the short error message for the http response\n # http://elasticsearch-py.readthedocs.org/en/latest/exceptions.html\n # first, set the error to the message returned by elastic\n try:\n error_msg = str(es_error.error)\n except:\n # if that fails, set the error to the dict returned by elastic\n try:\n error_msg = str(es_error.info)\n except:\n # if that fails, set the error to the json string returned by elastic\n try:\n error_msg = json.loads(str(es_error))\n except:\n # if that fails, set the error to the plain string returned by elastic\n error_msg = str(es_error)\n\n # set the status code for the http response\n try:\n if es_error.status_code == \"N/A\":\n error_status = 502\n else:\n error_status = es_error.status_code\n except:\n error_status = 502\n\n log_details = { \"error\": error_full, \"response_status\": error_status }\n _log(log_details, level=\"debug\")\n\n raise APIError(error_msg, status_code=error_status, passthrough=True)\n\ndef _ensure_index():\n try:\n es_check_res = g.es_conn.indices.exists(index=app.config[\"XAVIER_INDEX\"])\n\n # TODO: have a specific error response for when the elastic server is unreachable... test\n if not es_check_res:\n log_details = {}\n log_details[\"detail\"] = \"xavier index does not exist\"\n log_details[\"index\"] = app.config[\"XAVIER_INDEX\"]\n _log(log_details)\n\n # create a mapping to enable the _timestamp for alerts as part of the body\n # http://elasticsearch-py.readthedocs.org/en/latest/api.html#indices\n # http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-create-index.html\n # test by adding to this to the query to get the _timestamp: ?fields=_timestamp,_source\n timestamp_mapping = {\n \"mappings\" : {\n \"_default_\" : {\n \"_timestamp\" : {\n \"enabled\": True,\n \"store\": True\n }\n }\n }\n }\n\n try:\n es_create_res = g.es_conn.indices.create(index=app.config[\"XAVIER_INDEX\"], body=timestamp_mapping)\n\n if not es_create_res[\"acknowledged\"]:\n log_details = {}\n log_details[\"error\"] = \"unable to create xavier index\"\n log_details[\"index\"] = app.config[\"XAVIER_INDEX\"]\n _log(log_details, level=\"critical\")\n else:\n log_details = {}\n log_details[\"action\"] = \"creating index\"\n log_details[\"phase\"] = \"post\"\n log_details[\"index\"] = app.config[\"XAVIER_INDEX\"]\n _log(log_details, level=\"debug\")\n\n except Exception as error:\n _handle_es_error(error)\n except Exception as error:\n _handle_es_error(error)\n\nif __name__ == \"__main__\":\n # import pydevd\n # pydevd.settrace()\n\n import os\n from syslog import LOG_LOCAL0\n\n app.config.from_object(os.environ[\"APP_CONFIG\"])\n\n cors = CORS(app)\n mail = Mail(app)\n\n # TODO: make the syslog handler optional\n # add a syslog log handler\n # NOTE: SysLogHandler doesn't send messages in RFC3164 format, which is the\n # only format that logstash's syslog input supports. as such, the facility\n # and priority will be lost, and we will add an explicit log level field.\n import logging\n from logging.handlers import SysLogHandler\n syslog_handler = SysLogHandler(address=(app.config[\"SYSLOG_HOST\"], app.config[\"SYSLOG_PORT\"]), facility=LOG_LOCAL0)\n # don't terminate syslog messages with a NUL byte\n syslog_handler.append_nul = False\n if app.debug:\n # when running in debug mode, show messages on all log levels\n syslog_handler.setLevel(logging.DEBUG)\n else:\n # otherwise, show messages on the 'warning' level or higher\n # this will ignore 'info' and 'debug'\n syslog_handler.setLevel(logging.WARNING)\n app.logger.addHandler(syslog_handler)\n\n # add some log details for the app\n log_details = {}\n log_details[\"@timestamp\"] = datetime.now(timezone.utc).isoformat()\n log_details[\"type\"] = app.config[\"APP_NAME\"]\n log_details[\"level\"] = \"info\"\n log_details[\"category\"] = \"audit\"\n log_details[\"action\"] = \"starting\"\n log_details[\"config\"] = app.config.copy()\n\n datehandler = lambda obj: \"$$$ TEST $$$\" if isinstance(obj, datetime) else None\n app.logger.info(json.dumps(log_details, default=datehandler))\n\n app.run(host=app.config[\"APP_HOST\"], port=app.config[\"APP_PORT\"])","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":28405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"63857812","text":"# -*- coding:utf-8 -*-\n\n\n# Given a 32-bit signed integer, reverse digits of an integer.\n#\n# Note:\n# Assume we are dealing with an environment that could only store integers within the 32-bit signed integer range: [−231,  231 − 1]. For the purpose of this problem, assume that your function returns 0 when the reversed integer overflows.\n#\n#  \n# Example 1:\n# Input: x = 123\n# Output: 321\n# Example 2:\n# Input: x = -123\n# Output: -321\n# Example 3:\n# Input: x = 120\n# Output: 21\n# Example 4:\n# Input: x = 0\n# Output: 0\n#\n#  \n# Constraints:\n#\n#\n# \t-231 <= x <= 231 - 1\n#\n#\n\n\nclass Solution(object):\n def reverse(self, x):\n \"\"\"\n :type x: int\n :rtype: int\n \"\"\"\n y=0\n s=1\n if(x<0):\n s=-1\n x=abs(x)\n while (x/10!=0):\n y=y*10+x%10\n x=x/10\n y=y*10+x%10\n if (y>=pow(2,31)):\n return 0\n else:\n return y*s\n","sub_path":"0007-reverse-integer/reverse-integer.py","file_name":"reverse-integer.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"599321106","text":"from itertools import chain\n\n\nfrom six import u\n\nfrom circuits import handler\n\nfrom circuits.protocols.irc import joinprefix, reply\nfrom circuits.protocols.irc import Message as _Message\n\nfrom circuits.protocols.irc.replies import ERR_NOSUCHNICK, ERR_NOSUCHCHANNEL\nfrom circuits.protocols.irc.replies import ERR_CANNOTSENDTOCHAN\n\n\nfrom ..plugin import BasePlugin\nfrom ..models import Channel, User\nfrom ..commands import BaseCommands\n\n\nclass Commands(BaseCommands):\n\n @handler(\"privmsg\", \"notice\")\n def on_privmsg_or_notice(self, event, sock, source, target, message):\n user = User.objects.filter(sock=sock).first()\n\n prefix = user.prefix or joinprefix(*source)\n\n if target and target[0] in (u(\"&\"), u(\"#\"),):\n channel = Channel.objects.filter(name=target).first()\n if channel is None:\n return ERR_NOSUCHCHANNEL(target)\n\n if u(\"n\") in channel.modes:\n if not user.oper and user not in channel.users:\n return ERR_CANNOTSENDTOCHAN(channel.name)\n\n if u(\"m\") in channel.modes:\n if not user.oper and user not in chain(channel.operators, channel.voiced):\n return ERR_CANNOTSENDTOCHAN(channel.name)\n\n self.notify(\n channel.users,\n _Message(u(\"PRIVMSG\"), target, message, prefix=prefix),\n user\n )\n else:\n user = User.objects.filter(nick=target).first()\n if user is None:\n return ERR_NOSUCHNICK(target)\n\n return reply(\n user.sock,\n _Message(\n event.name.upper(), target, message,\n prefix=prefix\n )\n )\n\n\nclass Message(BasePlugin):\n\n def init(self, *args, **kwargs):\n super(Message, self).init(*args, **kwargs)\n\n Commands(*args, **kwargs).register(self)\n","sub_path":"charla/plugins/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"235710078","text":"import sys, os\nsys.path.append(os.getcwd()) ## this lets python find src\nimport numpy as np\nimport matplotlib\n#matplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom chainconsumer import ChainConsumer\n#import healpy as hp\n#from healpy import Alm\nimport pickle, argparse\n#import logging\nfrom src.hierarchical import postprocess\nmatplotlib.rcParams.update(matplotlib.rcParamsDefault)\n\nif __name__ == '__main__':\n\n # Create parser\n parser = argparse.ArgumentParser(prog='postproc', usage='%(prog)s [options] rundir', description='run hierarchical postprocessing')\n\n # Add arguments\n parser.add_argument('rundir', metavar='rundir', type=str, help='The path to the run directory.')\n parser.add_argument('--outdir', metavar='outdir', type=str, help='The path to the output directory Defaults to rundir.',default=None)\n parser.add_argument('--model', metavar='model', type=str, help='Parameterized spatial model to use.', default='breivik2020')\n parser.add_argument('--Nwalkers', metavar='Nwalkers', type=int, help='Number of walkers.', default=50)\n parser.add_argument('--Nsamples', metavar='Nsamples', type=int, help='Number of desired samples.', default=10000)\n parser.add_argument('--Nburn', metavar='Nburn', type=int, help='Number of desired burn-in samples.', default=1000)\n parser.add_argument('--seed', metavar='seed', type=int, help='Desired seed for the rng.', default=None)\n parser.add_argument('--Nthread', metavar='Nthread', type=int, help='Number of desired cores for multiprocessing.', default=1)\n # execute parser\n args = parser.parse_args()\n\n\n paramfile = open(args.rundir + '/config.pickle', 'rb')\n ## things are loaded from the pickle file in the same order they are put in\n params = pickle.load(paramfile)\n inj = pickle.load(paramfile)\n parameters = pickle.load(paramfile)\n ## initualize the postprocessing class\n postprocessor = postprocess(args.rundir,params,inj,parameters)\n ## run the sampler\n sampler = postprocessor.hierarchical_sampler(model=args.model,Nwalkers=args.Nwalkers,Nsamples=args.Nsamples,Nburn=args.Nburn,rng=args.seed,Nthread=args.Nthread)\n ## plot\n chain = sampler.flatchain\n ## model use cases\n knowTrue = False\n if args.model=='breivik2020':\n npar=2\n post_parameters = ['$r_h$','$z_h$']\n ## deal with older config files and assign true values if known\n if 'fg_type' in inj.keys():\n if inj['fg_type'] == 'breivik2020':\n knowTrue = True\n truevals = [inj['rh'],inj['zh']]\n else:\n raise TypeError(\"Unknown model. Currently supported models: 'breivik2020'.\")\n cc = ChainConsumer()\n cc.add_chain(chain, parameters=post_parameters)\n cc.configure(smooth=False, kde=False, max_ticks=2, sigmas=np.array([1, 2]), label_font_size=18, tick_font_size=18, \\\n summary=False, statistics=\"max_central\", spacing=2, summary_area=0.95, cloud=False, bins=1.2)\n cc.configure_truth(color='g', ls='--', alpha=0.7)\n\n if knowTrue:\n fig = cc.plotter.plot(figsize=(16, 16), truth=truevals)\n else:\n fig = cc.plotter.plot(figsize=(16, 16))\n\n ## make axis labels to be parameter summaries\n sum_data = cc.analysis.get_summary()\n axes = np.array(fig.axes).reshape((npar, npar))\n\n # Adjust axis labels\n for ii in range(npar):\n ax = axes[ii, ii]\n\n # get the right summary for the parameter ii\n sum_ax = sum_data[post_parameters[ii]]\n err = [sum_ax[2] - sum_ax[1], sum_ax[1]- sum_ax[0]]\n\n if np.abs(sum_ax[1]) <= 1e-3:\n mean_def = '{0:.3e}'.format(sum_ax[1])\n eidx = mean_def.find('e')\n base = float(mean_def[0:eidx])\n exponent = int(mean_def[eidx+1:])\n mean_form = str(base) + ' \\\\times ' + '10^{' + str(exponent) + '} '\n else:\n mean_form = '{0:.3f}'.format(sum_ax[1])\n\n if np.abs(err[0]) <= 1e-2:\n err[0] = '{0:.4f}'.format(err[0])\n else:\n err[0] = '{0:.2f}'.format(err[0])\n\n if np.abs(err[1]) <= 1e-2:\n err[1] = '{0:.4f}'.format(err[1])\n else:\n err[1] = '{0:.2f}'.format(err[1])\n\n label = post_parameters[ii][:-1] + ' = ' + mean_form + '^{+' + err[0] + '}_{-' + err[1] + '}$'\n\n ax.set_title(label, {'fontsize':18}, loc='left')\n\n ## save\n if args.outdir is None:\n plt.savefig(args.rundir + '/postproc_corners.png', dpi=150)\n print(\"Posteriors plots printed in \" + args.rundir + \"/postproc_corners.png\")\n plt.close()\n np.savetxt(args.rundir+'/postprocessing_samples.txt',chain)\n else:\n plt.savefig(args.outdir + '/postproc_corners.png', dpi=150)\n print(\"Posteriors plots printed in \" + args.outdir + \"/postproc_corners.png\")\n plt.close()\n np.savetxt(args.outdir+'/postprocessing_samples.txt',chain)\n \n\n\n\n\n\n\n\n","sub_path":"blip/tools/hierarchical_postprocess.py","file_name":"hierarchical_postprocess.py","file_ext":"py","file_size_in_byte":4895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"288624159","text":"from os import path\nimport datetime\n\n\ndef get_name_module(file_path):\n \"\"\"\n\n (string) -> string\n\n Function get name of file from path without file extension\n\n \"\"\"\n file_name = path.basename(file_path)\n idx = file_name.index('.')\n return file_name[:idx]\n\n\ndef logger_path_decorator(file_path='file1.log'):\n def logger_decorator(function):\n \"\"\"\n\n (address of function) -> address of function\n\n Function-decorator which logging to text file about input and output data and start time of execute of function\n\n\n \"\"\"\n\n def wrapper(*args, **kwargs):\n\n # If file exists then create file\n if not path.exists(file_path):\n with open(file_path, 'w', encoding='utf-8'):\n pass\n\n with open(file_path, 'a', encoding='utf-8') as file:\n lines = f\"{datetime.datetime.now()}: Вызов функции \\\"{function.__name__}\\\":\\n\"\n lines += f\"Аргументы функции \\\"{function.__name__}\\\":\\n\"\n for number, argument in enumerate(args, 1):\n lines += f\"аргумент №{number}: {argument}\\n\"\n for key, value in kwargs.items():\n lines += f\"аргумент \\\"{key}\\\": {value}\\n\"\n return_value = function(*args, **kwargs)\n lines += f\"Возвращаемое значение функции равно: {return_value}\\n\"\n file.writelines(lines)\n return return_value\n return wrapper\n return logger_decorator\n\n\n# Example 1\n@logger_path_decorator()\ndef function1_to_decorate(**kwargs):\n summa = 0\n for key, value in kwargs.items():\n summa += value\n return str(summa)\n\n# Example 2\n@logger_path_decorator()\ndef function2_to_decorate(*args):\n summa = 0\n for argument in args:\n summa += argument\n return str(summa)\n\n\nif __name__ == \"__main__\":\n function1_to_decorate(a=1, b=2, c=3)\n function2_to_decorate(1, 2, 3)\n\n\n\n","sub_path":"decorator_logger.py","file_name":"decorator_logger.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"199551744","text":"# -*- coding: utf-8 -*-\n\n\"\"\" My Input and Output IO clas/file\n\n\"\"\"\n#standard library imports\nimport os\nimport logging\n#related third party imports\nimport numpy as np\nfrom PIL import Image as PILImage\n#local application/library specific imports\n\n\nclass RnIo():\n \"\"\" My IO class\n \n \"\"\"\n \n def __init__(self, workspace = ''):\n \"\"\" IO initialisation\n \n \"\"\"\n self.workspace = workspace\n \n def write_nparray_csv(self, nparray, header = [], \n speichername = 'test.csv', \n delimiter = ';'):\n \"\"\" Write 2D nparray to ascii file\n \n nparray (nparray): 2D numpy data array\n header (arr): 1D python array of header\n speichername (str): name of file to write\n delimiter (str): delimiter which shall be used\n \n \"\"\"\n #Assertions\n try:\n assert(isinstance(nparray, np.ndarray))\n assert(isinstance(header, list))\n assert(isinstance(speichername, str))\n assert(isinstance(delimiter, str))\n except AssertionError:\n logging.error('ein paramter hat nicht das passende format')\n \n #Code\n filepath = self.workspace + speichername\n with open(filepath, 'wb') as f:\n #check if header exists\n if len(header) > 0:\n _row = ''\n for entry in header:\n _row += str(entry) + delimiter\n _row += '\\n'\n f.write(_row)\n \n for y in xrange(nparray.shape[0]):\n _row = ''\n for x in xrange(nparray.shape[1]):\n _row += str(nparray[y, x]) + delimiter\n _row += '\\n'\n f.write(_row)\n \n def read_csv_nparray(self, name = 'test.csv', \n header = False, \n delimiter = ';'):\n \"\"\" Reads csv file\n \n name (str): file name of file e.g. test.csv\n header (bool): if file has 1 line header set as True\n delimiter: delimiter of file\n \n Returns:\n header (arr)\n nparray\n \n \"\"\"\n _header = []\n _arr = []\n filepath = self.workspace + name\n with open(filepath, 'rb') as f:\n #read header\n if header == True:\n _row = f.readline()\n _header = _row.split(delimiter)\n _header.pop() #remove \\n element\n \n #read data\n for line in f.xreadlines():\n line = line.split(delimiter)\n line.pop() #remove new line char\n try:\n line = [float(x) for x in line] #convert string to float\n except ValueError:\n logging.error(\"\"\"coud no convert string to float, wrong data\n format ist given back!\"\"\")\n _arr.append(line)\n\n _arr = np.array(_arr) \n return _header, _arr\n \n def write_nparray_Image(self, nparray, name = 'test.bmp', normiert = True):\n \"\"\" Write an 2D/3D nparray as an Image\n \n nparray (numpy arra): 2D/3D Numpy array\n name (str): filename of image\n normiert (bool): if true the Image will be normalized\n \n \"\"\"\n filepath = self.workspace + name\n _image = PILImage.fromarray(nparray)\n \n if normiert == True:\n from PIL import ImageOps\n _image = ImageOps.autocontrast(_image, cutoff=0)\n \n _image.save(filepath)\n \n def read_Image_nparray(self, filepath):\n \"\"\" Reads Image with PIL and converts it to numpy array\n \n filepath (str): complete filepath to Image\n \n \"\"\"\n #with open(filepath, 'rb') as _fp:\n #_fp = open(filepath, 'rb')\n img = PILImage.open(filepath)\n #_img = _img.convert('L')\n #_fp.close()\n #_arr = np.array(img)\n arr = np.array(img)\n return arr\n \n \n def read_fits_nparray(self, name = 'test.fit', number = 0):\n \"\"\" Read .fits file from iStar camera\n \n name (str): file name\n number (int): number of hdulist (usually 0)\n \n Returns:\n _header (pyfits.header.Header): dictionary type something\n _arr (numpy.ndarray): numpy array\n \n \"\"\"\n import pyfits\n _file =self. workspace + name\n _fits = pyfits.open(_file)\n _header = _fits[number].header\n _arr = _fits[number].data\n return _header, _arr\n\n def read_prf_nparray(self, filepath):\n \"\"\" Read a prf spectrum file\n \n filepath (str): complete filepath to prf file\n \n Returns:\n _arr (numpy.ndarray): 2 D numpy array. first colum is index,\n second is intensity\n \n \"\"\"\n delimiter = '\\t'\n _arr = []\n with open(filepath, 'rb') as f:\n for line in f.xreadlines():\n line = line.split(delimiter)\n line[1] = line[1].rstrip('\\r\\n')\n #print line\n try:\n line = [float(x) for x in line] #convert string to float\n except ValueError:\n logging.error(\"\"\"coud no convert string to float, wrong data\n format ist given back!\"\"\")\n _arr.append(line)\n _arr = np.array(_arr) \n #print _arr\n return _arr\n \n def read_suaptxt_nparray(self, filepath):\n \"\"\" Reads txt file from suap\n \n filepath (str): complete filepath to prf file\n \n Returns:\n _arr (numpy.ndarray): 2 D numpy array. first colum is index,\n second is intensity\n \n \"\"\"\n delimiter = '\\t'\n _arr = []\n with open(filepath, 'rb') as f:\n for i, line in enumerate(f.xreadlines()):\n line = line.split(delimiter)\n if i >= 3:\n line[1] = line[1].rstrip('\\n')\n try:\n line = [float(x) for x in line] #convert string to float\n except ValueError:\n logging.error(\"\"\"coud no convert string to float, wrong data\n format ist given back!\"\"\")\n _arr.append(line)\n _arr = np.array(_arr) \n #print _arr\n return _arr\n \n def read_originPeaklist_nparray(self, filepath):\n \"\"\" Reads a peaklist (from origin exportet)\n \n filepath (str): complete filepath to prf file\n \n Returns:\n _arr (numpy.ndarray): 2 D numpy array. first colum is index,\n second is intensity\n \n \"\"\"\n delimiter = '\\t'\n _arr = []\n with open(filepath, 'rb') as f:\n for i, line in enumerate(f.xreadlines()):\n line = line.split(delimiter)\n line[1] = line[1].rstrip('\\r\\n')\n #komme zu punkt konversion\n line[0] = line[0].replace(',', '.')\n line[1] = line[1].replace(',', '.')\n try:\n line = [float(x) for x in line] #convert string to float\n except ValueError:\n logging.error(\"\"\"coud no convert string to float, wrong data\n format ist given back!\"\"\")\n _arr.append(line)\n \n _arr = np.array(_arr)\n #print _arr\n return _arr\n \n def write_nparray_txt(self, filepath, nparray, header = [], delimiter = '\\t'):\n \"\"\" Write an 2D numpy array to ascii file\n \n filepath (str): complete filepath for new file\n nparray (numpy.ndarray): 2D numpy array\n delimiter (str): optional delimiter parameter\n \n \"\"\"\n _nrows = nparray.shape[0]\n _ncolumns = nparray.shape[1]\n with open(filepath, 'wb') as f:\n #check if header exists\n if len(header) > 0:\n for row in header:\n _row = ''\n for item in row:\n _row += str(item) + delimiter\n _row += '\\n'\n f.write(_row)\n for y in xrange(_nrows):\n _row = ''\n for x in xrange(_ncolumns):\n _row += str(nparray[y, x]) + delimiter\n _row += '\\n'\n f.write(_row)\n \n \n\nif __name__ == \"__main__\":\n io = RnIo()\n path1 = 'D:/Raimund Buero/Python/SpyDev/Specfit/testdata/spectrum1.prf'\n path2 = 'D:/Raimund Buero/Python/SpyDev/Specfit/testdata/spectrum2.txt'\n path3 = 'D:/Raimund Buero/Python/SpyDev/Specfit/testdata/Peaklist.dat'\n #io.read_prf_nparray(path1)\n #io.read_suaptxt_nparray(path2)\n io.read_originPeaklist_nparray(path3)\n \n \n","sub_path":"rnio.py","file_name":"rnio.py","file_ext":"py","file_size_in_byte":9148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"622823164","text":"#\n# Copyright 2013 Urban Airship\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\nimport re\nip = '0.0.0.0'\nkey_dir = 'keys'\nkey_configs = {\n 'a-server.example.com': {\n 'cidr_ranges': ['128.0.0.1/24'],\n },\n 'b-server.example.com': {\n 'cidr_ranges': ['0.0.0.0/0'],\n 'service': 'b-server',\n 'path': [re.compile(r\".*key$\"), lambda x: True]\n }\n}\n","sub_path":"padlocker/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"160937827","text":"# -*- coding: utf-8 -*-\n'''\nЗадание 7.3a\n\nСделать копию скрипта задания 7.3.\n\nДополнить скрипт:\n- Отсортировать вывод по номеру VLAN\n\n\nОграничение: Все задания надо выполнять используя только пройденные темы.\n\n'''\nmac = []\nwith open('CAM_table.txt') as f:\n for line in f:\n if 'DYNAMIC' in line:\n mac.append(line.replace(' DYNAMIC ','').strip())\nfor line in sorted(mac):\n print(line)\n","sub_path":"exercises/07_files/task_7_3a.py","file_name":"task_7_3a.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"215571127","text":"#########################################################\n### Analysis/Ray-tracing module for Bulk SPDC sources ###\n### Written by: Arian Stolk\t\t\t\t\t\t\t ###\n### Date: September 2017\t\t\t\t\t\t\t ###\n#########################################################\n\n###The goal of this project is to provide a tool that will allow the investigation of SPDC sources made of bulk non-linear crystals.###\n###Import library####\n\nfrom math import *\nimport random\nimport scipy\nimport numpy as np\nimport sympy as sp\nimport matplotlib\nimport collections\nfrom itertools import compress\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport time, sys\nfrom vpython import *\nimport multiprocessing as mp\n\n\ndebug=False\n################################################################################################################\ndef Sellmeier(coeff=[0,0,0,0],lam = 785):\n\t\"Takes the wavelength [nm] array lam and coeff and outputs the refractive index. Used for BBO and YVO4\"\n\tl=lam*1e-3\n\treturn np.sqrt(coeff[0]+(coeff[1])/((l)**2-coeff[2])-coeff[3]*(l)**2)\n\ndef dSellmeier(coeff=[0,0,0,0],lam = 785):\n\t\"Takes the wavelength [nm] array lam and coeff and outputs the derivate of refractive index with respect to wavelength. Used for BBO and YVO4\"\n\tc = coeff\n\treturn ((1e-6)*lam*(-c[3]-(c[1])/(c[2]-(1e-6)*lam**2)**2))/np.sqrt((c[0]-(1e-6)*c[3]*lam**2-(c[1])/(c[2]-(1e-6)*lam**2)))\n\ndef v_group(lam=785,n=1,dn=0):\n\t\"Takes the arrays lam, n and dn and outputs the array of group velocities\"\n\n\treturn (2.998e11)/(n-lam*dn)\n\ndef n_ext_effective(coeff=[0,0,0,0,0,0,0,0],theta=0,lam=785):\n\t\"takes the full sellmeier coefficients, the angle array theta and the wavelength array wavelength and outputs the refractive index in an array \"\n\tc=coeff\n\t# try:\n\t# \tprint(lam.shape,theta.shape)\n\t# except AttributeError:\n\t# \tpass\n\treturn ((np.sin(theta)/(c[4]+(c[5])/((lam*1e-3)**2-c[6])-(c[7])*(lam*1e-3)**2)**0.5)**2+(np.cos(theta)/(c[0]+(c[1])/((lam*1e-3)**2-c[2])-(c[3])*(lam*1e-3)**2)**0.5)**2)**(-0.5)\n\ndef dn_ext_effective(coeff=[0,0,0,0,0,0,0,0],theta=0,lam=785):\n\t\"takes the full sellmeier coefficients, the angle array theta and the wavelength array wavelength and outputs the derivate of the refractive index in an array \"\n\tc=coeff\n\treturn -((1e-6)*lam*(((c[1]+c[3]*(c[2]-(1e-6)*lam**2)**2)*np.cos(theta)**2)/(c[1]+(c[2]-(1e-6)*lam**2)*(-c[0]+(1e-6)*c[3]*lam**2))**2\\\n\t\t\t+(((c[5]+c[7]*(c[6]-(1e-6)*lam**2)**2)*np.sin(theta)**2)/(c[5]+(c[6]-(1e-6)*lam**2)*(-c[4]+(1e-6)*c[3]*lam**2))**2))\\\n\t\t\t/((np.cos(theta)**2)/(c[0]-(1e-6)*c[3]*lam**2-c[1]/(c[2]-(1e-6)*lam**2))+(np.sin(theta)**2)/(c[4]-(1e-6)*c[7]*lam**2-c[5]/(c[6]-(1e-6)*lam**2)))**(3/2))\n\ndef flatten(l):\n\t\"flattens list l to having only 1 dimension\"\n\tfor el in l:\n\t\tif isinstance(el, collections.Iterable) and not isinstance(el, (str, bytes)):\n\t\t\tyield from flatten(el)\n\t\telse:\n\t\t\tyield el\n\ndef walkoff(theta=0,coeff=[0,0,0,0,0,0,0,0],lam=785,thickness=1):\n\t\"Takes the angle array theta, full Sellmeiercoeff, wavelength array lam [nm] and thickness array [mm] and returns the 1d value of the walkoff (SO NOT AN ARRAY) \"\n\n\t# if len(coeff)<8:\n\t# \tprint(\"Please provide all 8 Sellmeier coeffs\")\n\t# else:\n\tn_ord=Sellmeier(coeff[0:4],lam)\n\tn_ext=Sellmeier(coeff[4:8],lam)\n\treturn 0.5*thickness*(n_ext_effective(coeff=coeff,theta=theta,lam=lam)**2)*((n_ord**-2)-(n_ext**-2))*np.sin(2*theta)\n\ndef update_progress(progress):\n barLength = 10 # Modify this to change the length of the progress bar\n status = \"\"\n if isinstance(progress, int):\n progress = float(progress)\n if not isinstance(progress, float):\n progress = 0\n status = \"error: progress var must be float\\r\\n\"\n if progress < 0:\n progress = 0\n status = \"Halt...\\r\\n\"\n if progress >= 1:\n progress = 1\n status = \"Done...\\r\\n\"\n block = int(round(barLength*progress))\n text = \"\\rPercent: [{0}] {1}% {2}\".format( \"#\"*block + \"-\"*(barLength-block), progress*100, status)\n sys.stdout.write(text)\n sys.stdout.flush()\n\ndef phasefunction_vec(lpump=405,l_list=[785],theta_list=[0],coeff=[2.7359, 0.01878, 0.01822, 0.01354, 2.3753, 0.01224, 0.01667, 0.01516],cutangle=28.7991*np.pi/180,crystal_length=6,W=0.1):\n\t\"Input float of pump wavelength, and arrays size (1,n) of l_list, theta_list, cutangle and crystal_length output provides the array size (1,n) of the phasematching fucntion. \"\n\tc=coeff\n\t#create lists of pump,signal and idler wavelengths\n\n\tli=l_list*lpump/(l_list-lpump);\n\t#calculate the angular frequency from wavelength\n\twp,ws,wi = (2*np.pi)/lpump,(2*np.pi)/l_list,(2*np.pi)/li\n\n\t#calculate the ord/extraord refractive indices for the wavelenghts\n\tnop = Sellmeier(coeff=c[0:4],lam=lpump)\n\tnep = Sellmeier(coeff=c[4:8],lam=lpump)\n\tnos = Sellmeier(coeff=c[0:4],lam=l_list)\n\t# nes = Sellmeier(coeff=c[4:8],lam=l_list)\n\tnoi = Sellmeier(coeff=c[0:4],lam=li)\n\t# nei = Sellmeier(coeff=c[4:8],lam=li)\n\n\t#calculate the effective pump refractive index\n\tnpeff=np.sqrt(1/((np.cos(cutangle)/nop)**2+(np.sin(cutangle)/nep)**2))\n\tL=crystal_length\n\n\tthet_i=np.arcsin(nos*ws*np.sin(theta_list)/(noi*wi))\t\t\t\t\t\t\t\t\t\t\t#corresponding opening angle of the idler photon based on refractive indices\n\tdkz=npeff*wp-nos*ws*np.cos(theta_list)-noi*wi*np.cos(thet_i)\t\t\t\t\t\t\t\t\t#corresponding phase mismatch in z (propagationdirection) per unit length\n\tdky=-nos*ws*np.sin(theta_list)+noi*wi*np.sin(thet_i)\t\t\t\t\t\t\t\t\t\t\t#mismatch in pphase in y (direction perp to z) per unit length\n\tphi=np.exp(-((W*1e6)**2)*(np.square(dky))/2)*np.square(np.sin(0.5*dkz*L*1e6)/(0.5*dkz*L*1e6))\t#sinc2() func of the mismatch over the length of the crystal\n\n\treturn phi #(1,n) array\n\ndef dens_hist(data):\n \n\n #data definition\n xdat, ydat = data[0],data[1]\n \n x_range=np.array([-2,2])+np.mean(xdat)\n y_range=np.array([-2,2])+np.mean(ydat)\n xyrange = [x_range,y_range] # data range\n bins = [100,100] # number of bins\n thresh = 1 #density threshold\n\n # histogram the data\n hh, locx, locy = scipy.histogram2d(xdat, ydat, range=xyrange, bins=bins)\n posx = np.digitize(xdat, locx)\n posy = np.digitize(ydat, locy)\n\n #select points within the histogram\n ind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])\n hhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are\n xdat1 = xdat[ind][hhsub < thresh] # low density points\n ydat1 = ydat[ind][hhsub < thresh]\n hh[hh < thresh] = np.nan # fill the areas with low density by NaNs\n\n plt.imshow(np.flipud(hh.T),cmap='jet',extent=np.array(xyrange).flatten(), interpolation='none', origin='upper')\n plt.title(\"xmean i {0:.2f},xstd is {0:.2f} and ymean is{0:.2f}, ystd is {0:.2f}\".format(np.mean(xdat),np.std(xdat),np.mean(ydat),np.std(ydat)))\n plt.colorbar() \n plt.show()\n \n\n##################################################################################################################\nclass Visualization(object):\n\t\"Class used to visualize the outcome of a simulation\"\n\n\tdef __init__(self,**k):\n\t\tself.simulation\t\t\t\t= k.pop('simulation')\n\t\tself.complete_results\t\t=self.simulation.complete_results\n\t\tself.surface_pos\t\t\t=[x.fsurf for x in self.simulation.setup.elements]\n\t\tself.graph_open\t\t\t\t= False\n\n\n\tdef showtime(self,time_diff=False,bins=50,pos=0):\n\t\t\"this function shows the temporal results of the raytracing\"\n\t\t\n\t\tif pos < self.surface_pos[-1]:\n\t\t\tself.interpol_data(pos=pos)\n\t\t\tplot_data = self.dummy_surface\n\t\telse:\n\t\t\tprint(\"Please choose a position smaller than the end position of the simulation\")\n\n\n\t\tif not time_diff:\n\t\t\ttimelist=(1e15)*np.array([x[3][:] for x in plot_data])\n\n\t\telse:\n\t\t\tarrivtimes = (1e15)*np.array([x[3][:] for x in plot_data])\n\n\t\t\ttimelist = np.array([arrivtimes[0]-arrivtimes[1],arrivtimes[2]-arrivtimes[3]])\n\t\t\n\t\tplt.figure()\n\t\t\n\t\tfor i,times in enumerate(timelist):\n\t\t\tplt.hist(times,bins,alpha=0.5,label = \"Photons from crystal {}\".format([\"one\",\"two\"][i % 2]))\n\t\t\tprint(np.mean(times),len(times))\n\t\tplt.legend()\n\t\tplt.xlabel(\"D_arrival time between signal and idles [fs]\")\n\t\tplt.ylabel(\"Occurance\")\n\t\tplt.show()\n\n\tdef showpos(self,pos=0):\n\n\t\t\"this function shows the position results of the raytracing\"\n\t\tif pos < self.surface_pos[-1]:\n\t\t\tself.interpol_data(pos=pos)\n\t\t\tplot_data = self.dummy_surface\n\t\telse:\n\t\t\tprint(\"Please choose a position smaller than the end position of the simulation\")\n\n\t\tdata_list = [x[0][:].T[0:2] for x in plot_data]\n\t\n\t\tf,axarr = plt.subplots(2,2,figsize=(10,10))\n\t\t\n\t\tfor i,ax in enumerate(flatten(axarr)):\n\t\t\tax.clear()\n\t\t\tdata=data_list[i]\n\t\t\txdat, ydat = data[0],data[1]\n\t\t\tx_range=np.array([-2,2])+np.mean(xdat)\n\t\t\ty_range=np.array([-2,2])+np.mean(ydat)\n\t\t\txyrange = [x_range,y_range] # data range\n\t\t\tbins = [100,100] # number of bins\n\t\t\tthresh = 10\t #density threshold\n\n\t\t\t# histogram the data\n\t\t\thh, locx, locy = scipy.histogram2d(xdat, ydat, range=xyrange, bins=bins)\n\t\t\tposx = np.digitize(xdat, locx)\n\t\t\tposy = np.digitize(ydat, locy)\n\n\t\t\t#select points within the histogram\n\t\t\tind = (posx > 0) & (posx <= bins[0]) & (posy > 0) & (posy <= bins[1])\n\t\t\thhsub = hh[posx[ind] - 1, posy[ind] - 1] # values of the histogram where the points are\n\t\t\txdat1 = xdat[ind][hhsub < thresh] # low density points\n\t\t\tydat1 = ydat[ind][hhsub < thresh]\n\t\t\thh[hh < thresh] = np.nan # fill the areas with low density by NaNs\n\n\t\t\tax.imshow(np.flipud(hh.T),cmap='jet',extent=np.array(xyrange).flatten(), interpolation='none', origin='upper')\n\t\t\tax.set_title(\"xm ={:.2f}, xs ={:.2f}, ym = {:.2f}, ys ={:.2f}\".format(np.mean(xdat),np.std(xdat),np.mean(ydat),np.std(ydat)))\n\t\t\t# ax.set_fontsize(20)\n\t\t\t# ax.colorbar()\n\t\tplt.show()\n\n\tdef interpol_data(self,pos=0):\n\n\t\t[index_rsurf,rsurf_z]=[[i,x] for i,x in enumerate(self.surface_pos) if x >= pos ][0]\n\t\t[index_lsurf,lsurf_z]=[index_rsurf-1,self.surface_pos[index_rsurf-1]]\n\n\t\tscale=(pos-lsurf_z)/(rsurf_z-lsurf_z)\n\n\t\tself.dummy_surface=[[[] for i in range(5)] for j in range(4)]\n\n\t\tfor i,ray_list in enumerate(self.complete_results):\n\t\t\tfor j in [0,3]: #interpolate position and times\n\t\t\t\tarrays=np.split(ray_list[j],[index_lsurf,index_rsurf+1],axis=-1)[1]\n\t\t\t\tinterpolation_matrix = np.sum(np.array([(1-scale),scale])*arrays,axis=-1)\n\t\t\t\tself.dummy_surface[i][j]=interpolation_matrix\n\t\t\tfor j in [1,2,4]:#angles,wavelength and polarization dont need to be interpolated\n\t\t\t\tself.dummy_surface[i][j]=ray_list[j][...,index_lsurf]\n\n\tdef get_focus_pos(self,pos=0):\n\t\t\n\t\tif pos < self.surface_pos[-1]:\n\t\t\tself.interpol_data(pos=pos)\n\t\telse:\n\t\t\tprint(\"Please choose a position smaller than the end position of the simulation\")\n\n\t\txmeans=np.array(list(map(np.mean,[x[0][:].T[0] for x in self.dummy_surface])))\n\t\tymeans=np.array(list(map(np.mean,[x[0][:].T[1] for x in self.dummy_surface])))\n\t\treturn np.mean(np.array([xmeans,ymeans]),axis=1) \n\n\tdef filter_results(self,fibre_pos=np.array([0,0,0]),core_diam=0,Num_Ap=0):\n\t\t\"Filter function giving singles and coincidences that enter the fibre at fibre_pos with specified dimensions\"\n\n\t\tfilter_cond_match_list=[]\n\t\tself.singles=[]\n\t\tself.coincidences=[]\n\t\t\n\t\tpos = fibre_pos[-1]\n\n\t\tif pos < self.surface_pos[-1]:\n\t\t\tself.interpol_data(pos=pos)\n\t\telse:\n\t\t\tprint(\"Please choose a position smaller than the end position of the simulation\")\n\n\t\tfor ray_set in self.dummy_surface:\n\t\t\tpos = ray_set[0]\n\t\t\tangle = ray_set[1]\n\n\t\t\tfilter_cond_match=self.filter_func(pos,angle,fibre_pos=fibre_pos,core_diam=core_diam, Num_Ap = Num_Ap)\n\t\t\tfilter_cond_match_list.append(filter_cond_match)\n\n\t\t\tsingles_list = [ray[np.where(filter_cond_match)] for ray in ray_set[0:-1]]\n\t\t\tself.singles.append(singles_list)\n\n\t\tself.coincidences =[[x[np.where(np.logical_and(filter_cond_match_list[2*round(i/2.1)],filter_cond_match_list[2*round(i/2.1)+1]))] for x in ray_set[0:-1]] for i,ray_set in enumerate(self.dummy_surface)] #2*round(i/2.1) does (0,1,2,3) -> (0,0,2,2) to compare signal and idler\n\n\n\tdef filter_func(self,pos,angle,fibre_pos=np.array([0,0,0]),core_diam=0,Num_Ap=0):\n\n\t\t\"method called by filter_results to check if the rays fall in fibre pos with angle < NA\"\n\t\tcore_dist_check=np.linalg.norm(pos-fibre_pos,axis=1)optic_elements[1].bsurf: #check if ray is not already past this surface due to starting position\n\t\t\t#get optic elements from list\n\t\t\telement1=optic_elements[0]\n\t\t\telement2=optic_elements[1]\n\n\t\t\t#make (N,2) array of the refractive indices for before and after the surface\n\t\t\tnin=np.repeat(element1.getn(angle,lam),2,axis=1)\n\t\t\tnout=np.repeat(element2.getn(angle,lam),2,axis=1)\n\n\t\t\t#calculate the fraction needed for snells law and thin lens equation\n\t\t\tindexfract = (nin/nout)\t\t\n\n\t\t\t# print(nin,nout,ray.angles,ray.position,element1.position,element2.position)\n\t\t\t\n\t\t\tif hasattr(element1,\"ROC\"): #check if first element is lens, if yes use back surface ROC of element1\n\t\t\t\tposition_on_lens=(pos-element1.position)[:,0:2]\n\n\t\t\t\tif not element1.asphere:\t\t\t\t\t\n\t\t\t\t\tangles = np.arcsin(np.sin(angle)*(indexfract))+position_on_lens*((nin-nout)/(nout*element1.ROC[1]))\n\t\t\t\telse:\n\t\t\t\t\tdist_from_centre=np.linalg.norm(position_on_lens,axis=1)\n\t\t\t\t\tROClist=get_ROC_asphere(pos,element1.asph_coeff[1],element1.ROC[1])\n\t\t\t\t\tangles = np.arcsin(np.sin(angle)*(indexfract))+position_on_lens*((nin-nout)/(nout*element1.ROC[1]))\n\t\t\telif hasattr(element2,\"ROC\"): #check if second element is lens, if yes use front surface ROC of element2\n\t\t\t\tposition_on_lens=(pos-element2.position)[:,0:2]\n\t\t\t\tif not element2.asphere:\t\t\t\t\t\n\t\t\t\t\t# print(np.mean(position_on_lens))\n\t\t\t\t\tangles = np.arcsin(np.sin(angle)*(indexfract))+position_on_lens*((nin-nout)/(nout*element2.ROC[0]))\n\t\t\t\telse:\n\t\t\t\t\tdist_from_centre=np.linalg.norm(position_on_lens,axis=1)\n\t\t\t\t\tROClist=get_ROC_asphere(pos,element2.asph_coeff[0],element2.ROC[0])\n\t\t\t\t\tangles = np.arcsin(np.sin(angle)*(indexfract))+position_on_lens*((nin-nout)/(nout*element2.ROC[0]))\n\t\t\telse:#if not lenses, use snells law\n\t\t\t\tangles = np.arcsin(np.sin(angle)*(indexfract))\n\n\t\t\treturn angles\n\t\t\t\n\t\telse:\n\t\t\treturn angle\n\n\t\t\n\n\tdef translate(pos,angle,lam,optic_element):\n\t\t\"method for translating the rays throught the objects\"\n\n\t\tif not pos[0][2]>optic_element.bsurf: #check if ray is not already past this surface due to starting position\n\t\t\treturn optic_element.translate(pos,angle,lam)\n\t\telse:\n\t\t\treturn pos\n\t\t\n\t\tif debug:\n\t\t\tprint(\"I traced {} in {} to {}\".format(ray,optic_element,ray.position))\n\n\n\t\n\t\t\n\n\tdef get_SPDC_rayset_adv(self,Ntot=1,nr_crystals=1,pumpray=[],pump_waist=[0,0],pump_focus=[0,0],cutangle=28.8*np.pi/180,l_min=0,l_max=0,theta_min=0,theta_max=0,factor=0.1):\n\t\t\"This is a complex method that generates the starting positions/angles/wavelenght/polarization according to the Type-1 phasematching conditions in BBO\"\n\t\t#checks if the amount of trials is smaller than the maximum per loop: 10M.\n\t\tif Ntot<10000000:\n\t\t\tN=Ntot\n\t\telse:\n\t\t\tN=10000000\n\n\t\tNumber_of_cycles = int(ceil(Ntot/N)) #Number of cyles of 20M\n\n\t\tfinal_list = []\t\t\t\t\t\t#Initialization of list to store the cycles\n\n\t\tlpump=pumpray.wavelength \t\t\t#setting the wavelength and waists of the pump\n\t\tw0x=pump_waist[0]\n\t\tw0y=pump_waist[1]\n\n\t\tzR_pump_x=(np.pi*w0x**2)/(lpump*1e-6)#calculating the rayleigh ranges in x and y\n\t\tzR_pump_y=(np.pi*w0y**2)/(lpump*1e-6)\n\t\t\n\n\t\tfor i in range(Number_of_cycles):\n\n\t\t\t#generating large arrays of random numbers\n\n\t\t\trand_u = np.random.uniform(0,1,N)\t\t\t\t\t\t\t\t#used for calculating position along walkoff path of ray\n\t\t\t\n\t\t\trandN_l\t\t= np.random.uniform(l_min,2*lpump,N)\t\t\t\t#generating random wavelengths for singal_min to 2*pump wavelength (phasefunction is symmetric in 2*pump wavelength)\n\t\t\trandN_theta\t= np.random.uniform(theta_min,theta_max,N)\t\t\t#random opening angles \n\t\t\trand_check\t= np.random.uniform(0,1,N)\t\t\t\t\t\t\t#the random numbers used in the check for the rejection sampling (could these be reused?)\n\n\t\t\treturn_list=[]\n\n\t\t\tpumpray.position=np.array([0,0,0])\t\t\t\t\t\t\t\t#For all the cycles the pump starts at 0,0,0\n\n\t\t\tfor i in range(nr_crystals):#looping through all the crystals where downconversion happens\n\t\t\t\t\n\t\t\t\tcrystal=self.setup.crystals[i]\t#\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\tpumpray.position = np.array([pumpray.position[0],pumpray.position[1],crystal.fsurf]) #downconversion begins at frontsurface\n\t\t\t\tw_beg=pumpray.position\n\n\t\t\t\tw_end=w_beg+crystal.getwalkoff_ray(pumpray)+np.array([0,0,crystal.thickness])\t\t#downconversion ends at begin + walkoff + thickness\n\t\t\t\tstart_pos = np.repeat(np.array([w_beg]),N,axis=0) + np.outer(rand_u,(w_end-w_beg))\t#the random starting pos is just a linear combination of these\n\n\t\t\t\t\n\t\t\t\tstart_z = (start_pos.T)[2]\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t#the starting positions along the z axis\n\n\t\t\t\t#\"Introduce effects of gaussian pump\"\n\t\t\t\t\n\t\t\t\twzx,wzy = w0x*np.sqrt(1+((start_z-pump_focus[0])/zR_pump_x)**2),w0y*np.sqrt(1+((start_z-pump_focus[1])/zR_pump_y)**2)\t#for all z, there is a beam waist\n\n\t\t\t\tsx,sy=wzx,wzy\t\t\t#this beam waist is the standard deviation of the gaussian used for further sampling\n\t\t\t\n\t\t\t\tgauss_x=np.random.randn(1,N)*sx\t#random x position due to gaussian pump\n\t\t\t\tgauss_y=np.random.randn(1,N)*sy#random y position due to gaussian pump\n\n\t\t\t\tgauss_xy=np.concatenate((gauss_x,gauss_y)).T\n\n\t\t\t\tzpos_rel_wx=np.where(np.abs(-pump_focus[0]+start_z)>1e-9,(-pump_focus[0]+start_z),1e-9)\t#we want the z pos with respect to focus. np.where is used to remove all values smaller than 1e-9 to avoid dividing by 0\n\t\t\t\tzpos_rel_wy=np.where(np.abs(-pump_focus[1]+start_z)>1e-9,(-pump_focus[1]+start_z),1e-9)\n\n\t\t\t\tstart_angles_gauss=np.concatenate((-np.arcsin(gauss_x/(zpos_rel_wx+((zR_pump_x)**2)/(zpos_rel_wx))),-np.arcsin(gauss_y/(zpos_rel_wy+((zR_pump_y)**2)/(zpos_rel_wy))))).T #for ever (x,y) we can calculate propagation angles (a,b) using ROC(z) of the gaussian beam\n\n\t\t\t\t#\"Sampling the phasefunction for the N randomly generated angles. the weight will later be used to accept or reject samples. Factor is number <1, the smaller this number the smoother the distribution, but the more trys needed\"\n\t\t\t\t#TODO: need to choose which angle (a,b) it should add to the phasematching (currently always chooses a but it depends on crystal orientation)\n\t\t\t\tweight=factor*phasefunction_vec(l_list=randN_l,theta_list=randN_theta,lpump=lpump,cutangle=cutangle+(start_angles_gauss.T)[0],W = w0x)\n\n\t\t\t\t#reshape the gaussian coordinates x,y to the format [x,y,0] \n\t\t\t\tgauss_pad=np.zeros(start_pos.shape)\n\t\t\t\tgauss_pad[:gauss_xy.shape[0],:gauss_xy.shape[1]]=gauss_xy\n\n\t\t\t\t#put all the parameters so far (position, angles, wavelenght, opening_angle) in an array with length 100.000.0000 so we can select all the succesfull ones at once\n\t\t\t\tsampled_params=np.concatenate((start_pos+gauss_pad,start_angles_gauss,np.array([randN_l]).T,np.array([randN_theta]).T),axis=1)#list of important simulation parameters\n\n\t\t\t\tfinal_params=sampled_params[np.where(weight>rand_check)]#filters the sampled_params for the succesfully drawn samples\n\t\t\t\tNsuc=len(final_params)\n\n\t\t\t\t#extract the (position, angles, wavelenght, opening_angle) from the succesfull ones \n\t\t\t\t[ray_start,ray_angle,ls,opening_angle]=[final_params[:,0:3].T,final_params[:,3:5].T,final_params[:,5:6].T,final_params[:,6:7].T]\n\n\t\t\t\t#get idler wavelengths\n\t\t\t\tli = ls*(lpump)/(ls-lpump)\n\t\t\t\t\n\t\t\t\t#calculate actual propagation angles of signal and idler (rotationally symmetric in space due to ordidnary polarization)\n\t\t\t\trand_nr = np.random.uniform(0,1,len(final_params))\n\t\t\t\tazim=np.cos(rand_nr*np.pi)*opening_angle\n\t\t\t\thoriz=np.sign(azim)*np.sign(opening_angle)*np.sqrt(opening_angle**2 - azim**2)\n\n\t\t\t\tprop_angles=np.concatenate((azim,horiz)).T\n\t\t\t\t\n\n\t\t\t\t#Assembling the final list of parameters, combining all the angular and spatial effects\t\t\t\t\n\n\t\t\t\t#TODO:Need to automatically decide on polarization, depends on wavematching/crystal orientation\n\t\t\t\tSrays=[ray_start.T,(ray_angle.T+prop_angles),ls.T,np.full((Nsuc,1),'V',dtype=str)]\n\t\t\t\tIrays=[ray_start.T,(ray_angle.T-prop_angles),li.T,np.full((Nsuc,1),'V',dtype=str)]\n\t\t\t\t\n\t\t\t\treturn_list.append(Srays)\n\t\t\t\treturn_list.append(Irays)\n\n\t\t\t\tpumpray.position += crystal.getwalkoff_ray(pumpray) #update pumpray position for next crystal\n\n\t\t\tfinal_list.append(return_list) #update all the cycles\n\t\t\n\t\t\n\t\t#Create empty list the size of the list we want to return\n\t\tthe_result_list= [[[] for i in range(4)] for j in range(4)]\n\t\t\n\t\t#loop through all the elements of the empty list, filling them with the result of the individual cycles \n\t\t#THIS IS SLOW AND UGLY BUT I CANT FIND A BETTER WAY, maybe np.concatenate(final_list,axis=?)\t\n\t\tfor i in range(4):\n\t\t\tfor j in range(4):\n\t\t\t\tthe_result_list[i][j]=np.concatenate(tuple([final_list[k][i][j] for k in range(Number_of_cycles)]))\n\t\t\n\t\treturn the_result_list\n\n\n\tdef filter_results(self,fibre_pos=np.array([0,0,0]),core_diam=0,Num_Ap=0):\n\t\t\"Filter function giving singles and coincidences that enter the fibre at fibre_pos with specified dimensions\"\n\n\t\tfilter_cond_match_list=[]\n\t\tself.singles=[]\n\t\tself.coincidences=[]\n\n\t\tfor ray_set in self.complete_results:\n\t\t\tpos = ray_set[0]\n\t\t\tangle = ray_set[1]\n\n\t\t\tfilter_cond_match=self.filter_func(pos,angle,fibre_pos=fibre_pos,core_diam=core_diam, Num_Ap = Num_Ap)\n\t\t\tfilter_cond_match_list.append(filter_cond_match)\n\n\t\t\tsingles_list = [ray[np.where(filter_cond_match)] for ray in ray_set]\n\t\t\tself.singles.append(singles_list)\n\n\t\tself.coincidences =[[x[np.where(np.logical_and(filter_cond_match_list[2*round(i/2.1)],filter_cond_match_list[2*round(i/2.1)+1]))] for x in ray_set] for i,ray_set in enumerate(self.complete_results)] #2*round(i/2.1) does (0,1,2,3) -> (0,0,2,2) to compare signal and idler\n\n\n\tdef filter_func(self,pos,angle,fibre_pos=np.array([0,0,0]),core_diam=0,Num_Ap=0):\n\n\t\t\"method called by filter_results to check if the rays fall in fibre pos with angle < NA\"\n\t\tcore_dist_check=np.linalg.norm(pos-fibre_pos,axis=1) 0:\n\t\t\t\tself.elements.insert(0,Optic(name=\"Air_Begin\",material=\"Air\",position=np.array([0,0,(self.elements[0].fsurf)/2]),thickness=self.elements[0].fsurf))\n\t\t\tself.elements.insert(-1,Optic(name=\"Air_End\",material=\"Air\",position=np.array([0,0,self.elements[0].bsurf+2.5]),thickness=5))\n\n\t\telse:\n\t\t\tfor i in range(self.nr_elements-1,0,-1):\n\t\t\t\tgap = self.elements[i].fsurf - self.elements[i-1].bsurf\n\t\t\t\tif gap > 0:\n\t\t\t\t\tself.elements.insert(i,Optic(name=\"Air_after_{}\".format(self.elements[i].name),material=\"Air\",position=np.array([0,0,self.elements[i-1].bsurf+0.5*(self.elements[i].fsurf-self.elements[i-1].bsurf)]),thickness=gap))\n\t\t\tif self.elements[0].fsurf > 0:\n\t\t\t\tself.elements.insert(0,Optic(name=\"Air_Begin\",material=\"Air\",position=np.array([0,0,(self.elements[0].fsurf)/2]),thickness=self.elements[0].fsurf))\n\t\t\tself.elements.append(Optic(name=\"Air_End\",material=\"Air\",position=np.array([0,0,self.elements[-1].bsurf+2.5]),thickness=5))\n\t\n\tdef visualize(self,centre = [0,0,0]):\n\t\tboxes=[]\n\t\tfor elem in self.elements:\n\t\t\tboxes.append(box(pos=vec(0-centre[0],0-centre[1],elem.position[2]-centre[2]),size=vec(2,2,elem.thickness),opacity=0.3,color=elem.get_colour()))\n\t\treturn boxes \n\n\tdef group(self):\n\t\tlis=self.elements\n\t\tself.grouped_elements = [[lis[i],lis[i+1]] for i in range(0,len(lis)-1,1)]\n\n\nclass Ray(object):\n\t\"All instances of this class are monochromatic rays of light, resembling individual photons\"\n\n\tdef __init__(self,**k):\n\t\ttry:\n\t\t\tself.name \t\t\t= k.pop('name',\"ray\")\n\t\t\tself.position \t\t= np.array(k.pop('position'))\n\t\t\tself.angles \t\t= k.pop('angles')\n\t\t\tself.polarization\t= k.pop('polarization')\n\t\t\tself.wavelength\t\t= k.pop('wavelength')\n\t\t\tself.path\t\t\t= [self.position]\n\t\t\tself.time\t\t\t= [0]\n\t\texcept KeyError:\n\t\t\tprint('Please provide at least a name, position, angles, polarization and wavelength')\n\n\tdef showRay(self):\n\t\tprint(\"Position: {}, Angles: {}, Polarization: {}, Wavevlength: {}\".format(self.position,self.angles,self.polarization,self.wavelength))\n\n\tdef arrivaltime(self):\n\t\treturn np.sum(self.time)\n\n\nclass Optic(object):\n\t\"All instances of this class used to build a virtual setup that resembles the SPDC source that do NOT EMIT photons, but only act on them. E.G. non-linear crystals/lenses/HWP etc.\"\n\t\n\tdef __repr__(self):\n\t\treturn \"Optic_{}\".format(self.name)\n\n\tdef __init__(self,**k):\n\t\ttry:\n\t\t\tself.name \t\t= k.pop('name');\n\t\t\tself.material\t= k.pop('material',None)\n\t\t\tself.position\t= np.array(k.pop('position'))\n\t\t\tself.thickness \t= k.pop('thickness',0)\n\t\t\tself.fsurf \t\t= self.position[2] - 0.5*self.thickness\n\t\t\tself.bsurf \t\t= self.position[2] + 0.5*self.thickness\n\t\texcept KeyError:\n\t\t\tprint(\"Please provide at least a name, material, position to initiate an Optics object\")\n\n\tdef calcsurfaces(self):\n\t\tself.fsurf = self.position[2] - 0.5*self.thickness\n\t\tself.bsurf = self.position[2] + 0.5*self.thickness\n\n\tdef set_pol_flag(self,pol):\n\t\tself.ray_pol = pol\n\t\n\n\tdef showObject(self):\n\t\tprint (\"Name: {}, Material: {}, Position: {}\".format(self.name,self.material,self.position))\n\n\tdef get_colour(self):\n\t\t\"Gets colour for visualization as RGB Triplet\"\n\t\tcolour_dict={'BBO':vec(0,0,1),'YVO4':vec(0,1,0),'Air':vec(1,1,1)}\n\t\t\n\t\tcolour=colour_dict.pop(self.material,vec(1,1,1))\n\n\t\treturn colour\n\n\tdef getn(self,angle,lam):\n\n\t\tlamsq=(lam*1e-3)**-2\n\n\t\treturn 1+(0.05792105)/(238-lamsq)+(0.00167917)/(57.362-lamsq)\n\n\tdef getdn(self,angle,lam):\n\n\t\treturn -(3358.34)/(((57.362-(1e6)/lam**2)**2)*lam**3)-(115842)/(((238-(1e6)/lam**2)**2)*lam**3)\n\n\tdef translate(self,pos,angle,lam):\n\t\treturn (pos + np.array([np.tan(angle[:,0])*(self.thickness-(pos[:,2]-self.fsurf)),np.tan(angle[:,1])*(self.thickness-(pos[:,2]-self.fsurf)),(self.thickness-(pos[:,2]-self.fsurf))]).T)\n\n\nclass Crystal(Optic):\n\t\"All instances of this class (child of Optics), are objects that are (non-linear) crystals that act on the photons. They are all rectangular boxes that are made of a birefringent material\"\n\t\n\tmaterials \t=\t[\"BBO\",\"YVO4\"]\n\n\tselm_coeff \t= {\t\"BBO\" \t\t: \t[2.7359, 0.01878, 0.01822, 0.01354, 2.3753, 0.01224, 0.01667, 0.01516],\n \t\t\t\t\t\"YVO4\" \t\t: \t[3.77834, 0.069736, 0.04724, 0.0108133, 4.59905, 0.110534, 0.04813, 0.0122676]\n \t\t\t\t}\n\n\tdef __init__(self,**k):\n\t\ttry:\n\t\t\tself.orientation \t= k.pop('orientation')\n\t\t\tself.cutangle\t\t= k.pop('cutangle')\n\t\texcept KeyError:\n\t\t\tprint(\"Please provide at least orientation and cutangle to initiate a Crystal object\")\n\t\t\n\t\tsuper(Crystal,self).__init__(**k)\n\n\tdef getn(self,angle,lam):\n\n\t\t\t\t\n\t\tc=self.selm_coeff[self.material]\n\t\tlamb=lam\n\n\t\tif self.ray_pol == \"H\":\n\t\t\tif self.orientation in {\"left\",\"right\"}:\n\t\t\t\treturn (c[0]+(c[1])/((lamb*1e-3)**2-c[2])-c[3]*(lamb*1e-3)**2)**0.5\n\n\t\t\telse:\n\t\t\t\tif self.orientation is \"up\":\n\t\t\t\t\treturn n_ext_effective(coeff = c,theta = angle[:,[0]] - self.cutangle,lam=lamb)\n\n\t\t\t\telif self.orientation is \"down\":\n\t\t\t\t\treturn n_ext_effective(coeff = c,theta = angle[:,[0]] + self.cutangle,lam=lamb)\n\n\t\telif self.ray_pol == \"V\":\n\t\t\tif self.orientation in {\"up\",\"down\"}:\n\t\t\t\treturn (c[0]+(c[1])/((lamb*1e-3)**2-c[2])-c[3]*(lamb*1e-3)**2)**0.5\n\t\t\telse:\n\t\t\t\tif self.orientation is \"left\":\n\t\t\t\t\treturn n_ext_effective(coeff = c,theta = angle[:,[1]] - self.cutangle,lam=lamb)\n\n\t\t\t\telif self.orientation is \"right\":\n\t\t\t\t\treturn n_ext_effective(coeff = c,theta = angle[:,[1]] + self.cutangle,lam=lamb)\n\t\n\t\n\tdef getdn(self,angle,lam):\n\t\t\t\t\t\n\t\tc=self.selm_coeff[self.material]\n\t\tlamb=lam\n\n\t\tif self.ray_pol == \"H\":\n\t\t\tif self.orientation in {\"left\",\"right\"}:\n\t\t\t\treturn dSellmeier(coeff=c[0:4],lam=lamb)\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tif self.orientation is \"up\":\n\t\t\t\t\treturn dn_ext_effective(coeff=c,theta=self.cutangle-angle[:,[0]],lam=lamb)\n\n\t\t\t\telif self.orientation is \"down\":\t\t\t\t\t\n\t\t\t\t\treturn dn_ext_effective(coeff=c,theta=self.cutangle+angle[:,[0]],lam=lamb)\n\t\t\t\t\t\n\t\n\t\telif self.ray_pol == \"V\":\n\t\t\tif self.orientation in {\"up\",\"down\"}:\t\t\t\t\n\t\t\t\treturn dSellmeier(coeff=c[0:4],lam=lamb)\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tif self.orientation is \"left\":\t\t\t\t\t\n\t\t\t\t\treturn dn_ext_effective(coeff=c,theta=self.cutangle-angle[:,[1]],lam=lamb)\n\t\t\t\t\t\n\n\t\t\t\telif self.orientation is \"right\":\t\t\t\t\t\n\t\t\t\t\treturn dn_ext_effective(coeff=c,theta=self.cutangle+angle[:,[1]],lam=lamb)\n\t\t\t\t\t\n\n\tdef getwalkoff(self,pos,angle,lam):\n\n\t\t\n\t\tc=self.selm_coeff[self.material]\n\t\tN=len(lam)\n\t\tw_ret=np.zeros((N,3))\n\t\t\t\n\t\tif self.ray_pol == \"H\":\n\t\t\tif self.orientation == \"up\":\n\t\t\t\tw = walkoff(theta=self.cutangle-angle[:,[0]],coeff=c,lam=lam,thickness=self.thickness-(pos[:,[2]]-self.fsurf))\n\t\t\t\tw_ret[:,[0]] = w\n\t\t\t\treturn w_ret\n\t\t\telif self.orientation == \"down\":\n\t\t\t\tw = -1*walkoff(theta=self.cutangle+angle[:,[0]],coeff=c,lam=lam,thickness=self.thickness-(pos[:,[2]]-self.fsurf))\n\t\t\t\tw_ret[:,[0]] = w\n\t\t\t\treturn w_ret\n\t\t\telse:\n\t\t\t\treturn w_ret\n\t\telif self.ray_pol == \"V\":\n\t\t\tif self.orientation == \"left\":\n\t\t\t\tw = walkoff(theta=self.cutangle-angle[:,[1]],coeff=c,lam=lam,thickness=self.thickness-(pos[:,[2]]-self.fsurf))\n\t\t\t\tw_ret[:,[1]] = w\n\t\t\t\treturn w_ret\n\t\t\telif self.orientation == \"right\":\n\t\t\t\tw = -1*walkoff(theta=self.cutangle+angle[:,[1]],coeff=c,lam=lam,thickness=self.thickness-(pos[:,[2]]-self.fsurf))\n\t\t\t\tw_ret[:,[1]] = w\n\t\t\t\treturn w_ret\n\t\t\telse:\n\t\t\t\treturn w_ret\n\n\tdef getwalkoff_ray(self,ray):\n\n\t\tif not isinstance(ray,Ray):\n\t\t\traise Exception(\"Please provide me with a ray to calculate the walkoff for!\")\n\n\t\tc=self.selm_coeff[self.material]\n\n\t\t\t\n\t\tif ray.polarization == \"H\":\n\t\t\tif self.orientation == \"up\":\n\t\t\t\tw = walkoff(theta=self.cutangle-ray.angles[0],coeff=c,lam=ray.wavelength,thickness=self.thickness-(ray.position[2]-self.fsurf))\n\t\t\t\treturn np.array([w,0,0])\n\t\t\telif self.orientation == \"down\":\n\t\t\t\tw = -1*walkoff(theta=self.cutangle+ray.angles[0],coeff=c,lam=ray.wavelength,thickness=self.thickness-(ray.position[2]-self.fsurf))\n\t\t\t\treturn np.array([w,0,0])\n\t\t\telse:\n\t\t\t\tw = 0\n\t\t\t\treturn np.array([0,w,0])\n\t\telif ray.polarization == \"V\":\n\t\t\tif self.orientation == \"left\":\n\t\t\t\tw = walkoff(theta=self.cutangle-ray.angles[1],coeff=c,lam=ray.wavelength,thickness=self.thickness-(ray.position[2]-self.fsurf))\n\t\t\t\treturn np.array([0,w,0])\n\t\t\telif self.orientation == \"right\":\n\t\t\t\tw = -1*walkoff(theta=self.cutangle+ray.angles[1],coeff=c,lam=ray.wavelength,thickness=self.thickness-(ray.position[2]-self.fsurf))\n\t\t\t\treturn np.array([0,w,0])\n\t\t\telse:\n\t\t\t\tw = 0\n\t\t\t\treturn np.array([0,w,0])\n\n\n\tdef translate(self,pos,angle,lam):\n\t\t#returns the updated position array\n\t\twalkoff=self.getwalkoff(pos,angle,lam)\n\t\n\t\treturn (walkoff + pos + np.array([np.tan(angle[:,0])*(self.thickness-(pos[:,2]-self.fsurf)),np.tan(angle[:,1])*(self.thickness-(pos[:,2]-self.fsurf)),(self.thickness-(pos[:,2]-self.fsurf))]).T)\n\t\n\nclass Lens(Optic):\n\t\"All instances of this class (Child of optics) are objects that are curved surfaces (spherical) that act on the photons. One can have two different Radii of Curvature, so this field should be a vector\"\n\t\n\tmaterials \t= [\"N-SF6HT\",\"N-LAK22\"]\n\n\tselm_coeff \t= {\t\"N-SF6HT\" \t:\t[1.77931763, 0.0133714182, 0.338149866, 0.0617533621, 2.08734474, 174.01759],\n\t\t\t\t\t\"N-LAK22\" \t:\t[1.14229781, 0.00585778594, 0.535138441, 0.0198546147, 1.04088385, 100.834017]\n \t\t\t\t\t}\n\n\tdef __init__(self,**k):\n\t\tsuper(Lens,self).__init__(**k)\n\t\ttry:\n\t\t\tself.ROC \t\t= k.pop('ROC')\n\t\t\tself.centre \t= k.pop('centre')\n\t\t\tself.position \t= self.position+self.centre\n\t\t\tself.asphere\t= k.pop('apshere',False)\n\t\t\tif self.asphere:\n\t\t\t\tself.asph_coeff = k.pop('asph_coeff')\n\n\t\texcept KeyError:\n\t\t\tprint(\"Please provide at least ROC (Radii of Curvature) and centre position to initiate a Lens object, aspheric lenses need asph_coeff\")\n\t\n\tdef achromat(position=[],centre=[],f=0):\n\t\t\"Create achromat at position\"\n\t\tlens1=Lens(name='Lens1',material =\"N-SF6HT\",orientation=None,position=np.array(position)+np.array([0,0,-2.3/2]),thickness=2.3,ROC=[13.5,-10.6],centre=[centre[0],centre[1],0])\n\t\tlens2=Lens(name='Lens2',material =\"N-LAK22\",orientation=None,position=np.array(position)+np.array([0,0,+1.3/2]),thickness=1.3,ROC=[-10.6,-47.8],centre=[centre[0],centre[1],0])\n\t\tairf = Optic(name='Air_f',material=\"Air\",orientation = None, position = np.array(position)+np.array([0,0,1.3+(f-1.3)/2]),thickness=f-1.3)\n\n\t\treturn [lens1,lens2,airf]\n\n\tdef getn(self,angle,lam):\n\t\t\n\t\tc=self.selm_coeff[self.material]\n\t\tlamb=(1e-3)*lam\n\n\t\treturn (1+(c[0]*(lamb**2))/(lamb**2-c[1])+(c[2]*(lamb**2))/(lamb**2-c[3])+(c[4]*(lamb**2))/(lamb**2-c[5]))**(0.5)\n\n\tdef getdn(self,angle,lam):\n\n\t\tc=self.selm_coeff[self.material]\n\t\tlamb=lam*1e-3\n\t\n\t\treturn 1e-6*lamb*((-(c[0]*c[1])/(c[1]-lamb**2)**2-(c[2]*c[3])/(c[3]-lamb**2)**2-(c[4]*c[5])/(c[5]-lamb**2)**2)/np.sqrt(1+(c[0]*lamb**2)/(-c[1]+lamb**2)+(c[2]*lamb**2)/(-c[3]+lamb**2)+(c[4]*lamb**2)/(-c[5]+lamb**2)))\n\n\tdef translate(self,pos,angle,lam):\n\t\t\n\t\treturn (pos + np.array([np.tan(angle[:,0])*(self.thickness-(pos[:,2]-self.fsurf)),np.tan(angle[:,1])*(self.thickness-(pos[:,2]-self.fsurf)),(self.thickness-(pos[:,2]-self.fsurf))]).T)\n\t\n\tdef get_ROC_asphere(self,pos,coeffs,ROC):\n\t\t\"Calculate the ROC of an asphere, depending on the position of the ray to the lens\"\n\t\tc=coeffs\n\t\tY=np.norm(pos[0:2]-self.position[0:2],axis=1)\n\n\t\tsag=(Y**2)/(ROC*(1+np.sqrt(1-((1+c[0])*Y**2)/ROC**2)))+(c[1])*Y**2+(c[2])*Y**4+(c[3])*Y**6+(c[4])*Y**8+(c[5])*Y**10\n\n\t\treturn 0.5*sag+Y**2/(2*sag)\n\n\nclass HWP(Optic):\n\t\"All instances of this class (Child of optics) are objects that perform the mathematical operation of H->V and V->H for a certain wavelenth range\"\n\n\tdef __init__(self,**k):\n\t\ttry:\n\t\t\tself.cutoff=k.pop('cutoff')\n\t\texcept KeyError:\n\t\t\tprint(\"Please provide the cutoff wavelenght in nm for the HWP\")\n\n\t\tsuper(HWP,self).__init__(**k)\n\n\tdef show_cutoff(self):\n\t\tprint(\"I act on a waveplate for photons with wavelength above {}nm\".format(self.cutofff))\n\n\tdef propagate(self,pos,angle,lam):\n\n\t\tif np.all(lam > self.cutoff):\n\t\t\tif self.ray_pol == \"H\":\n\t\t\t\tself.ray_pol = \"V\"\n\t\t\telse:\n\t\t\t\tself.ray_pol = \"H\"\n\n\tdef translate(self,pos,angle,lam):\n\t\tself.propagate(pos,angle,lam)\n\n\t\treturn pos\n\t\t\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":45480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"576463939","text":"poll = {}\n\nwhile True:\n\n name = input('Could you gives us your name before we begin: ')\n\n if name == 'quit':\n break\n\n response = input('If you could go anywhere in this world,\\\n where would you go?\\n')\n \n \n print('\\n\\tIf you want to stop giving input type exit or quit')\n\n poll[name] = response\n\nfor key, value in poll.items():\n print('{} would love to visit: {}.'.format(key, value))\n \n","sub_path":"ex7-/7-10.py","file_name":"7-10.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615324624","text":"from django.views.generic import ListView, DetailView\nfrom django.urls import reverse\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect\nfrom . import models\n\n\nclass HomeView(ListView):\n \"\"\" HomeView Definition\"\"\"\n\n model = models.Room\n paginate_by = 10\n paginate_orphans = 5\n ordering = \"created\"\n\n\nclass RoomDetail(DetailView):\n model = models.Room\n template_name = \"\"\n\n\ndef room_detail(request, pk):\n try:\n room = models.Room.objects.get(pk=pk)\n return render(request, \"rooms/detail.html\", context={\"room\": room})\n except models.Room.DoesNotExist:\n raise Http404()\n # return redirect(reverse(\"core:home\"))\n","sub_path":"rooms/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"27188960","text":"def is_sub(s):\n if len(s) >= 1:\n for word in s:\n if (s.count(word) > 1):\n return False\n return True\n else:\n return False\n\n\ndef get_substring(s):\n l = len(s)\n min = 3\n max = 0\n lst = list()\n for i in range(0, l):\n for j in range(i + 1, l + 1):\n sub = s[i:j]\n # print(sub)\n if is_sub(sub):\n if len(sub) > max:\n # print(sub)\n max = len(sub)\n lst.append(sub)\n if lst == []:\n return -1\n else:\n return lst[-1]\n\ns = input()\nss=get_substring(s)\nprint(len(ss))\n","sub_path":"SUMMER FS/14 may/t1.py","file_name":"t1.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"78064069","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.interpolate as interpolate\nimport pickle\nimport my.util\n\n\n# 시험체별 기울기비 - 1 : 3.375 : 8 : 15.625 : 125 = h20:h30:h40:h50:h100\n\nwith open('data_new.pickle', 'rb') as f:\n data_new = pickle.load(f)\nwith open('elastic_limits.pickle', 'rb') as f:\n elastic_limits = pickle.load(f)\n\ncolor = {20: 'r', 30: 'g', 40: 'b', 50: 'c', 100: 'm'}\n\n\n# 하중-처짐 곡선을 총 3 부분으로 구성하고, 각 부분은 10개의 점으로 구성\n# 1. 선형 상향\n# 2. 비선형 상향\n# 3. 비선형 하향\n\nfor thk in data_new:\n fig, ax = plt.subplots(1, 1)\n for no in data_new[thk]:\n x = data_new[thk][no]['defl']\n y = data_new[thk][no]['load']\n indices = [0, elastic_limits[thk][no], np.argmax(y), y.size - 1]\n ax.plot(x, y, '.-')\n ax.plot(x[indices], y[indices], 'ro')\n ax.grid()\n plt.show()\n\n\nxymean = dict()\nndiv = 10\nnpt = 3 * ndiv + 1\nfor thk in data_new:\n xymean[thk] = dict()\n xymean[thk]['defl'] = np.zeros(npt)\n xymean[thk]['load'] = np.zeros(npt)\n\n plt.figure()\n for no in data_new[thk]:\n xymean[thk][no] = {'defl': np.zeros(npt), 'load': np.zeros(npt)}\n\n xy = np.loadtxt('T{:03d}({:1d}).csv'.format(thk, no), delimiter=',', skiprows=1, usecols=(1, 2))\n iis = my.util.get_increasing_index(xy[:, 0])\n xy = xy[iis, :]\n spl = interpolate.splrep(xy[:, 0], xy[:, 1], k=1)\n\n ids = [0, elastic_limits[thk][no], np.argmax(xy[:, 1]), xy.shape[0] - 1]\n xy4o = np.zeros((npt, 2))\n for i in range(0, 3):\n ii = np.s_[ids[i]:(ids[i + 1] + 1)]\n x = np.linspace(xy[ids[i], 0], xy[ids[i + 1], 0], ndiv + 1)\n y = interpolate.splev(x, spl, ext=1)\n xy4o[(i * ndiv):((i + 1) * ndiv + 1), 0] = x\n xy4o[(i * ndiv):((i + 1) * ndiv + 1), 1] = y\n\n xymean[thk][no]['defl'] = xy4o[:, 0]\n xymean[thk][no]['load'] = xy4o[:, 1]\n xymean[thk]['defl'] += xy4o[:, 0]\n xymean[thk]['load'] += xy4o[:, 1]\n# plt.plot(xy4o[:, 0], xy4o[:, 1], '.-')\n\n xymean[thk]['defl'] = xymean[thk]['defl'] / len(list(data_new[thk]))\n xymean[thk]['load'] = xymean[thk]['load'] / len(list(data_new[thk]))\n plt.plot(xymean[thk]['defl'], xymean[thk]['load'], 'ko-', markerfacecolor='None')\n plt.grid()\n plt.show()\n\n\nfig, ax = plt.subplots()\nfor thk in xymean:\n ax.plot(xymean[thk]['defl'], xymean[thk]['load'], 'ko-', markerfacecolor='None')\nax.grid()\nplt.show()\n\nwith open('data-mean.pickle', 'wb') as f:\n pickle.dump(xymean, f)\n\n\n# 초기 구성한 포인트\n# indices[20][1] = [0, 258, 1497, 2532]\n# indices[20][2] = [0, 128, 675, 1186]\n# indices[20][3] = [0, 119, 682, 1006]\n# indices[30][1] = [0, 138, 471, 1126]\n# indices[30][2] = [0, 143, 560, 1074]\n# indices[30][3] = [0, 144, 623, 1233]\n# indices[40][1] = [0, 155, 553, 1072]\n# indices[40][2] = [0, 174, 502, 1126]\n# indices[40][3] = [0, 173, 537, 1138]\n# indices[40][4] = [0, 175, 530, 1055]\n# indices[50][1] = [0, 197, 415, 1038]\n# indices[50][2] = [0, 195, 456, 1027]\n# indices[50][3] = [0, 188, 401, 1135]\n# indices[100][1] = [0, 256, 520, 947]\n# indices[100][2] = [0, 159, 301, 827]\n# indices[100][3] = [0, 156, 327, 909]\n","sub_path":"02 adjust-data/step4_get_mean_curve.py","file_name":"step4_get_mean_curve.py","file_ext":"py","file_size_in_byte":3242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"515357244","text":"import numpy as np\n\nmean = [np.array([0] * 28 * 28)] * 10 # init\nvariance = [np.matrix([[0] * 28 * 28] * 28 * 28)] * 10\n\nw = [np.array([0] * 28 * 28)] * 10\nw0 = [0] * 10\n\n\ndef train():\n with open(\"train-labels.idx1-ubyte\", mode='rb') as trainLabelFile:\n with open(\"train-images.idx3-ubyte\", mode=\"rb\") as trainImageFile:\n trainLabelFile.seek(4) # jump over the magic number\n trainImageFile.seek(4)\n s1 = trainLabelFile.read(4) # the amount of items\n s2 = trainImageFile.read(4)\n number = int.from_bytes(s1, byteorder=\"big\")\n assert (number == int.from_bytes(s2, byteorder=\"big\")) # if the amount of images are equal\n\n number = int(number)\n rows = int.from_bytes(trainImageFile.read(4), byteorder=\"big\") # get rows and columns\n columns = int.from_bytes(trainImageFile.read(4), byteorder=\"big\")\n\n Images = [[0] * rows * columns] * number # data struct to save the data\n Labels = [0] * number\n\n for i in range(0, number): # get the data\n if i % 100 == 0:\n print(i)\n label = int.from_bytes(trainLabelFile.read(1), byteorder=\"big\")\n image = []\n for j in range(0, rows * columns):\n image.append(int.from_bytes(trainImageFile.read(1), byteorder=\"big\"))\n Labels[i] = label\n Images[i] = image\n\n total = [np.array([0] * rows * columns)] * 10 # init the total array\n totalVariance = [np.matrix([[0] * rows * rows] * columns * columns)] * 10\n\n for i in range(0, number): # get the mean\n if i % 100 == 0:\n print(i)\n label = Labels[i]\n total[label] = total[label] + Images[i]\n for i in range(0, 10):\n mean[i] = total[i] / number\n\n for i in range(0, number): # get the variance\n if i % 100 == 0:\n print(i)\n label = Labels[i]\n imageArray = np.array(Images[i])\n diff = imageArray - mean[label]\n diff.shape = (28 * 28, 1)\n totalVariance[label] = totalVariance[label] + diff.dot(np.transpose(diff))\n\n for i in range(0, 10):\n variance[i] = totalVariance[i] / number + variance[i]\n\n averageVariance = np.matrix([[0] * rows * rows] * columns * columns)\n for i in range(0, 10):\n averageVariance = averageVariance + variance[i]\n averageVariance = averageVariance / 10\n\n for i in range(0, 10):\n mean[i].shape = (28 * 28, 1)\n temp = np.linalg.pinv(averageVariance)\n w[i].shape = (28 * 28, 1)\n w[i] = temp.dot(mean[i])\n w0[i] = -0.5 * np.transpose(mean[i]).dot(temp).dot(mean[i])\n w[i] = np.transpose(w[i])\n\n\ndef test():\n with open(\"t10k-images.idx3-ubyte\", mode=\"rb\") as testImageFile:\n with open(\"t10k-labels.idx1-ubyte\", mode=\"rb\") as testLabelFile:\n testLabelFile.seek(4) # jump over the magic number\n testImageFile.seek(4)\n s1 = testLabelFile.read(4) # the amount of items\n s2 = testImageFile.read(4)\n number = int.from_bytes(s1, byteorder=\"big\")\n assert (number == int.from_bytes(s2, byteorder=\"big\")) # if the amount of images are equal\n\n number = int(number)\n rows = int.from_bytes(testImageFile.read(4), byteorder=\"big\") # get rows and columns\n columns = int.from_bytes(testImageFile.read(4), byteorder=\"big\")\n\n Images = [np.array([0] * rows * columns)] * number # data struct to save the data\n Labels = [0] * number\n\n for i in range(0, number): # get the data\n if i % 100 == 0:\n print(i)\n label = int.from_bytes(testLabelFile.read(1), byteorder=\"big\")\n image = []\n for j in range(0, rows * columns):\n image.append(int.from_bytes(testImageFile.read(1), byteorder=\"big\"))\n Labels[i] = label\n Images[i] = np.array(image)\n Images[i].shape = (28 * 28, 1)\n\n count = 0\n max_pos = 0\n count_every=[0]*10\n correct_every=[0]*10\n for i in range(0, number):\n max = float(\"-inf\")\n for j in range(0, 10):\n temp = w[j].dot(Images[i]) + w0[j]\n if temp > max:\n max = temp\n max_pos = j\n count_every[Labels[i]]+=1\n if max_pos == Labels[i]:\n count = count + 1\n correct_every[Labels[i]]+=1\n print(count / number)\n for i in range(0,10):\n print(\"the correctness of \"+str(i)+\" is \"+ str(correct_every[i]/count_every[i]))\n\n\nif __name__ == \"__main__\":\n train()\n test()\n","sub_path":"homework1/LDF.py","file_name":"LDF.py","file_ext":"py","file_size_in_byte":5094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"160797684","text":"with open('INGREDIENT.csv', 'r') as f:\n\twith open('INGREDIENT2.csv', 'w+') as f2:\n\t\twhile True:\n\t\t\tdata = f.readline()\n\t\t\tif not data:\n\t\t\t\tbreak\n\t\t\t\t\n\t\t\tdata = data.split(',')\n\t\t\tfor i in range(len(data)):\n\t\t\t\tdata[i] = data[i].replace(\",\", \"\")\n\n\t\t\tfor i in range(1, len(data)):\n\t\t\t\tf2.write(str(data[0]) + \",\" + data[i] + \"\\n\")\n\n","sub_path":"data/ingredient.py","file_name":"ingredient.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"599089430","text":"# -*- coding: utf-8 -*- \n# Author: bellawu\n# Date: 2021/3/8 15:18\n# File: vari_example.py\n# IDE: PyCharm\n\nimport tensorflow as tf\n\nstate = tf.Variable(0, name='counter')\none = tf.constant(1)\n\nnew_value = tf.add(state, one)\nupdate = tf.assign(state, new_value)\n\n# 如果定义Variable就一定要initialize\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as sess:\n sess.run(init)\n for _ in range(3):\n sess.run(update)\n print(sess.run(state))","sub_path":"base_tensorflow/vari_example.py","file_name":"vari_example.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"622647333","text":"\"\"\"\nFake It Til You Make It\n\"\"\"\nimport sys\nimport pygame\nfrom pygame.sprite import Group\nimport json\nfrom button import Button\nfrom settings import Settings\nimport display\nimport functions as gf\nfrom stats import Stats\nfrom player import Player\n\ndef run():\n\t#Initialize game, settings and create a screen object\n\tpygame.init()\t\n\tsettings = Settings()\n\tscreen_setup = display.build(settings)\n\tscreen = screen_setup[0]\n\tpygame.display.set_caption(\"Fake It Til You Make It\")\n\t\n\t#Change screen dims to match fullscreen dims\n\tsettings.screen_width = screen_setup[1]\n\tsettings.screen_height = screen_setup[2]\n\t\n\t#create variables for screen center\n\tscx = settings.screen_width/2\n\tscy = settings.screen_height/2\n\t\n\t#Make Main Menu\n\tbuttons = []\n\tplay_button = Button(settings, screen, \"NEW GAME\",\n\t\tscx-100, 500, 300,75,(0,0,0),None)\n\tquit_button = Button(settings, screen, \"QUIT\",\n\t\tscx-100, 600, 300,75,(0,0,0),None)\n\tbuttons.append(play_button)\n\tbuttons.append(quit_button)\n\t\n\t#Make Ingame Menu\n\tig_buttons = []\n\tinv_button = Button(settings, screen, \"INVENTORY\",\n\t\tsettings.screen_width*.2-100, 100,\n\t\t300,75,(0,0,0),None)\n\tcraft_button = Button(settings, screen, \"CRAFT\",\n\t\tsettings.screen_width*.35-100, 100,\n\t\t300,75,(0,0,0),None)\n\tbuild_button = Button(settings, screen, \"BUILD\",\n\t\tsettings.screen_width/2-100, 100,\n\t\t300,75,(0,0,0),None)\n\tcharacter_button = Button(settings, screen, \"CHARACTER\",\n\t\tsettings.screen_width*.65-100, 100,\n\t\t300,75,(0,0,0),None)\n\tmenu_button = Button(settings, screen, \"MENU\",\n\t\tsettings.screen_width*.8-100, 100,\n\t\t300,75,(0,0,0),None)\n\tig_buttons.append(inv_button)\n\tig_buttons.append(craft_button)\n\tig_buttons.append(build_button)\n\tig_buttons.append(character_button)\n\tig_buttons.append(menu_button)\n\t\n\t#Make Loot PIP menu\n\tlp_buttons = []\n\tlp_title = Button(settings, screen, \"\",\n\t\tscx-250, scy-200, 500,50,(0,0,0),None,20)\n\tlptake_button = Button(settings, screen, \"TAKE\",\n\t\tscx+25, scy-125, 200,50,(0,0,0),None,20)\n\tlpdesc_button = Button(settings, screen, \"\",\n\t\tscx+25, scy-50, 200,175,(0,0,0),None,10)\n\tlp_window = Button(settings, screen, \"\",\n\t\tscx-250, scy-150, 500,300,(100,100,100),None)\n\tlp_loot_window = Button(settings, screen, \"\",\n\t\tscx-225, scy-125, 200,250,(180,180,180),None)\n\tlp_loot = Button(settings, screen, \"\",\n\t\tscx-215, scy-115, 180,230,(250,250,250),None)\n\tlp_buttons.append(lp_title)\n\tlp_buttons.append(lp_window)\n\tlp_buttons.append(lptake_button)\n\tlp_buttons.append(lpdesc_button)\n\tlp_buttons.append(lp_loot_window)\n\tlp_buttons.append(lp_loot)\n\t\n\t#Create a stats instance\n\tstats = Stats(settings)\n\t\n\t#Create item groups\n\tplayer = Player(settings, screen)\n\tfurniture = Group()\n\titems = Group()\n\tloots = Group()\n\tcustomers = Group()\n\t\n\t#Create clock to stabilize framerate\n\tclock = pygame.time.Clock()\n\t\n\t#Initialize Global Variables\n\tday = 1\n\thour = 6\n\tminute = 0\n\t\n\twhile True:\n\t\tclock.tick(100)\n\t\tgf.check_events(\tsettings, screen, stats, buttons, \n\t\t\t\t\t\t\tig_buttons, lp_buttons, loots)\n\t\tgf.update_screen(\tsettings,screen, stats, buttons, ig_buttons, \n\t\t\t\t\t\t\tlp_buttons, player, loots)\nrun()\n\t\n","sub_path":"FITYMI.py","file_name":"FITYMI.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"136310634","text":"###############################################################\n# Modified from https://www.cnblogs.com/rxbook/p/7509530.html #\n# 脚本功能：根据视频或照片中的exif信息，按照日期重命名文件名 #\n###############################################################\nimport os\nimport time\nimport exifread\n\n\nMY_DATE_FORMAT = '%Y%m%d_%H%M%S'\n\n# SUFFIX_FILTER = ['.jpg', '.png', '.mpg', '.mp4', '.thm', '.bmp', '.jpeg', '.avi', '.mov']\nDELETE_FILTER = ['thumbs.db', 'sample.dat']\n\nVIDEO_LIST = ['.mp4', '.avi', '.mov']\nIMAGE_LIST = ['.jpg', '.png', '.mpg', '.thm', '.bmp', '.jpeg']\n# SUFFIX_FILTER = VIDEO_LIST + IMAGE_LIST\nSUFFIX_FILTER = ['.mp4']\n\n\ndef isFormatedFileName(filename):\n #判断是否已经是格式化过的文件名\n try :\n filename_nopath = os.path.basename(filename)\n f, e = os.path.splitext(filename_nopath)\n time.strptime(f, MY_DATE_FORMAT)\n return True\n except ValueError :\n return False\n\ndef isTargetedFileType(filename):\n #根据文件扩展名，判断是否是需要处理的文件类型\n filename_nopath = os.path.basename(filename)\n f, e = os.path.splitext(filename_nopath)\n if e.lower() in SUFFIX_FILTER :\n return True\n else :\n return False\n\ndef isDeleteFile(filename):\n #判断是否是指定要删除的文件\n filename_nopath = os.path.basename(filename)\n if filename_nopath.lower() in DELETE_FILTER :\n return True\n else :\n return False\n\ndef generateNewFileName(filename):\n #根据照片的拍照时间生成新的文件名（如果获取不到拍照时间，则使用文件的创建时间）\n try :\n if os.path.isfile(filename):\n fd = open(filename, 'rb')\n else :\n raise \"[%s] is not a file!\\n\" % filename\n except :\n raise \"unopen file [%s]\\n\" % filename\n\n data = exifread.process_file(fd)\n if data :\n #取得照片的拍摄日期\n try :\n t = data['EXIF DateTimeOriginal']\n #转换成 yyyymmdd_hhmmss的格式\n dateStr = str(t).replace(\":\", \"\")[: 10] + \"_\" + str(t)[11:].replace(\":\", \"\")\n except :\n pass\n\n #如果没有取得exif信息，则用图像文件的创建日期作为拍摄日期\n state = os.stat(filename)\n dateStr = time.strftime(MY_DATE_FORMAT, time.localtime(state[-2]))\n dirname = os.path.dirname(filename)\n filename_nopath = os.path.basename(filename)\n f, e = os.path.splitext(filename_nopath)\n if e.lower() in VIDEO_LIST:\n dateStr = 'VID_' + dateStr\n elif e.lower() in IMAGE_LIST:\n dateStr = 'IMG_' + dateStr\n newFileName = os.path.join(dirname, dateStr + e)\n return newFileName\n\ndef scandir(startdir):\n #遍历指定目录以及子目录，对满足条件的文件进行改名或删除处理\n os.chdir(startdir)\n for obj in os.listdir(os.curdir):\n if os.path.isfile(obj):\n if isTargetedFileType(obj) and isFormatedFileName(obj) == False :\n #对满足过滤条件的文件进行改名处理\n newFileName = generateNewFileName(obj)\n print(\"rename[%s] => [%s]\" % (obj, newFileName))\n os.rename(obj, newFileName)\n elif isDeleteFile(obj):\n #删除制定的文件\n print(\"delete[%s]: \" % obj)\n os.remove(obj)\n else :\n pass\n if os.path.isdir(obj):\n scandir(obj)\n os.chdir(os.pardir)\n\n\nif __name__ == \"__main__\" :\n path = r\"/Users/jackeroo/Desktop/Photos\"\n scandir(path)\n","sub_path":"Video_Photo_Rename_by_Date.py","file_name":"Video_Photo_Rename_by_Date.py","file_ext":"py","file_size_in_byte":3676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"5106992","text":"# -*- coding: utf-8 -*-\r\nimport functools\r\nimport math\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\n\r\nfrom common.registry import registry\r\nfrom models.base_model import BaseModel\r\nfrom modules.layers import VGG_FeatureExtractor, ResNet_FeatureExtractor, AvgPooling_FeatureExtractor, SelfAttnLSTM_FeatureExtractor\r\nfrom modules.transformer import Transformer\r\nfrom transformers.modeling_bert import BertConfig\r\n\r\n@registry.register_model(\"ocr_transfm\")\r\nclass OCRTransfm(BaseModel):\r\n def __init__(self, config):\r\n super(OCRTransfm, self).__init__(config)\r\n self.Transfm_config = BertConfig(**self.config.transfm) #model config for ocr_transfm\r\n \r\n def build(self):\r\n self.backbone_transfm_modules = []\r\n self._build_feature_extractor()\r\n self._build_transformer()\r\n self._build_prediction()\r\n \r\n def _build_feature_extractor(self):\r\n backbone_map = {\"ResNet\": ResNet_FeatureExtractor, \"VGG\": VGG_FeatureExtractor}\r\n assert self.Transfm_config.backbone in backbone_map.keys(), f\"{self.Transfm_config.backbone} is not a supported visual feature extraction backbone\"\r\n self.vf_backbone = backbone_map[self.Transfm_config.backbone](3, self.Transfm_config.vf_dim) #input_channel: 3, output_channel: vf_dim\r\n self.backbone_transfm_modules.append({\"module\": self.vf_backbone, \"lr_scale\": self.Transfm_config.lr_ratio_backbone})\r\n backbone_holistic_map = {\"self_attn_LSTM\": SelfAttnLSTM_FeatureExtractor, \"avg_pooling\": AvgPooling_FeatureExtractor}\r\n self.holistic_backbone = backbone_holistic_map[self.Transfm_config.holistic_backbone](self.Transfm_config.vf_dim, self.Transfm_config.holistic_ndims)\r\n self.backbone_transfm_modules.append({\"module\": self.holistic_backbone, \"lr_scale\": self.Transfm_config.lr_ratio_holistic})\r\n \r\n def _build_transformer(self):\r\n \r\n self.transformer = Transformer(self.Transfm_config)\r\n self.backbone_transfm_modules.append({\"module\": self.transformer, \"lr_scale\": self.Transfm_config.lr_ratio_transformer})\r\n \r\n def _build_prediction(self):\r\n self.inter_linear1 = nn.Linear(self.Transfm_config.transfm_dims, self.Transfm_config.transfm_ntokens)\r\n \r\n def forward(self, sample_list):\r\n fwd_results = {}\r\n self._forward_feature_extractor(sample_list, fwd_results)\r\n self._forward_transformer_prediction(sample_list, fwd_results)\r\n results = {\"scores\": fwd_results[\"scores\"]} \r\n return results\r\n \r\n def _forward_feature_extractor(self, sample_list, fwd_results):\r\n img = sample_list.img_processed\r\n visual_features = self.vf_backbone(img)\r\n holistic_features = self.holistic_backbone(visual_features)\r\n fwd_results[\"visual_features\"] = visual_features\r\n fwd_results[\"holistic_features\"] = holistic_features\r\n \r\n def _forward_transformer_prediction(self, sample_list, fwd_results):\r\n if self.training:\r\n fwd_results[\"prev_inds\"] = sample_list.targets\r\n decoded_features = self.transformer(fwd_results[\"visual_features\"], fwd_results[\"holistic_features\"], fwd_results[\"prev_inds\"])\r\n fwd_results[\"decoded_features\"] = decoded_features[0]\r\n fwd_results[\"decoded_attns\"] = decoded_features[1] if self.Transfm_config.transfm_output_attns else None \r\n self._forward_prediction(sample_list, fwd_results)\r\n else:\r\n targets = torch.LongTensor(sample_list.targets)\r\n dec_step_num = targets.size(1)\r\n fwd_results[\"prev_inds\"] = torch.zeros_like(targets)\r\n fwd_results[\"prev_inds\"][:, 0] = targets[:, 0]\r\n for _ in range(dec_step_num):\r\n decoded_features = self.transformer(fwd_results[\"visual_features\"], fwd_results[\"holistic_features\"], fwd_results[\"prev_inds\"])\r\n fwd_results[\"decoded_features\"] = decoded_features[0]\r\n fwd_results[\"decoded_attns\"] = decoded_features[1] if self.Transfm_config.transfm_output_attns else None \r\n self._forward_prediction(sample_list, fwd_results)\r\n argmax_inds = fwd_results[\"scores\"].argmax(dim=-1)\r\n fwd_results[\"prev_inds\"][:, 1:] = argmax_inds[:, :-1] #transformer does not predict start_token, so fwd_results[\"scores\"][:, 0] is the score of the first non-start_token character\r\n \r\n def _forward_prediction(self, sample_list, fwd_results):\r\n scores = self.inter_linear1(fwd_results[\"decoded_features\"])\r\n fwd_results[\"scores\"] = scores #B x L x C \r\n \r\n def get_optimizer_parameters(self, config):\r\n optimizer_param_groups = []\r\n base_lr = config.optimizer.params.lr \r\n finetune_params_set = set()\r\n for m in self.backbone_transfm_modules:\r\n optimizer_param_groups.append({\"params\": list(m[\"module\"].parameters()), \"lr\": base_lr * m[\"lr_scale\"], })\r\n finetune_params_set.update(list(m[\"module\"].parameters()))\r\n remaining_params = [p for p in self.parameters() if p not in finetune_params_set]\r\n optimizer_param_groups.insert(0, {\"params\": remaining_params})\r\n return optimizer_param_groups\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n","sub_path":"models/ocr_transfm.py","file_name":"ocr_transfm.py","file_ext":"py","file_size_in_byte":5364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"212681008","text":"import logging.handlers\nimport os\n\n\nclass NoAdminNTEventLogHandler(logging.handlers.NTEventLogHandler):\n \"\"\"\n Modified version of the NTEventLogHandler that does not register the\n logger in the registry unless told to do so. This allows running the\n handler without administrator privileges.\n \"\"\"\n def __init__(self, appname, dllname=None, logtype=\"Application\",\n add_to_registry=False):\n logging.Handler.__init__(self)\n try:\n import win32evtlogutil, win32evtlog\n self.appname = appname\n self._welu = win32evtlogutil\n if not dllname:\n dllname = os.path.split(self._welu.__file__)\n dllname = os.path.split(dllname[0])\n dllname = os.path.join(dllname[0], r'win32service.pyd')\n self.dllname = dllname\n self.logtype = logtype\n if(add_to_registry):\n self._welu.AddSourceToRegistry(appname, dllname, logtype)\n self.deftype = win32evtlog.EVENTLOG_ERROR_TYPE\n self.typemap = {\n logging.DEBUG : win32evtlog.EVENTLOG_INFORMATION_TYPE,\n logging.INFO : win32evtlog.EVENTLOG_INFORMATION_TYPE,\n logging.WARNING : win32evtlog.EVENTLOG_WARNING_TYPE,\n logging.ERROR : win32evtlog.EVENTLOG_ERROR_TYPE,\n logging.CRITICAL: win32evtlog.EVENTLOG_ERROR_TYPE,\n }\n except ImportError:\n print(\"The Python Win32 extensions for NT (service, event \"\\\n \"logging) appear not to be available.\")\n self._welu = None\n","sub_path":"admin/django/dafousers/logginghandlers.py","file_name":"logginghandlers.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"49290535","text":"import math\n\n# Første eksempel på en egendefinert klasse: Punkt\n#\n# Denne versjonen bruker properties for å sørge for at koordinatene ikke kan være negative. En property kan\n# brukes som om den var en instansvariabel (variabel definert i __init__)\nclass Punkt:\n # Konstruktør, denne skal lage objektet. self er objektet som blir\n # lagd. De andre parameterne er som for en funksjon\n def __init__(self, start_x=0, start_y=0):\n self.x = start_x\n self.y = start_y\n\n # Getter for egenskapen x. En getter er en metode som henter ut verdien til en egenskap\n @property\n def x(self):\n return self.__x\n\n # Setter for egenskapen x. Denne sjekker at x ikke kan være negativ . En setter er en metode som setter verdien\n # til en egenskap.\n @x.setter\n def x(self, ny_x):\n if ny_x > 0.0:\n self.__x = ny_x\n\n # Getter for egenskapen y\n @property\n def y(self):\n return self.__y\n\n # Setter for egenskapen y\n @y.setter\n def y(self, ny_y):\n if ny_y > 0.0:\n self.__y = ny_y\n\n # Metode for klassen Punkt.\n def flytt(self, delta_x, delta_y):\n self.x += delta_x\n self.y += delta_y\n\n def __str__(self):\n return \"Punkt: (\" + str(self.x) + \", \" + str(self.y) + \")\"\n\n\ndef flytt_til_midten(punkt1, punkt2):\n midt_x = (punkt1.x + punkt2.x)/2\n midt_y = (punkt1.y + punkt2.y)/2\n punkt1.x = midt_x\n punkt1.y = midt_y\n\n\nif __name__ == \"__main__\":\n punkt1 = Punkt(3, 4)\n print(str(punkt1))\n print(punkt1.x)\n punkt1.flytt(-2, 0)\n punkt2 = Punkt(12, 23)\n print(punkt2)\n punkt2.flytt(1, 1)\n print(punkt2)\n print(punkt1)\n flytt_til_midten(punkt1, punkt2)\n print(punkt1)\n punkt2.x = 3\n print(punkt2)\n punkt2.x = -3\n print(punkt2)\n\n\n","sub_path":"klasser_og_objekter/punkt_properties.py","file_name":"punkt_properties.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"82568848","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport csv, codecs, cStringIO\nimport json\nimport logging\nimport os\nimport re\nfrom collections import defaultdict\n\n# Script that cleans up budgettaire_tabellen_owb_201X_origineel.csv\n# files and writes the output to another .csv called\n# 'budgettaire_tabellen_owb_2015.csv' and the directory\n# 'budgettaire_tabellen_json' which contains a .json file holding the\n# values in a nested way for each combination of year,\n# uitgaven (U)/verplichtingen (V)/ontvangsten (O) and type of budget\n# (i.e., ontwerpbegroting/vastgestelde_begroting/\n# eerste_suppletoire_begroting/tweede_suppletoire_begroting/realisatie).\n# The .json files are hierarchically structured in the format used for\n# hierarchical visualisations by D3.js\n# (https://github.com/d3/d3-hierarchy/blob/master/README.md#hierarchy).\n\n# Classes to read and write UTF-8 .csv's\nclass UTF8Recoder:\n \"\"\"\n Iterator that reads an encoded stream and reencodes the input to UTF-8\n \"\"\"\n def __init__(self, f, encoding):\n self.reader = codecs.getreader(encoding)(f)\n\n def __iter__(self):\n return self\n\n def next(self):\n return self.reader.next().encode(\"utf-8\")\n\nclass UnicodeReader:\n \"\"\"\n A CSV reader which will iterate over lines in the CSV file \"f\",\n which is encoded in the given encoding.\n \"\"\"\n\n def __init__(self, f, dialect=csv.excel, encoding=\"utf-8\", **kwds):\n f = UTF8Recoder(f, encoding)\n self.reader = csv.reader(f, dialect=dialect, **kwds)\n\n def next(self):\n row = self.reader.next()\n return [unicode(s, \"utf-8\") for s in row]\n\n def __iter__(self):\n return self\n\nclass UnicodeWriter:\n \"\"\"\n A CSV writer which will write rows to CSV file \"f\",\n which is encoded in the given encoding.\n \"\"\"\n\n def __init__(self, f, dialect=csv.excel, encoding=\"utf-8\", **kwds):\n # Redirect output to a queue\n self.queue = cStringIO.StringIO()\n self.writer = csv.writer(self.queue, dialect=dialect, **kwds)\n self.stream = f\n self.encoder = codecs.getincrementalencoder(encoding)()\n\n def writerow(self, row):\n self.writer.writerow([s.encode(\"utf-8\") for s in row])\n # Fetch UTF-8 output from the queue ...\n data = self.queue.getvalue()\n data = data.decode(\"utf-8\")\n # ... and reencode it into the target encoding\n data = self.encoder.encode(data)\n # write to the target stream\n self.stream.write(data)\n # empty queue\n self.queue.truncate(0)\n\n def writerows(self, rows):\n for row in rows:\n self.writerow(row)\n\n\n# Create a directory to store logs of this script\nlog_dir = 'logs'\nif not os.path.exists(log_dir):\n os.mkdir(log_dir)\n\n# Initialize logger where we write the duplicate entries to\nlog_dup = logging.getLogger('overwrite_logger')\nlog_dup.addHandler(logging.FileHandler(log_dir + '/duplicate_hierarchies.log'))\n\n# Mapping of hoofdstuk indicator to the name of the hoofdstuk\nmapping = {\n \"A\": \"Infrastructuurfonds\",\n \"B\": \"Gemeentefonds\",\n \"C\": \"Provinciefonds\",\n \"F\": \"Diergezondheidsfonds\",\n \"H\": \"BES-fonds\",\n \"I\": \"De Koning\",\n \"IIA\": \"De Staten Generaal\",\n \"IIB\": \"Overige Hoge Colleges van Staat\",\n \"III\": \"Algemene Zaken\",\n \"IIIA\": \"Algemene Zaken\",\n \"IIIB\": \"Kabinet van de Koning\",\n \"IIIC\": \"Commissie van Toezicht betreffende de Inlichtingen- en Veiligheidsdienst\",\n \"IV\": \"Koninkrijksrelaties\",\n \"IXA\": \"Nationale Schuld\",\n \"IXB\": u\"Financiën\",\n \"J\": \"Deltafonds\",\n \"V\": \"Buitenlandse Zaken\",\n \"VII\": \"Binnenlandse Zaken en Koninkrijksrelaties\",\n \"VIII\": \"Onderwijs, Cultuur en Wetenschap\",\n \"VI\": \"Veiligheid en Justitie\",\n \"X\": \"Defensie\",\n \"XIII\": \"Economische Zaken\",\n \"XII\": \"Infrastructuur en Milieu\",\n \"XVII\": \"Buitenlandse Handel & Ontwikkelingssamenwerking\",\n \"XVIII\": \"Wonen en Rijksdienst\",\n \"XVI\": \"Volksgezondheid, Welzijn en Sport\",\n \"XV\": \"Sociale Zaken en Werkgelegenheid\",\n \"LVIII\": \"Diergezondheidsfonds\"\n}\n\n# Set up the datastructure for the .json output files\ntree = lambda: defaultdict(tree)\n\n# Log information on lines which would have overwritten an already\n# existing line in the json_data, because their hierarchy is the same\ndef print_existing(value, hierarchy_list, new_line):\n log_dup.warning(\"Found value %s at: '%s'\" % (value, \"' > '\".join(hierarchy_list)))\n log_dup.warning('Thus could not save: %s\\n' % (', '.join(new_line)))\n\n# Use the values in hierarchy_list to retrieve its value stored in\n# json_data. E.g., when hierarchy_list contains\n# [u'U', 'Koninkrijksrelaties', u'Nominaal en onvoorzien'] this function\n# will the value stored in\n# json_data[u'U']['Koninkrijksrelaties'][u'Nominaal en onvoorzien']\ndef get_dict_with_list(json_data, hierarchy_list):\n for k in hierarchy_list: json_data = json_data[k]\n return json_data\n\n# The same logic of get_dict_with_list to use hierarchy_list to traverse\n# json_data is used here to store a value instead of retrieving a value\ndef set_dict_with_list(json_data, hierarchy_list, value):\n for key in hierarchy_list[:-1]:\n json_data = json_data.setdefault(key, {})\n json_data[hierarchy_list[-1]] = value\n\n# Check if a value is already stored using this hierarchy_list of the\n# current line. get_dict_with_list can return three states. 1) it\n# returns empty, which means nothing is stored yet at this place in the\n# hierarchy, so the current line can be stored here. 2) it is not empty,\n# the current line and already stored information are printed for later\n# analysis as to why there is a collision. 3) a TypeError is returned,\n# which means that something is already stored in this hierarchy but at\n# a higher level. We still want to know what is stored there for\n# analysis, so we recursively call this function again using the\n# hierarchy_list one level up.\ndef already_exists(json_data, hierarchy_list, new_line, exists=False):\n try:\n if get_dict_with_list(json_data, hierarchy_list):\n print_existing(get_dict_with_list(json_data, hierarchy_list), hierarchy_list, new_line)\n return True\n elif exists:\n return True\n else:\n return False\n except TypeError:\n return already_exists(json_data, hierarchy_list[:-1], new_line, True)\n\n# This function starts by trying to save the line at its most detailed\n# level (omschrijving), but if this field is empty then it moves up one\n# level and tries the same. Checks are also in place to see if a line\n# will overwrite an already existing value in the hierarchy, in which\n# case the information is logged and the new line is discarded in favor\n# of the already saved line.\ndef store_json_data_recursively(json_data, hierarchy_list, new_line):\n if hierarchy_list[-1] or len(hierarchy_list) == 3:\n if already_exists(json_data, hierarchy_list, new_line):\n return\n set_dict_with_list(json_data, hierarchy_list, new_line[21])\n else:\n store_json_data_recursively(json_data, hierarchy_list[:-1], new_line)\n\n# Artikel names are not consistent, so the artikel numbers/codes are\n# used in this script. For human readability we do want to output the\n# artikel names in the JSON output. A mapping is created for each each\n# artikel number/code to the artikel name when an artikel is seen for\n# the first time.\nartikel_mapping = {}\n\n# Save lines in a hierarchically structured way. This requires recursion\n# as we need to find the most detailed level for which we can store a\n# value (e.g., some value are stored at the 'artikelonderdeel' level,\n# while others are stored at the 'omschrijving' level).\ndef store_json_data(json_data, uvo, hoofdstuk, artikel, new_line):\n hoofdstuk = mapping[hoofdstuk]\n # If the artikel number/code is not available in the mapping, then\n # add it together with the artikel name to the mapping\n if hoofdstuk + '_' + artikel not in artikel_mapping:\n artikel_mapping[hoofdstuk + '_' + artikel] = new_line[8]\n artikel = artikel_mapping[hoofdstuk + '_' + artikel]\n artikelonderdeel = new_line[15]\n subartikelonderdeel = new_line[16]\n uitsplitsing = new_line[17]\n omschrijving = new_line[18]\n store_json_data_recursively(json_data, [uvo, hoofdstuk, artikel, artikelonderdeel, subartikelonderdeel, uitsplitsing, omschrijving], new_line)\n\n# Perform all cleanup actions, see the comments for details\ndef clean(year):\n # This dictionary will be used to keep track of the largest total\n # value found for a certain artikel. If this artikel doesn't have\n # any detailed values then use this total value as its most detailed\n # value.\n dict_data = tree()\n # This dictionary will be used to store the data in a hierarchical\n # way in order to be exported to json\n json_data = tree()\n\n # All rows are doubled in 2016, so don't process a hoofdstuk if\n # we've seen it already\n seen = {}\n \n # Open the .csv files to read from and write to\n with open('budgettaire_tabellen_owb_%s_origineel.csv' % (year)) as IN, \\\n open('budgettaire_tabellen_owb_%s.csv' % (year), 'w') as OUT:\n budget_data = UnicodeReader(IN)\n writer = UnicodeWriter(OUT)\n # Retrieve the first line containing the column names\n column_names = budget_data.next()\n # Write the first line containing the column names to the .csv\n # file\n writer.writerow(column_names)\n\n seen_last_row = ''\n\n # Process each line of the input data\n line_count = 1\n for line in budget_data:\n line_count += 1\n # Store any changes to the line in new_line\n new_line = line\n\n # Remove leading and trailing whitespace from all fields\n new_line = [field.strip() for field in new_line]\n\n # Logic to tell if we've already seen a whole block of one\n # hoofdstuk in 2016 in order to skip the second block with\n # the same values\n if line[0] == '2016' and line[1] in seen:\n continue\n elif line[0] == '2016' and line[1] not in seen:\n if seen_last_row != line[1] and seen_last_row:\n seen[seen_last_row] = True\n seen_last_row = line[1]\n\n # This line contains years as values which is incorrect so\n # skip it in 2017\n if line_count == 1409 and line[0] == '2017':\n continue\n\n # Skip lines which don't have an artikel number/code; this\n # happens at least in line 162-164 in 2017\n if not new_line[6]:\n continue\n\n # The following two lines contain\n # 'V/U/O (Verplichtingen/Uitgaven/Ontvangsten)' instead of\n # 'V' as value in 2017\n if (line_count == 2313 or line_count == 2332) and line[0] == '2017':\n new_line[12] = 'V'\n # Sometimes lowercase values are used, 'u'/'v'/'o', convert them to upper case\n uvo = new_line[12].upper()\n\n # The input .csv files use just 'III' for three different\n # begrotingen, so correct these to the codes that are used\n # on rijksbegroting.nl, e.g.\n # http://rijksbegroting.nl/2017/voorbereiding/begroting,kst225610.html\n if new_line[3] == 'Algemene Zaken':\n new_line[1] = 'IIIA'\n elif new_line[3] == 'Kabinet van de Koning':\n new_line[1] = 'IIIB'\n elif new_line[3] == 'Commissie van Toezicht betreffende de Inlichtingen- en Veiligheidsdienst':\n new_line[1] = 'IIIC'\n\n # In 2017, the last 7/8 columns of lines 899-936 have\n # shifted one column to the right\n if line_count in range(899, 937) and line[0] == '2017':\n if new_line[19]:\n new_line[18] = new_line[19]\n new_line[19:] = new_line[20:]\n\n # Column T has a 'o' instead of a 0 in 2017\n if line_count == 2419 and line[0] == '2017':\n new_line[19] = '0'\n\n # Change line[3] to 'Overige Hoge Colleges van Staat en\n # Kabinetten van de Gouverneurs' instead of\n # 'Staten Generaal' in 2016 and 2017 for hoofdstuk IIB\n if new_line[1] == 'IIB' and (new_line[0] == '2016' or new_line[0] == '2017') and new_line[3] == 'Staten Generaal':\n new_line[3] = 'Overige Hoge Colleges van Staat en Kabinetten van de Gouverneurs'\n\n # In 2017, the 'Deltafonds' hoofdstuk value is 'A' instead\n # of 'J' in 2017\n if new_line[3] == 'Deltafonds' and new_line[0] == '2017':\n new_line[1] = 'J'\n\n # Remove lines containing either 'pm' ('pro memorie', i.e., the value is not (yet) known) or '%' or if it doesn't contain a number\n if not new_line[21] or 'pm' in new_line[21] or '%' in new_line[21] or not re.search(r'\\d', new_line[21]):\n continue\n\n # Remove the comma thousands separator\n val = new_line[21].replace(',', '')\n\n # 2015 uses badly formatted floats, so round those values\n if '.' in val:\n val = round(float(val))\n val = int(val)\n\n new_line[21] = unicode(val)\n\n artikel = new_line[6]\n hoofdstuk = new_line[1]\n\n # Don't output lines with a 'J' in column N as these are\n # (sub)totals which we don't need as we can calculate them\n # using the most detailed values. We do store the maximum\n # total values found as some artikelen don't have detailed\n # values so this total value will be the most detailed\n # value.\n if new_line[13] == 'J':\n if 'max_val' in dict_data[hoofdstuk][artikel][uvo]:\n if val > dict_data[hoofdstuk][artikel][uvo]['max_val']:\n dict_data[hoofdstuk][artikel][uvo]['max_val'] = val\n dict_data[hoofdstuk][artikel][uvo]['max_val_line'] = new_line\n else:\n dict_data[hoofdstuk][artikel][uvo]['max_val'] = val\n dict_data[hoofdstuk][artikel][uvo]['max_val_line'] = new_line\n # If column N has the value 'N', then this line contains a\n # detailed value so write it to the output .csv file and\n # also set the 'has_detailed_values' flag which shows that\n # this artikel has detailed values\n else:\n writer.writerow(new_line)\n\n # Store data for JSON export\n store_json_data(json_data, uvo, hoofdstuk, artikel, new_line)\n\n if 'has_detailed_values' not in dict_data[hoofdstuk][artikel][uvo]:\n dict_data[hoofdstuk][artikel][uvo]['has_detailed_values'] = True\n # Some artikelen don't have detailed values and only a total\n # value. If this is the case then write this line to the .csv\n # file as well, because it is the most detailed value.\n for hoofdstuk, hoofdstuk_values in dict_data.iteritems():\n for artikel, artikel_values in hoofdstuk_values.iteritems():\n for uvo, uvo_values in artikel_values.iteritems():\n if not 'has_detailed_values' in uvo_values:\n writer.writerow(uvo_values['max_val_line'])\n # Store data for JSON export\n store_json_data(json_data, uvo, hoofdstuk, artikel, uvo_values['max_val_line'])\n return json_data\n\n# Recursively iterate over all nested levels from json_data and save the\n# names and values in the hierarchically structured format\ndef recursively_extract(parent_item):\n if type(parent_item) == unicode:\n return u'found_leaf'\n item_list = []\n for item, item_value in parent_item.iteritems():\n child_item_return = recursively_extract(item_value)\n if child_item_return == u'found_leaf':\n item_list.append(\n {\n \"name\": item,\n \"value\": item_value\n }\n )\n if type(child_item_return) == list:\n item_list.append(\n {\n \"name\": item,\n \"children\": child_item_return\n }\n )\n return item_list\n\n## Save hierarchical JSON data\n# Loop over all years\nyears = ['2015', '2016', '2017']\nfor year in years:\n json_data = clean(year)\n\n # Directory name to save the .json files in\n dirname = 'budgettaire_tabellen_json'\n # Create the directory if it does not exist\n if not os.path.exists(dirname):\n os.mkdir(dirname)\n # Loop over all hierarchies in the dictionary\n for uvo, uvo_values in json_data.iteritems():\n # Open a .json file for this combination of year, uvo and\n # budget_type (we currently only take ontwerpbegroting value\n # 't'/column N) to write to\n filename = '%s-%s-%s' % (year, uvo, 'ontwerpbegroting')\n with open('%s/%s.json' % (dirname, filename), 'w') as OUT:\n hoofdstuk_list = recursively_extract(uvo_values)\n out_data = { \n \"name\": filename,\n \"children\": hoofdstuk_list\n }\n json.dump(out_data, OUT, indent=4)\n\n# Save the created artikel mapping for logging purposes\nwith open(log_dir + '/artikel_mapping.json', 'w') as OUT:\n json.dump(artikel_mapping, OUT, indent=4, sort_keys=True)\n","sub_path":"budgettaire tabellen/convert_budgettaire_tabellen.py","file_name":"convert_budgettaire_tabellen.py","file_ext":"py","file_size_in_byte":17543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308617838","text":"# coding=utf-8\n# summary:\n# author: Jianqiang Ren\n# date:\n\n\nimport tensorflow as tf\nimport argparse\nimport os\nimport cv2\nimport numpy as np\nfrom module import generator_resnet\nfrom collections import namedtuple\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('--ckpt', dest='ckpt', type=str)\nparser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='# images in batch')\nparser.add_argument('--fine_size', dest='fine_size', type=int, default=256, help='then crop to this size')\nparser.add_argument('--ngf', dest='ngf', type=int, default=64, help='# of gen filters in first conv layer')\nparser.add_argument('--ndf', dest='ndf', type=int, default=64, help='# of discri filters in first conv layer')\nparser.add_argument('--input_nc', dest='input_nc', type=int, default=3, help='# of input image channels')\nparser.add_argument('--output_nc', dest='output_nc', type=int, default=3, help='# of output image channels')\n\nargs = parser.parse_args()\n\n\n\ndef freeze(ckpt_path):\n \n \n OPTIONS = namedtuple('OPTIONS', 'batch_size image_size \\\n gf_dim df_dim output_c_dim')\n options = OPTIONS._make((args.batch_size, args.fine_size,\n args.ngf, args.ndf, args.output_nc))\n \n \n inp_content_image = tf.placeholder(tf.float32, shape=(1, None, None, 3), name='input')\n \n \n out_image = generator_resnet(inp_content_image, options,name='generatorA2B')\n out_image = tf.identity(out_image, name='output')\n \n init_op = tf.global_variables_initializer()\n \n restore_saver = tf.train.Saver()\n \n with tf.Session() as sess:\n sess.run(init_op)\n restore_saver.restore(sess, ckpt_path)\n frozen_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,\n output_node_names=['output'])\n \n path = os.path.dirname(ckpt_path)\n with open(path + '/cyclegan.pb', 'wb') as f:\n f.write(frozen_graph_def.SerializeToString())\n\n\nif __name__ == '__main__':\n ckpt_path = args.ckpt\n freeze(ckpt_path)\n print('freeze done')","sub_path":"freeze.py","file_name":"freeze.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"329937715","text":"\"\"\"falsr_b\"\"\"\nimport os\nimport cv2\nimport paddle\n\nimport paddlehub as hub\n\nif paddle.is_compiled_with_cuda():\n paddle.set_device(\"gpu\")\n use_gpu = True\nelse:\n paddle.set_device(\"cpu\")\n use_gpu = False\n\n\ndef test_falsr_b_predict():\n \"\"\"falsr_b\"\"\"\n os.system(\"hub install falsr_b\")\n sr_model = hub.Module(name=\"falsr_b\")\n im = cv2.imread(\"low_pixel.jpeg\").astype(\"float32\")\n # visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。\n res = sr_model.reconstruct(images=[im], visualization=True)\n print(res[0][\"data\"])\n os.system(\"hub uninstall falsr_b\")\n","sub_path":"models/PaddleHub/hub_all_func/all_module/all_falsr_b.py","file_name":"all_falsr_b.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"143232180","text":"from karapace.schema_reader import SchemaType, TypedSchema\nfrom karapace.serialization import SchemaRegistryClient\nfrom tests.utils import schema_avro_json\n\n\nasync def test_remote_client(registry_async_client):\n schema_avro = TypedSchema.parse(SchemaType.AVRO, schema_avro_json)\n reg_cli = SchemaRegistryClient()\n reg_cli.client = registry_async_client\n sc_id = await reg_cli.post_new_schema(\"foo\", schema_avro)\n assert sc_id >= 0\n stored_schema = await reg_cli.get_schema_for_id(sc_id)\n assert stored_schema == schema_avro, f\"stored schema {stored_schema.to_json()} is not {schema_avro.to_json()}\"\n stored_id, stored_schema = await reg_cli.get_latest_schema(\"foo\")\n assert stored_id == sc_id\n assert stored_schema == schema_avro\n","sub_path":"tests/integration/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"635596042","text":"from kivy.app import App\nfrom kivy.uix.widget import Widget\nfrom kivy.properties import NumericProperty, ReferenceListProperty,\\\n ObjectProperty\nfrom kivy.vector import Vector\nfrom kivy.clock import Clock\nimport random\nfrom kivy.uix.image import Image\n\nclass Magnes(Widget):\n narysowany = NumericProperty(0)\n velocity_x = NumericProperty(0)\n velocity_y = NumericProperty(-3)\n velocity = ReferenceListProperty(velocity_x, velocity_y)\n pozycja_x = NumericProperty(0)\n pozycja_y = NumericProperty(0)\n pozycja = ReferenceListProperty(pozycja_x, pozycja_y)\n #magnet_image = ObjectProperty(Image())\n\n\n def przyspiesz(self):\n self.velocity_y -= 1\n\n def wyczysc(self, race):\n self.narysowany = 0\n self.pos = (random.randrange(race.width - self.width),race.height)\n self.velocity_y = -3\n\n def zderzenie(self, ball, race):\n if self.collide_widget(ball):\n self.narysowany = 0\n self.pos = (random.randrange(race.width - self.width),race.height)\n self.velocity_y = -3\n return True\n\n def move_obstacle(self, race):\n if self.narysowany == 0:\n self.pos = (random.randrange(race.width - self.width), race.height)\n self.size = (race.width * 4/10, race.height * 1/10)\n self.narysowany = 1\n self.pos = Vector(*self.velocity) + self.pos\n self.pozycja = self.pos\n if self.pozycja_y + self.height < 0:\n self.narysowany = 0\n\nclass Electron(Widget):\n score = NumericProperty(0)\n record = NumericProperty(0)\n #electron_image = ObjectProperty(Image())\n\nclass RaceGame(Widget):\n ball = ObjectProperty(None)\n magnes1 = ObjectProperty(None)\n magnes2 = ObjectProperty(None)\n magnes3 = ObjectProperty(None)\n move = NumericProperty(0)\n first_draw = NumericProperty(0)\n first_draw_magnes2 = NumericProperty(0)\n first_draw_magnes3 = NumericProperty(0)\n przyspieszenie_kulki = NumericProperty(5)\n przyrost_odleglosci = NumericProperty(1)\n poziom_przyspieszenia = NumericProperty(0)\n prog_przyspieszenia = NumericProperty(500)\n\n def update(self, dt):\n if self.ball.score > self.prog_przyspieszenia and self.poziom_przyspieszenia < 18:\n self.poziom_przyspieszenia += 1\n self.prog_przyspieszenia += 2000 * self.poziom_przyspieszenia\n self.magnes1.przyspiesz()\n self.magnes2.przyspiesz()\n self.magnes3.przyspiesz()\n self.przyrost_odleglosci += self.poziom_przyspieszenia\n self.przyspieszenie_kulki += 1\n\n\n Magnes.move_obstacle(self.magnes1, self)\n if self.magnes1.pozycja_y + self.magnes1.height / 2 < self.height * 2/3 or self.first_draw_magnes2 == 1:\n Magnes.move_obstacle(self.magnes2, self)\n self.first_draw_magnes2 = 1\n if self.magnes2.pozycja_y + self.magnes2.height / 2 < self.height * 2/3 or self.first_draw_magnes3 == 1:\n Magnes.move_obstacle(self.magnes3, self)\n self.first_draw_magnes3 = 1\n\n \"\"\"\n Czyszczenie ekranu po kolizji\n \"\"\"\n if Magnes.zderzenie(self.magnes1, self.ball, self)\\\n or Magnes.zderzenie(self.magnes2, self.ball, self)\\\n or Magnes.zderzenie(self.magnes3, self.ball, self):\n if self.ball.score > self.ball.record:\n self.ball.record = self.ball.score\n self.first_draw = 0\n self.first_draw_magnes2 = 0\n self.first_draw_magnes3 = 0\n Magnes.wyczysc(self.magnes1, self)\n Magnes.wyczysc(self.magnes2, self)\n Magnes.wyczysc(self.magnes3, self)\n self.przyrost_odleglosci = 1\n self.poziom_przyspieszenia = 0\n self.prog_przyspieszenia = 500\n\n if self.first_draw == 0:\n self.ball.score = 0\n self.ball.center_x = self.center_x\n self.first_draw = 1\n self.ball.center_y = self.center_y * 1/10\n self.ball.score += self.przyrost_odleglosci\n\n\n if self.move == 1 and self.ball.center_x > self.width - self.width + 25:\n if self.ball.center_x - self.przyspieszenie_kulki < self.width - self.width + 25:\n self.ball.center_x = self.width - self.width + 25\n else:\n self.ball.center_x -= self.przyspieszenie_kulki\n elif self.move == 2 and self.ball.center_x < self.width - 25:\n if self.ball.center_x + self.przyspieszenie_kulki > self.width - 25:\n self.ball.center_x = self.width - 25\n else:\n self.ball.center_x += self.przyspieszenie_kulki\n pass\n\n \"\"\"\n Reakcja na dotyk, reaguje na przytrzymanie\n \"\"\"\n def on_touch_down(self, touch):\n if touch.x < self.width / 2 and self.ball.center_x > self.width - self.width + 25:\n self.move = 1\n if touch.x > self.width / 2 and self.ball.center_x < self.width - 25:\n self.move = 2\n\n def on_touch_up(self, touch):\n self.move = 0\n\nclass RaceApp(App):\n def build(self):\n game = RaceGame()\n Clock.schedule_interval(game.update, 1.0 / 60.0)\n return game\n\n\nif __name__ == '__main__':\n RaceApp().run()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"288592973","text":"import sys\n\n# input file\nf = open('testinput.txt', 'r')\nsys.stdin = f\n\ndef miniMaxSum(arr):\n t=0\n ar = sorted(arr)\n for i in ar:\n t += i\n print(*[t-ar[4],t-ar[0]])\n\n\nif __name__ == '__main__':\n arr = list(map(int, input().rstrip().split()))\n\n miniMaxSum(arr)\n","sub_path":"122---HackerRank/ProblemSolving/minimaxsum/minimaxsum.py","file_name":"minimaxsum.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"584594272","text":"from question_model import Question\nfrom data import question_data as data\nfrom quiz_brain import QuizBrain\n\n\ndef main():\n question_bank = []\n for e in data:\n question_bank.append(Question(e[\"text\"], e[\"answer\"]))\n quiz = QuizBrain(question_bank)\n while quiz.still_has_questions():\n quiz.next_question()\n print(\"You've completed the quiz!\")\n print(f\"Your final score was {quiz.score}/{quiz.question_number}\")\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Day_017/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"425867712","text":"#!/usr/bin/python\n#coding:utf-8\n\nimport cv2\nimport os\nimport sys\nimport argparse\n\nusage = 'Usage: python {} INPUT_FILE [--prefix ] [--dir ] [--help]'.format(__file__)\nparser = argparse.ArgumentParser(description='This script is to generate images from a video.',\n usage=usage)\nparser.add_argument('input_video', action='store', nargs=None, \n type=str, help='Input video.')\nparser.add_argument('-p', '--prefix', action='store', nargs='?',\n default='frame', type=str, help='Prefix of Output file name.')\nparser.add_argument('-d', '--dir', action='store', nargs='?',\n default='data', type=str, help='Directory of Output files.')\nparser.add_argument('-r', '--ratio', action='store',\n default=0.1, type=float, help='Ratio of test datum.')\nparser.add_argument('-w', '--width', action='store',\n default=-1, type=int, help='Width of images.')\nparser.add_argument('-g', '--height', action='store',\n default=-1, type=int, help='height of images.')\nargs = parser.parse_args()\n\n\nvidcap = cv2.VideoCapture(args.input_video)\nsuccess, image = vidcap.read()\ncount = 0\nfiles = []\nprint(\"Start to save images...\")\nwhile True:\n success, image = vidcap.read()\n if not success:\n break\n files.append(os.path.join(args.dir, \"%s_%07d.jpg\" % (args.prefix, count)))\n sys.stdout.write('\\rSave {}'.format(files[-1]))\n sys.stdout.flush()\n if args.width > 0:\n height, width = image.shape[0], image.shape[1]\n if args.height < 0:\n height = int(height * float(args.width) / width)\n else:\n height = args.height\n image = cv2.resize(image, (args.width, height))\n cv2.imwrite(files[-1], image)\n count += 1\n\ntrain_list_file = os.path.join(args.dir, \"train_list.txt\")\ntest_list_file = os.path.join(args.dir, \"test_list.txt\")\nratio = max(0.0, min(1.0, args.ratio))\nindex = int(len(files) * (1.0 - ratio))\nprint('\\nSave %s' % train_list_file)\nwith open(train_list_file, 'w') as f:\n f.write('\\n'.join(files[:index]))\nprint('Save %s' % test_list_file)\nwith open(test_list_file, 'w') as f:\n f.write('\\n'.join(files[index:]))\nprint(\"Done.\")\n","sub_path":"PredNet_scripts/generate_image.py","file_name":"generate_image.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"647283356","text":"# vim: ts=2:sw=2:tw=80:nowrap\n\nimport copy\nfrom logging import error, warn, debug, log, DEBUG, INFO, root as rootlog\nimport Pyro4\n\nfrom ....device import Device as Base\nfrom .....tools.signal_graphs import nearest_terminal\nfrom .....tools import cached\nfrom physical import unit\nimport nidaqmx\nimport numpy as np\n\n\nclass Task(Base):\n task_type = None\n task_class = None\n STATIC = 0\n WAVEFORM_SINGLE = 1\n WAVEFORM_CONTINUOUS = 2\n finite_end_clock = False # whether this task requires end-clock (see analog.py)\n\n def __init__(self, driver, device):\n Base.__init__(self, name='{d}/{tt}'.format(d=device,tt=self.task_type))\n self.task = None\n self.channels = dict()\n self.clocks = dict()\n self.clock_terminal = None\n self.use_case = None\n self.t_max = 0.0 * unit.s\n\n # first find the possible trigger and clock sources\n self.trig_sources = list()\n self.clock_sources = list()\n self.SCTB_sources = list()\n self.sources_to_native = dict()\n\n # make sure the strip off the leading 'ni' but leave the '/'\n clk = self.format_ni_terminal_name('SampleClock')\n trg = self.format_ni_terminal_name('StartTrigger')\n sctb= self.format_ni_terminal_name('SampleClockTimebase')\n\n lD = {clk:self.clock_sources, trg:self.trig_sources, sctb:self.SCTB_sources}\n\n for i in driver.rl.signal_route_map.items():\n l = lD.get( i[1][1], None )\n if l is not None:\n l.append( i[0][0] )\n self.sources_to_native[ i[0][0] ] = i[1][0]\n\n self.config = self.get_config_template()\n\n\n def __del__(self):\n self.clear()\n\n\n def clear(self):\n if self.task:\n debug( 'nidaqmx: clearing NIDAQmx task %s', self.task )\n del self.task\n self.task = None\n self.t_max = 0.0 * unit.s\n\n def format_ni_terminal_name(self, terminal):\n return self.name[len(self.prefix):] + '/' + terminal\n\n def add_channels(self):\n \"\"\"\n Sub-task types must override this for specific channel creation.\n \"\"\"\n # populate the task with output channels and accumulate the data\n for c in self.channels:\n warn( 'creating unknown NIDAQmx task/channel: %s/%s', self.task, c )\n self.task.create_channel(c.partition('/')[-1]) # cut off the prefix\n\n @cached.property\n def has_onboardclock(self):\n return self.onboardclock_name in self.clock_sources\n\n @cached.property\n def onboardclock_name(self):\n return self.name + '/' + 'SampleClock'\n\n def set_config(self, config=None, channels=None, signal_graph=None):\n do_rebuild = False\n if channels and self.channels != channels:\n self.channels = channels\n do_rebuild = True\n if config and self.config != config:\n self.config = config\n do_rebuild = True\n\n if not self.config['clock']['value']:\n self.clock_terminal = None\n else:\n if signal_graph:\n if self.config['clock']['value'] == self.onboardclock_name:\n # don't have to lookup anymore, since we know it is already the\n # onboard clock\n self.clock_terminal = 'OnboardClock'\n else:\n self.clock_terminal = \\\n self.sources_to_native[\n nearest_terminal( self.config['clock']['value'],\n set(self.clock_sources),\n signal_graph ) ]\n do_rebuild = True\n\n if do_rebuild:\n self._rebuild_task()\n\n def _rebuild_task(self):\n # rebuild the task\n self.clear()\n if not self.channels:\n return\n debug( 'nidaqmx: creating task: %s', self.name )\n self.task = self.task_class(self.name.replace('/','-'))\n self.use_case = None\n self.add_channels()\n\n # set persistent task properties\n # Not sure if we really need to worry about on-board memory\n # self.task.set_use_only_onboard_memory(\n # self.config['use-only-onboard-memory']['value'] )\n\n\n def set_clocks(self, clocks):\n \"\"\"\n If this is an analog device, this must be the onboard clock only.\n If this is a digital device, either an Onboard timer for the digital device\n (if supported) or aperiodic clock implemented by a digital line of a digital\n device.\n If this is a timing device, this must be one of the counters.\n \"\"\"\n if self.clocks != clocks:\n self.clocks = clocks\n\n\n def set_output(self, data):\n \"\"\"\n Sets a static value on each output channel of this task.\n \"\"\"\n if self.use_case in [ None, Task.STATIC ]:\n if self.use_case is not None:\n debug( 'nidaqmx: stopping task: %s', self.task )\n self.task.stop()\n else:\n self._rebuild_task()\n self.use_case = Task.STATIC\n\n debug( 'nidaqmx: configuring task for static output: %s', self.task )\n self.task.set_sample_timing( timing_type='on_demand',\n mode='finite',\n samples_per_channel=1 )\n if self.trig_sources:\n # this device _does_ accept triggers\n self.task.configure_trigger_disable_start()\n # get the data\n px = self.prefix\n chlist = ['{}/{}'.format(px,c) for c in self.task.get_names_of_channels()]\n assert set(chlist) == set( data.keys() ), \\\n 'NIDAQmx.set_output: mismatched channels'\n debug( 'nidaqmx: writing static data for channels: %s', chlist )\n if rootlog.getEffectiveLevel() <= (DEBUG-1):\n log(DEBUG-1, '%s', [ float(data[c]) for c in chlist ])\n self.task.write( [ data[c] for c in chlist ] )\n debug( 'nidaqmx: starting task: %s', self.task )\n self.task.start()\n\n\n def get_min_period(self):\n if self.has_onboardclock and self.task and self.channels:\n return unit.s / self.task.get_sample_clock_max_rate()\n return 0*unit.s\n\n\n def get_clock_rate(self):\n if not self.has_onboardclock:\n # It seems that if a device does not have an onboard clock, the call to\n # get_sample_clock_max_rate fails.\n # If this is ever an analog output device, this will probably fail since\n # the max-rate must be specified to program the DAC settling time.\n return 0\n if self.clock_terminal == 'OnboardClock':\n return self.clocks[ self.onboardclock_name ]['rate']['value']\n return self.task.get_sample_clock_max_rate()\n\n\n def set_waveforms(self, waveforms, clock_transitions, t_max, continuous):\n \"\"\"\n Set up the hardware for waveform output. This function does:\n 1. Sets sample clock properly.\n 2. Sets triggering.\n 3. Writes data to hardware buffers without auto_start.\n\n waveforms : see gui/plotter/{analog.py,digital.py} for format\n clock_transitions : dictionary of clocks to dict(ignore,transitions)\n t_max : maximum time of waveforms in units of time.\n \"\"\"\n if self.use_case in [None, Task.WAVEFORM_SINGLE, Task.WAVEFORM_CONTINUOUS]:\n if self.use_case is not None:\n debug( 'nidaqmx: stopping task: %s', self.task )\n self.task.stop()\n else:\n self._rebuild_task()\n if continuous:\n self.use_case = Task.WAVEFORM_CONTINUOUS\n else:\n self.use_case = Task.WAVEFORM_SINGLE\n\n if not self.clock_terminal:\n raise UserWarning('cannot start waveform without a output clock defined')\n\n\n my_clock = clock_transitions[ self.config['clock']['value'] ]\n dt_clk = my_clock['dt']\n transitions = list( my_clock['transitions'] )\n transitions.sort()\n\n if self.finite_end_clock and not continuous:\n # This task requires an additional clock pulse at the end of the sequence\n # in order for the hardware to properly notify the software of completion.\n # It is the responsibility of each driver to ensure that the last clock\n # transitions is ignored if the driver has already indicated to arbwave\n # that an extra clock pulse is required.\n transitions = transitions[:-1]\n\n # 1. Sample clock\n if continuous:\n mode = self.config['clock-settings']['mode']['value']\n else:\n mode = 'finite'\n\n clock_rate = self.get_clock_rate()\n min_dt = self.get_min_period()\n\n debug( 'nidaqmx: configuring task timing for waveform output: %s', self.task )\n if rootlog.getEffectiveLevel() <= (DEBUG-1):\n log(DEBUG-1,'self.task.configure_timing_sample_clock('\n 'source' '=%s,'\n 'rate' '=%s Hz,'\n 'active_edge' '=%s,'\n 'sample_mode' '=%s,'\n 'samples_per_channel=%s)',\n self.clock_terminal,\n clock_rate,\n self.config['clock-settings']['edge']['value'],\n mode,\n len(transitions),\n )\n self.task.configure_timing_sample_clock(\n source = self.clock_terminal,\n rate = clock_rate, # Hz\n active_edge = self.config['clock-settings']['edge']['value'],\n sample_mode = mode,\n samples_per_channel = len(transitions) )\n # 2. Triggering\n if not self.trig_sources:\n pass # this device does not accept triggers\n elif self.config['trigger']['enable']['value']:\n debug( 'nidaqmx: configuring task trigger for waveform output: %s', self.task )\n if rootlog.getEffectiveLevel() <= (DEBUG-1):\n log(DEBUG-1, 'self.task.configure_trigger_digital_edge_start('\n 'source=%s,edge=%s)',\n self.config['trigger']['source']['value'],\n self.config['trigger']['edge']['value'],\n )\n self.task.configure_trigger_digital_edge_start(\n source=self.config['trigger']['source']['value'],\n edge=self.config['trigger']['edge']['value'] )\n else:\n debug('nidaqmx: disabling trigger start for task: %s', self.task)\n self.task.configure_trigger_disable_start()\n # 3. Data write\n # 3a. Get data array\n # loop through each transition and accumulate a list of scans for each\n # channel for each transition.\n # probably need to do some rounding to the nearest clock pulse to ensure\n # that we only have pulses matched to the correct transition\n\n px = self.prefix\n chlist = ['{}/{}'.format(px,c) for c in self.task.get_names_of_channels()]\n assert set(chlist).issuperset( waveforms.keys() ), \\\n 'NIDAQmx.set_waveforms: mismatched channels'\n\n # get all the waveform data into the scans array. All remaining None values\n # mean that the prior value for the particular channels(s) should be kept\n # for that scan.\n n_channels = len(chlist)\n scans = dict.fromkeys( transitions )\n nones = [None] * n_channels\n for i in range( n_channels ):\n if chlist[i] not in waveforms:\n continue\n for wf_path, (encoding,group_trans) in waveforms[ chlist[i] ].items():\n assert encoding == 'step', \\\n 'non-step transition encoding for NIDAQmx: '+encoding\n for timestamp, value in group_trans:\n if not scans[timestamp]:\n scans[timestamp] = copy.copy( nones )\n scans[timestamp][i] = value\n\n # for now, if a channel does not have any data for t=0, we'll issue\n # an error and set the empty channel value at t=0 to zero.\n def zero_if_none(v, channel):\n if v is None:\n warn('NIDAQmx: missing starting value for channel (%s)--using 0',\n chlist[channel])\n return 0\n else:\n return v\n\n S0 = scans[ transitions[0] ]\n if S0 is None:\n # must be sharing a clock with another card. init all channels to zero\n last = scans[ transitions[0] ] = [0] * n_channels\n else:\n scans[ transitions[0] ] = [\n zero_if_none(v,i) for v,i in zip( S0, range(len(S0)) )\n ]\n last = scans[ transitions[0] ]\n\n if len(transitions) > 1:\n # NI seems to have problems with only one transition any way, but...\n diff_transitions = np.diff( transitions )\n min_transition = np.argmin( diff_transitions )\n if diff_transitions[min_transition] < round(min_dt/dt_clk):\n raise RuntimeError(\n '{name}: Samples too small for NIDAQmx at t={tl}->{t}: {dt}<({m}/{clk})'\n .format(name=self.name,\n tl=transitions[min_transition],\n t=transitions[min_transition+1],\n dt=diff_transitions[min_transition],\n m=min_dt, clk=dt_clk)\n )\n\n for t in transitions:\n t_array = scans[t]\n if t_array is None:\n # must be sharing a clock with another card. keep last values\n scans[t] = last\n else:\n for i in range( n_channels ):\n if t_array[i] is None:\n t_array[i] = last[i]\n last = t_array\n\n # now, we finally build the actual data to send to the hardware\n scans = [ scans[t] for t in transitions ]\n\n # 3b. Send data to hardware\n debug( 'nidaqmx: writing waveform data for channels: %s', chlist )\n debug( 'nidaqmx: NIDAQmx len(transitions/in) = %s, len(scans/out) = %s',\n len(transitions), len(scans) )\n if rootlog.getEffectiveLevel() <= (DEBUG-1):\n log(DEBUG-1, 'NIDAQmx task.write(, False, group_by_scan_number)' )\n log(DEBUG-1, ':' )\n for scan in scans:\n log(DEBUG-1, ' %s', np.array(scan).astype(float).tolist())\n self.task.write( scans, auto_start=False, layout='group_by_scan_number' )\n self.t_max = t_max\n\n @Pyro4.expose\n def start(self):\n if self.task:\n self.task.start()\n\n @Pyro4.expose\n def wait(self):\n if self.task:\n log(DEBUG-1,'NIDAQmx: waiting for task (%s) to finish...', self.task)\n log(DEBUG-1,'NIDAQmx: already done? %s', self.task.is_done())\n if self.use_case == Task.WAVEFORM_CONTINUOUS:\n raise RuntimeError('Cannot wait for continuous waveform tasks')\n try: self.task.wait_until_done( timeout = self.t_max.coeff*2 )\n except nidaqmx.libnidaqmx.NIDAQmxRuntimeError as e:\n debug('NIDAQmx: task.wait() timed out! finished=%s',\n self.task.is_done())\n log(DEBUG-1,'NIDAQmx: task (%s) finished', self.task)\n # may as well stop the task since we are finished\n self.stop()\n\n @Pyro4.expose\n def stop(self):\n if self.task:\n debug('nidaqmx: stopping task for %s', self)\n self.task.stop()\n\n @Pyro4.expose\n def get_config_template(self):\n C = {\n 'use-only-onboard-memory' : {\n 'value' : True,\n 'type' : bool,\n 'range' : None,\n },\n 'clock' : {\n 'value' : '',\n 'type' : str,\n 'range' : self.clock_sources,\n },\n 'clock-settings' : {\n 'mode' : {\n 'value' : 'continuous',\n 'type' : str,\n 'range' : [\n ('finite', 'Finite'),\n ('continuous', 'Continuous'),\n ('hwtimed', 'HW Timed Single Point'),\n ],\n },\n 'edge' : {\n 'value' : 'rising',\n 'type' : str,\n 'range' : [\n ('falling','Sample on Falling Edge of Trigger'),\n ('rising', 'Sample on Rising Edge of Trigger'),\n ],\n },\n 'Timebase' : {\n 'clock' : {\n 'value' : '', #FIXME: what should the default be? MasterTimebase?\n 'type' : str,\n 'range' : self.SCTB_sources,\n },\n },\n },\n }\n\n if self.trig_sources:\n C.update(\n trigger = {\n 'enable' : {\n 'value' : False,\n 'type' : bool,\n 'range' : None,\n },\n 'source' : {\n 'value' : '',\n 'type' : str,\n 'range' : self.trig_sources,\n },\n 'edge' : {\n 'value' : 'rising',\n 'type' : str,\n 'range' : [\n ('falling','Trigger on Falling Edge of Trigger'),\n ('rising', 'Trigger on Rising Edge of Trigger'),\n ],\n },\n },\n )\n return C\n","sub_path":"python/arbwave/backend/drivers/nidaqmx/task/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":15633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"403478584","text":"def delete_db(user_id, token, post_data, db, list_data):\n email = post_data['email']\n if len(list_data) == 0:\n return {'Error': 'invalid voter_id and token'}\n elif list_data[0]['user_id'] != user_id:\n return {'Error': 'invalid registered user_id'}\n elif list_data[0]['token'] != token:\n return {'Error': 'invalid registered token'}\n elif list_data[0]['role_name'] == 'admin':\n cur = db.cursor()\n query = \"SELECT * FROM crud_table where email = ('\" + str(email) + \"')\"\n cur.execute(query)\n fetch_table = cur.fetchall()\n list_table = []\n for table in fetch_table:\n dict_data = {'name': table[0], 'email': table[1], 'role_type': table[2], 'status': table[3]}\n list_table.append(dict_data)\n if len(list_table) == 0:\n return {'Error': 'invalid email'}\n elif list_table[0]['email'] == email:\n query = \"DELETE FROM crud_table WHERE email = ('\" + str(email) + \"')\"\n cur.execute(query)\n db.commit()\n return {\"User\": 'DELETED successfully'}\n else:\n return {'Error': 'you are not an registered admin to delete'}\n","sub_path":"package_data/delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"103877072","text":"import pygame\n\n# Global constants\n\n# Colors\n\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600\n\npygame.init()\n\ngameDisplay = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\npygame.display.set_caption('Slither')\n\n\ngameExit = False\nlead_x = 300\nlead_y = 300\nlead_x_change = 0\nlead_y_change = 0\nFPS = 30\n\nclock = pygame.time.Clock()\n\nwhile not gameExit:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n gameExit = True\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n lead_x_change -= 2\n lead_y_change = 0\n if event.key == pygame.K_RIGHT:\n lead_x_change += 2\n lead_y_change = 0\n if event.key == pygame.K_UP:\n lead_y_change -= 2\n lead_x_change = 0\n if event.key == pygame.K_DOWN:\n lead_y_change += 2\n lead_x_change = 0\n if lead_x > SCREEN_WIDTH:\n lead_x = 0\n if lead_x < 0:\n lead_x = SCREEN_WIDTH\n if lead_y > SCREEN_HEIGHT:\n lead_y = 0\n if lead_y < 0:\n lead_y = SCREEN_HEIGHT\n\n lead_x += lead_x_change\n lead_y += lead_y_change\n\n gameDisplay.fill(BLUE)\n pygame.draw.rect(gameDisplay, RED, [lead_x, lead_y, 10, 10])\n pygame.display.update()\n\n clock.tick(FPS)\n\npygame.quit()\nquit()\n","sub_path":"src/second.py","file_name":"second.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"643292643","text":"import salem\nimport xarray as xr\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nimport pdb\n\nfpath = '/localscratch/wllf030/cornkle/obs_data/blob_maps_MSG/'\nfile = fpath + 'blob_map_90km_sum_18UTC.nc'\nfile2 = fpath + 'blob_map_30km_sum_18UTC.nc'\nfile3 = fpath + 'blob_map_90km_sum_3UTC.nc'\nfile4 = fpath + 'blob_map_30km_sum_3UTC.nc'\ntpath = '/users/global/cornkle/data/pythonWorkspace/proj_CEH/topo/gtopo_1min_afr.nc'\nspath = '/users/global/cornkle/C_paper/wavelet/figs/paper/'\n\ndiff30 = fpath + 'blob_map_30km_18-3UTRC_diff.nc'\ndiff90 = fpath + 'blob_map_90km_18-3UTRC_diff.nc'\n\nds = xr.open_dataarray(file)\ntop = xr.open_dataarray(tpath)\nds2 = xr.open_dataarray(file2)\nds3 = xr.open_dataarray(file3)\nds4 = xr.open_dataarray(file4)\n\nd30diff = xr.open_dataarray(diff30)\nd90diff = xr.open_dataarray(diff90)\n\n\nds.name = '100k'\nds2.name = '30k'\n\nds = ds.sel(lon=slice(-17.5,20), lat=slice(4.5,20)) # lake chad lon=slice(10,20), lat=slice(10,15)\nds2 = ds2.sel(lon=slice(-17.5,20), lat=slice(4.5,20)) # volta lon=slice(-10,8), lat=slice(4,10)\nds3 = ds3.sel(lon=slice(-17.5,20), lat=slice(4.5,20))\nds4 = ds4.sel(lon=slice(-17.5,20), lat=slice(4.5,20))\ntop = top.sel(lon=slice(-17.5,20), lat=slice(4.5,20))\n\ngrid = ds.salem.grid\nsrtm_on_ds = ds.salem.lookup_transform(top)\n\nds[ds == 0]=np.nan\nds2[ds2 == 0] =np.nan\nds3[ds3 == 0] =np.nan\nds4[ds4 == 0] =np.nan\n\n# ds[srtm_on_ds == 0]=np.nan\n# ds2[srtm_on_ds == 0] =np.nan\n# ds3[srtm_on_ds == 0] =np.nan\n# ds4[srtm_on_ds == 0] =np.nan\n\nperc = ds.quantile(0.99)\nperc2 =ds2.quantile(0.99)\nperc3 =ds3.quantile(0.99)\nperc4 =ds4.quantile(0.99)\n\n# perc = np.max(ds)\n# perc2 =np.max(ds2)\n# perc3 =np.max(ds3)\n# perc4 =np.max(ds4)\n\npercc = np.max([perc,perc3])\npercc1 = np.max([perc2, perc4])\n#\n# ds = (ds-1) / (percc- 1) # dim=['lon']\n# ds2 = (ds2-1) / (percc1- 1)\n# ds3 = (ds3-1) / (percc- 1)\n# ds4 = (ds4-1) / (percc1- 1)\n\n# dsr = (ds-1) / (perc- 1) # dim=['lon']\n# ds2r = (ds2-1) / (perc2- 1)\n# ds3r = (ds3-1) / (perc3- 1)\n# ds4r = (ds4-1) / (perc4- 1)\n#\n# dsr = dsr.where(ds<=1)\n# ds2r = ds2r.where(ds2<=1)\n# ds3r = ds3r.where(ds3<=1)\n# ds4r = ds4r.where(ds4<=1)\n\n# ds[ds.where(ds<=1)]=-999\n# ds2[ds2 > 1]=-999\n# ds3[ds3 > 1]=-999\n# ds4[ds4 > 1]=-999\n\n# fact90 = np.sum(ds3)/np.sum(ds)\n# fact30 = np.sum(ds4)/np.sum(ds2)\n#\n# dsr = ds *fact90\n# ds2r = ds2 * fact30\n# ds3r = ds3\n# ds4r = ds4\n\nmap = ds.salem.get_map(cmap='Greys')\nmap.set_shapefile(rivers=True)\n# read the ocean shapefile (data from http://www.naturalearthdata.com)\noceans = salem.read_shapefile(salem.get_demo_file('ne_50m_ocean.shp'),\n cached=True)\n\nlakes = salem.read_shapefile(salem.get_demo_file('ne_50m_lakes.shp'), cached=True)\nmap.set_shapefile(lakes, edgecolor='k', facecolor='none', linewidth=2,)\n\n\n\n\nf,((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2,figsize = (11,5.5))\n\n\n\nmap.set_plot_params(levels=[-40,-20, -10, 10, 20, 40], cmap='RdBu') #[-40,-20, -10, 10, 20, 40] [-0.6,-0.3, -0.15, 0.15, 0.3,0.6]\n#map.set_plot_params(levels=[-100,-75,-50,-40,-30,30,40,50,75,100], cmap='RdBu')\ndat = (ds.values - ds3.values)-7#/ds.values *100 #(d90diff-7)\n\nmap.set_data(dat)\nmap.set_lonlat_contours(add_ytick_labels=False)\nmap.visualize(ax=ax2, addcbar=False)\n\ndat = (ds2.values - ds4.values)*2-5#/ds2.values *100#(d30diff*2-5)\n\nmap.set_data(dat)\nmap.set_lonlat_contours(add_ytick_labels=True)\nmap.visualize(ax=ax1, addcbar=False) #title='<35km 1800UTC - 0300UTC',\nkw2 = map.get_colorbarbase_kwargs()\n\nfor tick in ax1.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nfor tick in ax2.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nfor tick in ax1.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nfor tick in ax2.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\n\n\nzuse = map.set_topography(top, relief_factor=1.4)\nmap.set_lonlat_contours(add_ytick_labels=True)\nmap.set_plot_params(vmax=2000, cmap='topo')\nmap.set_data(zuse)\nmap.visualize(ax=ax3, addcbar=False) #, title='Topography'\nfor tick in ax3.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nfor tick in ax3.xaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nkw = map.get_colorbarbase_kwargs()\n\nax4.axis('off')\n\n\nfsiz = 14\nx = 0.02\nx2 = 0.48\nplt.annotate('a)', xy=(x, 0.95), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),\n textcoords='offset points')\nplt.annotate('b)', xy=(x2, 0.95), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),\n textcoords='offset points')\nplt.annotate('c)', xy=(x, 0.49), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),\n textcoords='offset points')\n\n\n\nplt.tight_layout()\n\nf.subplots_adjust(right=0.89)\n# cax = f.add_axes([0.95,0.55,0.025,0.4])\n# salem.DataLevels(ds, nlevels=8)\n# #kw1=salem.DataLevels.colorbarbase(cax, **kw1)\n# mpl.colorbar.ColorbarBase(cax, **kw1)\n\ncax = f.add_axes([0.90,0.60,0.017,0.32])\ndl=salem.DataLevels(ds, nlevels=8)\n#kw1=salem.DataLevels.colorbarbase(cax, **kw1)\ncb1 = mpl.colorbar.ColorbarBase(cax, label = 'Nocturnal | Afternoon', **kw2)\n#dl.set_extend(extend='both')\n#cb1.set_ticklabels(['']*6)\n#f.colorbar(cax).set_yticklabels(['','','','','',''])\n\n\ncax = f.add_axes([0.46,0.13,0.017,0.32])\nmpl.colorbar.ColorbarBase(cax, label='m', **kw)\n\nplt.savefig(spath+'/scales_map_abs.png', dpi=300)\n\n\nplt.close('all')\n","sub_path":"wavelet_paper/salem_map_basic_absOnly.py","file_name":"salem_map_basic_absOnly.py","file_ext":"py","file_size_in_byte":5431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"437416893","text":"arr = input()\nn = len(arr)\nbase = [0] * n\nvisited = [False] * n\nresult_arr = []\n\ndef recur(cur, pre):\n if cur == n:\n temp = \"\"\n for i in range(n):\n temp += arr[base[i]]\n\n if temp in result_arr:\n return\n\n result_arr.append(temp)\n return\n\n else:\n for i in range(n):\n if visited[i]:\n continue\n\n if pre == arr[i]:\n continue\n\n visited[i] = True\n base[cur] = i\n recur(cur+1, arr[i])\n visited[i] = False\n\nrecur(0, -1)\nprint(len(result_arr))","sub_path":"week_004/week_004_1342.py","file_name":"week_004_1342.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"620813524","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nimport backpropagation as bp\n\n\ndef gaussian_training_vectors(features, means, sd, dim):\n \"\"\"Make a set of N training feature vectors for M classes in a k dimensional space.\n\n Arguments:\n classes {int} -- how many classes the problem has\n features {int} -- how many training feature vectors you want per mean\n means {dict} -- a dict for each class containing a list of means for the class\n sd {np.array} -- a numpy array of every class' standard deviation with floating entries\n dim {int} -- the dimension of the space and hence the feature vectors\n\n Returns:\n 3D np.array -- the set of training feature vectors in a 3D array\n \"\"\"\n s = 0\n classes = 0\n for element in means:\n s += len(means[element])\n xtr = np.zeros((features * s, dim))\n ytr = np.zeros((features * s,))\n index = 0\n for element in means:\n for mean in range(len(means[element])):\n for _ in range(features):\n ytr[index] = classes\n xtr[index, :] = np.random.multivariate_normal(\n means[element][mean], sd, 1)\n index += 1\n classes += 1\n return xtr, ytr\n\n\ndef logistic(x, a=1):\n \"\"\"Calculate the output from the logistic function.\n\n Arguments:\n x {array or float or int} -- the function parameter as either a numpy array or as int/float\n a {float} -- defaults to 1; a constant describing the slope of the function (larger number gives steeper slope)\n\n Returns:\n array/float -- the value of the logistic function in the same format as the function parameter x\n \"\"\"\n return 1 / (1 + np.exp(-a * x))\n\n\ndef d_logistic(x, a=1):\n \"\"\"Find the derivative of the logistic function.\n\n Arguments:\n x {array or float or int} -- the function parameter as either a numpy array or as int/float\n a {float} -- defaults to 1; a constant describing the slope of the function (larger number gives steeper slope)\n\n Returns:\n array or float -- the value of the total derivative of the logistic function in the same format as the function parameter x\n \"\"\"\n return a * logistic(x, a) * (1 - logistic(x, a))\n\n\ndef prob_4_2():\n \"\"\"Using the computer, generate four 2D Gaussian random sequences with\n Σ=[0.01, 0.0; 0.0, 0.01],\n ω_1:\n μ_1 = [0, 0].T,\n μ_2 = [1, 1].T,\n ω_2:\n μ_3 = [0, 1].T,\n μ_4 = [1, 0].T.\n Produce 100 xtr from each distribution. Use the batch mode backpropagation algorithm (p. 170) of\n sec. 4.6 to train a two-layer perceptron with two hidden neurons and one in the output.\n Let the activation function be the logistic one with\n a = 1.\n Plot the error curve as a function of iteration steps. Experiment yourselves with various\n values of the learning parameter μ. Once the algorithm has converged, produce 50 more\n vectors from each distribution and try to classify them using the weights you have obtained.\n What is the percentage classification error?\n \"\"\"\n features = 100 # number of feature vectors from each mean value/vector\n real_data = 50\n d = 2 # the dimension of the feature space\n means = {'1': [[0, 0], [1, 1]], '2': [[0, 1], [1, 0]]}\n sd = np.array([[0.01, 0.0], [0.0, 0.01]])\n xtr, ytr = gaussian_training_vectors(features, means, sd, d)\n xte, yte = gaussian_training_vectors(real_data, means, sd, d)\n\n L = 2\n\n # epoch_error = np.array([30, 28])\n accuracy = [.5, .5]\n\n nn = bp.NeuralNetwork(L=L, layer_dim=[2, 2, 1], i_num=2)\n\n count = 0\n while accuracy[-1] < 0.96 or np.abs(accuracy[-1] - accuracy[-2]) > 0.01:\n accuracy.append(nn.training(xtr, ytr, 2))\n count += 1\n if count > 10000:\n break\n\n print(f'Number of epochs before convergence: {count}')\n plot_surface(xte, yte, nn, accuracy)\n\n\ndef prob_4_10():\n means = {'1': [[0.4, 0.9], [2.0, 1.8], [2.3, 2.3], [2.6, 1.8]],\n '2': [[1.5, 1.0], [1.9, 1.0], [1.5, 3.0], [3.3, 2.6]]}\n features = 50\n d = 2\n sd = np.array([[0.008, 0.0], [0.0, 0.008]])\n xtr, ytr = gaussian_training_vectors(features, means, sd, d)\n\n L = 3\n layer_dim = [2, 3, 2, 1]\n\n epoch_error = np.array([30, 28])\n accuracy = [.5, .5]\n\n nn = bp.NeuralNetwork(L=L, layer_dim=layer_dim, i_num=2)\n nn.set_error_const(.006)\n\n count = 0\n threshold = 30000\n while accuracy[-1] < 0.96 or np.abs(accuracy[-1] - accuracy[-2]) > 0.01:\n # epoch_error = np.append(epoch_error, nn.training(xtr, ytr, 2)[0])\n accuracy.append(nn.training(xtr, ytr, 2))\n count += 1\n if count > threshold:\n print(f'No convergence after {count} epochs.')\n break\n\n if count < threshold + 1:\n print(f'Number of epochs before convergence: {count}')\n plot_surface(xtr, ytr, nn, accuracy)\n\n\ndef plot_surface(Xtr, Ytr, nn, accuracy):\n plt.figure()\n # plt.subplot(1, 3, 1)\n # plt.semilogy(np.abs(epoch_error))\n plt.subplot(1, 2, 1)\n plt.title('Accuracy')\n plt.plot(accuracy)\n plt.subplot(1, 2, 2)\n plt.title('Classification surface')\n # Generate a grid of datapoints.\n x1min = np.min(Xtr[:, 0])\n x1max = np.max(Xtr[:, 0])\n x1margin = 0.05 * (x1max - x1min)\n\n x2min = np.min(Xtr[:, 1])\n x2max = np.max(Xtr[:, 1])\n x2margin = 0.05 * (x2max - x2min)\n\n x1axis = np.linspace(x1min - x1margin, x1max + x1margin, 200)\n x2axis = np.linspace(x2min - x2margin, x2max + x2margin, 200)\n\n X1grid = np.tile(x1axis, (len(x2axis), 1)).T.reshape(-1, 1)\n X2grid = np.tile(x2axis, (1, len(x1axis))).reshape(-1, 1)\n\n Xgrid = np.concatenate((X1grid, X2grid), axis=1)\n\n f_x = nn.propagate_forward(Xgrid)\n\n # Plot contour\n X1grid = X1grid.reshape(len(x2axis), -1)\n X2grid = X2grid.reshape(-1, len(x1axis))\n f_x = f_x.reshape(len(x2axis), len(x1axis))\n\n # Plot decision boundary and margins\n plt.contour(X1grid, X2grid, f_x, levels=[0], linestyles=(\n 'solid'), linewidths=2, colors='k')\n\n plt.contourf(X1grid, X2grid, f_x, levels=np.linspace(\n np.min(f_x), np.max(f_x), 200), cmap='seismic')\n\n col = np.where(Ytr == 1.0, 'b', 'y')\n plt.scatter(Xtr[:, 0], Xtr[:, 1], c=col)\n\n plt.show()\n\n\nnp.random.seed(0)\n# prob_4_2()\nprob_4_10()\n","sub_path":"neural_network/main38.py","file_name":"main38.py","file_ext":"py","file_size_in_byte":6352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"239798116","text":"#! /usr/local/bin/python3\n\ndef checksum(filename):\n with open(filename) as f:\n sum = 0\n\n for line in f:\n values = line.split('\\t')\n values = list(map(int, values))\n\n for v in values:\n for w in values:\n if v != w and v % w == 0:\n sum += int(v / w) \n break\n\n return sum\n\ndef main():\n result = checksum(\"input.txt\")\n print(\"Checksum: \" + str(result))\n\nif __name__ == \"__main__\":\n main()","sub_path":"day-02/corruption-part-two.py","file_name":"corruption-part-two.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"324061983","text":"# Crash Course Text Project 1 - 3rd Iteration:\n\n''' This file will contain game functions to refactor the code in the program\n to make the main less lengthy and easier to follow the logic of it.\n \n The book discusses that refactoring will simplify the structure of the code\n making it easier to build upon.\n \n The author starts by creating a check_events function that will manage the\n events. This is to simplify run_game and isolate the event MGT loop.\n'''\nimport sys\n\nimport pygame\n\ndef check_events():\n \n ''' Respond to keypresses and mouse events. '''\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n sys.exit()\n \n''' Also creating an update screen function which will clean up the game loop\n by encapsulating the screen updating, the image drawing, and the flip\n to new screen refresh.\n'''\ndef update_screen(ai_settings, screen, ship):\n \n screen.fill(ai_settings.bg_color)\n ship.blitme()\n pygame.display.flip()","sub_path":"Python_Crash_Course_text/Crash_Course_Text_Projects/03_ver_Project_01/game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615597960","text":"import sdp.scripts.load_nstx_exp_ref as nstx_exp\nimport sdp.scripts.FWR2D_NSTX_139047_Postprocess as fwrpp\nimport sdp.plasma.analysis as ana\nimport matplotlib.pyplot as plt\n\nimport pickle\nimport numpy as np\n\nwith open('/p/gkp/lshi/XGC1_NSTX_Case/FullF_XGC_ti191_output/ref_pos.pck','r') as f:\n ref_pos = pickle.load(f)\n\ndne_ana = ana.XGC_Density_Loader('/p/gkp/lshi/XGC1_NSTX_Case/FullF_XGC_ti191_output/dne_file.sav.npz')\n\n\nn_channel = 16\n\n#create the distance matrix, dx[i,j] is the absolute distance between the reflection points of i-th and j-th channel \ndx = np.absolute(np.zeros((n_channel,n_channel))+ref_pos[np.newaxis,:]-ref_pos[:,np.newaxis])\n\n#calculate cross-correlation matrix from synthetic signals\ncc_fwr = fwrpp.pp.Cross_Correlation_by_fft(fwrpp.ref2d_out)\ncc_fwr2 = fwrpp.pp.Cross_Correlation_by_fft(fwrpp.ref2d_amp2_out)\ncc_fwr01 = fwrpp.pp.Cross_Correlation_by_fft(fwrpp.ref2d_amp01_out)\ncc_3d = fwrpp.pp.Cross_Correlation_by_fft(fwrpp.ref3d_out)\n\ncs_fwr = fwrpp.pp.Self_Correlation(fwrpp.ref2d_out)\ncs_fwr2 = fwrpp.pp.Self_Correlation(fwrpp.ref2d_amp2_out)\ncs_fwr01 = fwrpp.pp.Self_Correlation(fwrpp.ref2d_amp01_out)\ncs_3d = fwrpp.pp.Self_Correlation(fwrpp.ref3d_out)\n\nprint('FWR data loaded')\n\n#calculate cross-correlation matrix from experimental signals, note that for our case, the simulated time slice is at t=0.632s, so we choose corresponding experimental data from 0.632-0.640, the total sample number is chosen to be 2000 because larger sample doesn't bring in any difference, since the increased samples are not statistical independent. \n\ncc_exp = nstx_exp.analyser.Cross_Correlation_by_fft(0.632,0.640,8000)\n#cc_exp_short = nstx_exp.analyser.Cross_Correlation_by_fft(0.634,0.6348,8000)\n\n#calculate coherent signal for all channels from NSTX. The result is an 2D array containing time series of coherent signal from all the channels.\n\ncs_exp = nstx_exp.analyser.Coherent_over_time(0.632,0.640,2e-5,1e-4)\nprint('nstx data loaded')\n\n#choose the channel ranges representing top/bottom part of pedestal, and center channels for each region. \ntop_center = 11\ntop_range = [8,12]\n\nbottom_center = 6\nbottom_range = [2,7]\n\n#pick chosen data from whole correlation matrices\n\nfwr_top=[]\nfwr2_top = []\nfwr01_top=[]\nfwr3d_top=[]\nexp_top = []\ndx_top=[]\ndef pick_top():\n global fwr_top,fwr2_top,exp_top,dx_top,fwr01_top,fwr3d_top\n fwr_top = np.absolute(cc_fwr[top_center,top_range[0]:top_range[1]])\n fwr2_top = np.absolute(cc_fwr2[top_center,top_range[0]:top_range[1]])\n fwr01_top = np.absolute(cc_fwr01[top_center,top_range[0]:top_range[1]])\n fwr3d_top = np.absolute(cc_3d[top_center,top_range[0]:top_range[1]])\n exp_top = np.absolute(cc_exp[top_center,top_range[0]:top_range[1]])\n dx_top = dx[top_center,top_range[0]:top_range[1]]\n\npick_top()\n\nfwr_bot=[]\nfwr2_bot=[]\nfwr01_bot = []\nfwr3d_bot = []\nexp_bot=[]\ndx_bot=[]\ndef pick_bottom():\n global fwr_bot,fwr2_bot,fwr01_bot,exp_bot,dx_bot,fwr3d_bot\n fwr_bot = np.absolute(cc_fwr[bottom_center,bottom_range[0]:bottom_range[1]])\n fwr2_bot = np.absolute(cc_fwr2[bottom_center,bottom_range[0]:bottom_range[1]])\n fwr01_bot = np.absolute(cc_fwr01[bottom_center,bottom_range[0]:bottom_range[1]])\n fwr3d_bot = np.absolute(cc_3d[bottom_center,bottom_range[0]:bottom_range[1]])\n exp_bot = np.absolute(cc_exp[bottom_center,bottom_range[0]:bottom_range[1]])\n dx_bot = dx[bottom_center,bottom_range[0]:bottom_range[1]]\n\npick_bottom()\n\n#fitting with gaussian(for bottom) and exponential(for top)\nxmax_t = 0\nxfit_t = 0\nfwr_fit_t = 0\nfwr2_fit_t = 0\nfwr01_fit_t = 0\nfwr3d_fit_t = 0\nexp_fit_t = 0\nfwr_t_a,fwr_t_sa = 0,0\nfwr2_t_a,fwr2_t_sa = 0,0\nfwr01_t_a,fwr01_t_sa = 0,0\nfwr3d_t_a,fwr3d_t_sa = 0,0\nexp_t_a,exp_t_sa = 0,0\n\nxgc_fit_t = 0\nxgc_t_a,xgc_t_sa = 0,0\nx_t,dne_c_t = 0,0\n\ndef fit_top():\n global fwr_t_a,fwr_t_sa,fwr2_t_a,fwr2_t_sa,fwr01_t_a,fwr01_t_sa,fwr3d_t_a,fwr3d_t_sa,exp_t_a,expt_sa,xmax_t,xfit_t,fwr_fit_t,fwr2_fit_t,exp_fit_t,fwr01_fit_t,fwr3d_fit_t,xgc_fit_t,xgc_t_a,xgc_t_sa,x_t,dne_c_t\n fwr_t_a,fwr_t_sa = fwrpp.pp.fitting_cross_correlation(fwr_top,dx_top,'exponential')\n fwr2_t_a,fwr2_t_sa = fwrpp.pp.fitting_cross_correlation(fwr2_top,dx_top,'exponential')\n fwr01_t_a,fwr01_t_sa = fwrpp.pp.fitting_cross_correlation(fwr01_top,dx_top,'exponential')\n fwr3d_t_a,fwr3d_t_sa = fwrpp.pp.fitting_cross_correlation(fwr3d_top,dx_top,'exponential')\n exp_t_a,exp_t_sa = fwrpp.pp.fitting_cross_correlation(exp_top,dx_top,'exponential')\n opt_t,x_t,dne_c_t = dne_ana.density_correlation(ref_pos[top_center],width = ref_pos[top_range[0]]-ref_pos[top_center])\n xgc_t_a,xgc_t_sa = opt_t\n \n xmax_t = 2*np.max((np.abs(fwr_t_a),np.abs(fwr2_t_a),np.abs(exp_t_a)))\n xfit_t = np.linspace(0,xmax_t,500)\n fwr_fit_t = fwrpp.pp.exponential_fit(xfit_t,fwr_t_a)\n fwr2_fit_t = fwrpp.pp.exponential_fit(xfit_t,fwr2_t_a)\n fwr01_fit_t = fwrpp.pp.exponential_fit(xfit_t,fwr01_t_a)\n fwr3d_fit_t = fwrpp.pp.exponential_fit(xfit_t,fwr3d_t_a)\n exp_fit_t = fwrpp.pp.exponential_fit(xfit_t,exp_t_a)\n xgc_fit_t = ana.gaussian_correlation_func(xfit_t,xgc_t_a)\n\nfit_top()\n\nxmax_b = 0\nxfit_b = 0\nfwr_fit_b = 0\nfwr2_fit_b = 0\nfwr01_fit_b = 0\nfwr3d_fit_b = 0\nexp_fit_b = 0\nfwr_b_a,fwr_b_sa = 0,0\nfwr2_b_a,fwr2_b_sa = 0,0\nfwr01_b_a,fwr01_b_sa = 0,0\nfwr3d_b_a,fwr3d_b_sa = 0,0\nexp_b_a,exp_b_sa = 0,0\n\nxgc_fit_b = 0\nxgc_b_a,xgc_b_sa = 0,0\nx_b,dne_c_b = 0,0\n\ndef fit_bot():\n global fwr_b_a,fwr_b_sa,fwr2_b_a,fwr2_b_sa,fwr01_b_a,fwr01_b_sa,fwr3d_b_a,fwr3d_b_sa,exp_b_a,expt_sa,xmax_b,xfit_b,fwr_fit_b,fwr2_fit_b,exp_fit_b,fwr01_fit_b,fwr3d_fit_b,xgc_fit_b,xgc_b_a,xgc_b_sa,x_b,dne_c_b\n fwr_b_a,fwr_b_sa = fwrpp.pp.fitting_cross_correlation(fwr_bot,dx_bot,'gaussian')\n fwr2_b_a,fwr2_b_sa = fwrpp.pp.fitting_cross_correlation(fwr2_bot,dx_bot,'gaussian')\n fwr01_b_a,fwr01_b_sa = fwrpp.pp.fitting_cross_correlation(fwr01_bot,dx_bot,'gaussian')\n fwr3d_b_a,fwr3d_b_sa = fwrpp.pp.fitting_cross_correlation(fwr3d_bot,dx_bot,'gaussian')\n exp_b_a,exp_b_sa = fwrpp.pp.fitting_cross_correlation(exp_bot,dx_bot,'gaussian')\n \n opt_b,x_b,dne_c_b = dne_ana.density_correlation(ref_pos[bottom_center],width = ref_pos[bottom_range[0]]-ref_pos[bottom_center])\n xgc_b_a,xgc_b_sa = opt_b\n \n xmax_b = 2*np.sqrt(np.max((np.abs(fwr_b_a),np.abs(fwr2_b_a),np.abs(exp_b_a))))\n xfit_b = np.linspace(0,xmax_b,500)\n fwr_fit_b = fwrpp.pp.gaussian_fit(xfit_b,fwr_b_a)\n fwr2_fit_b = fwrpp.pp.gaussian_fit(xfit_b,fwr2_b_a)\n fwr01_fit_b = fwrpp.pp.gaussian_fit(xfit_b,fwr01_b_a)\n fwr3d_fit_b = fwrpp.pp.gaussian_fit(xfit_b,fwr3d_b_a)\n exp_fit_b = fwrpp.pp.gaussian_fit(xfit_b,exp_b_a)\n xgc_fit_b = ana.gaussian_correlation_func(xfit_b,xgc_b_a)\nfit_bot()\n\nprint('fitting complete')\nprint('fitting curve ready. call plot() to plot. note that the default region is top, pass \"bottom\" as the argument to plot bottom region. ')\n#plot the data points and curves\n\ntotal_plot = 0\n\n#top data\ndef plot(region = 'top'):\n global total_plot\n #plt.figure()\n #total_plot += 1\n if(region == 'top'):\n plt.title('Cross-Correlation at Upper Pedestal,center_channel at {0:.4}m'.format(ref_pos[top_center]))\n plt.plot(dx_top,exp_top,'bs',label = 'exp data')\n plt.plot(dx_top,fwr_top,'ro',label = 'FWR data amp=1')\n plt.plot(dx_top,fwr2_top,'r^',label = 'FWR data amp=2')\n plt.plot(dx_top,fwr01_top,'r+',label = 'FWR data amp=0.1')\n plt.plot(xfit_t,exp_fit_t,'b-',label = 'exp exponential fit')\n plt.plot(xfit_t,fwr_fit_t,'r--',label = 'FWR fit')\n plt.plot(xfit_t,fwr2_fit_t,'r-.',label = 'FWR amp2 fit')\n plt.plot(xfit_t,fwr01_fit_t,'r:',label = 'FWR amp0.1 fit')\n plt.xlabel('distance from center channel reflection($m$)')\n plt.ylabel('cross-correlation')\n plt.legend(labelspacing = 0.2,prop = {'size':12})\n plt.tight_layout()\n elif(region == 'bottom'):\n plt.title('Cross-Correlation at Lower Pedestal,center_channel at {0:.4}m'.format(ref_pos[bottom_center]))\n plt.plot(dx_bot,exp_bot,'bs',label = 'exp data')\n plt.plot(dx_bot,fwr_bot,'ro',label = 'FWR data amp=1')\n plt.plot(dx_bot,fwr2_bot,'r^',label = 'FWR data amp=2')\n plt.plot(dx_bot,fwr01_bot,'r+',label = 'FWR data amp=0.1')\n plt.plot(xfit_b,exp_fit_b,'b-',label = 'exp gaussian fit')\n plt.plot(xfit_b,fwr_fit_b,'r--',label = 'FWR fit')\n plt.plot(xfit_b,fwr2_fit_b,'r-.',label = 'FWR amp2 fit')\n plt.plot(xfit_b,fwr01_fit_b,'r:',label = 'FWR amp0.1 fit')\n plt.xlabel('distance from center channel reflection($m$)')\n plt.ylabel('cross-correlation')\n plt.legend(labelspacing = 0.2,prop = {'size':12})\n plt.tight_layout()\n elif(region == '2d/3d_top'):\n plt.title('Cross-Correlation at Upper Pedestal,center_channel at {0:.4}m'.format(ref_pos[top_center]))\n plt.plot(dx_top,exp_top,'bs',label = 'exp data')\n plt.plot(dx_top,fwr_top,'ro',label = 'FWR2D data')\n plt.plot(dx_top,fwr3d_top,'r^',label = 'FWR3D data')\n plt.plot(xfit_t,exp_fit_t,'b-',label = 'exp exponential fit')\n plt.plot(xfit_t,fwr_fit_t,'r--',label = 'FWR2D fit')\n plt.plot(xfit_t,fwr3d_fit_t,'r-.',label = 'FWR3D fit')\n plt.xlabel('distance from center channel reflection($m$)')\n plt.ylabel('cross-correlation')\n plt.legend(labelspacing = 0.2,prop = {'size':12})\n plt.tight_layout()\n elif(region =='2d/3d_bot'):\n #plt.title('Cross-Correlation at Lower Pedestal,center_channel at {0:.4}m'.format(ref_pos[bottom_center]))\n plt.plot(dx_bot,exp_bot,'bs',label = 'exp data')\n plt.plot(dx_bot,fwr_bot,'go',label = 'FWR2D data')\n plt.plot(dx_bot,fwr3d_bot,'r^',label = 'FWR3D data')\n plt.plot(xfit_b,exp_fit_b,'b-')\n plt.plot(xfit_b,fwr_fit_b,'g--')\n plt.plot(xfit_b,fwr3d_fit_b,'r-.')\n plt.xlabel('$distance from center channel(mm)$')\n plt.ylabel('$\\gamma$')\n plt.legend(labelspacing = 0.2,prop = {'size':15})\n plt.tight_layout()\n elif(region == '3d_bot'):\n plt.title('2D/3D Cross-Correlation and XGC1 Density Correlation, Lower')\n plt.plot(dx_bot,fwr_bot,'ro',label = '2D')\n plt.plot(dx_bot,fwr3d_bot,'r^',label = '3D')\n plt.plot(x_b,dne_c_b,'bs',label = 'XGC')\n plt.plot(xfit_b,fwr_fit_b,'r-.',label = '2D fit')\n plt.plot(xfit_b,fwr3d_fit_b,'r--',label = '3D fit')\n plt.plot(xfit_b,xgc_fit_b,'b-',label = 'XGC fit')\n plt.xlabel('distance from center channel relfection($m$)')\n plt.ylabel('cross-corelation')\n plt.legend(labelspacing = 0.2,prop = {'size':12})\n plt.tight_layout()\n elif(region == '3d_top'):\n plt.title('2D/3D Cross-Correlation and XGC1 Density Correlation, Upper')\n plt.plot(dx_top,fwr_top,'ro',label = '2D')\n plt.plot(dx_top,fwr3d_top,'r^',label = '3D')\n plt.plot(x_t,dne_c_t,'bs',label = 'XGC')\n plt.plot(xfit_t,fwr_fit_t,'r-.',label = '2D fit')\n plt.plot(xfit_t,fwr3d_fit_t,'r--',label = '3D fit')\n plt.plot(xfit_t,xgc_fit_t,'b-',label = 'XGC fit')\n plt.xlabel('distance from center channel relfection($m$)')\n plt.ylabel('cross-corelation')\n plt.legend(labelspacing = 0.2,prop = {'size':12})\n plt.tight_layout()\n\ndef clear_all():\n global total_plot\n\n for i in range(total_plot):\n plt.close()\n\n\n\n\n# Coherent Signal comparison\n\n\n\n\n\n","sub_path":"src/python3/sdp/scripts/FWR_Postprocess/nstx_multichannel_analysis.py","file_name":"nstx_multichannel_analysis.py","file_ext":"py","file_size_in_byte":11408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"603289828","text":"from Crypto.Hash import MD5\nimport string\n\ns = []\ntarget = '7ecc19e1a0be36ba2c6f05d06b5d3058'\nprintable = string.printable\n\ndef getHash(s):\n return MD5.new(s).hexdigest()\n\ndef recursive(index, length):\n global s, printable\n if (index == length):\n cur = ''.join(s)\n if getHash(cur) == target:\n print(cur)\n exit()\n return\n\n for val in printable:\n s[index] = val\n recursive(index+1, length)\n\ndef solve(length):\n global s\n s = ['']*length\n print('Bruteforcing len=', length)\n recursive(0, length)\n\nfor i in range(1, 10):\n solve(i)\n","sub_path":"AnPham/Root-Me/Cryptanalysis/Hash-MD5/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"49859513","text":"import time\n\nfrom jina import __default_executor__\nfrom jina.helper import random_identity\nfrom jina.logging.predefined import default_logger\nfrom jina.parsers import set_pea_parser\nfrom jina.peapods.peas import BasePea\nfrom jina.peapods.zmq import Zmqlet\nfrom jina.types.message import Message\nfrom jina.types.request import Request\nfrom tests import validate_callback\n\n\nclass MockBasePeaNotRead(BasePea):\n def _post_hook(self, msg: 'Message') -> 'BasePea':\n super()._post_hook(msg)\n assert not msg.request.is_decompressed\n\n\nclass MockBasePeaRead(BasePea):\n def _post_hook(self, msg: 'Message') -> 'BasePea':\n super()._post_hook(msg)\n assert msg.request.is_decompressed\n\n\nargs1 = set_pea_parser().parse_args(\n [\n '--host-in',\n '0.0.0.0',\n '--host-out',\n '0.0.0.0',\n '--socket-in',\n 'PULL_CONNECT',\n '--socket-out',\n 'PUSH_CONNECT',\n '--timeout-ctrl',\n '-1',\n ]\n)\n\nargs2 = set_pea_parser().parse_args(\n [\n '--host-in',\n '0.0.0.0',\n '--host-out',\n '0.0.0.0',\n '--port-in',\n str(args1.port_out),\n '--port-out',\n str(args1.port_in),\n '--socket-in',\n 'PULL_BIND',\n '--socket-out',\n 'PUSH_BIND',\n '--timeout-ctrl',\n '-1',\n ]\n)\n\nargs3 = set_pea_parser().parse_args(\n [\n '--host-in',\n '0.0.0.0',\n '--host-out',\n '0.0.0.0',\n '--port-in',\n str(args1.port_out),\n '--port-out',\n str(args1.port_in),\n '--socket-in',\n 'PULL_BIND',\n '--socket-out',\n 'PUSH_BIND',\n '--uses',\n __default_executor__, # will NOT trigger use\n '--timeout-ctrl',\n '-1',\n ]\n)\n\n\ndef test_read_zmqlet():\n with MockBasePeaRead(args2), Zmqlet(args1, default_logger) as z:\n req = Request()\n req.request_id = random_identity()\n d = req.data.docs.add()\n d.tags['id'] = 2\n msg = Message(None, req, 'tmp', '')\n z.send_message(msg)\n\n\ndef test_not_read_zmqlet():\n with MockBasePeaNotRead(args3), Zmqlet(args1, default_logger) as z:\n req = Request()\n req.request_id = random_identity()\n d = req.data.docs.add()\n d.tags['id'] = 2\n msg = Message(None, req, 'tmp', '')\n z.send_message(msg)\n\n\ndef test_recv_message_zmqlet(mocker):\n zmqlet1 = Zmqlet(args1, default_logger)\n zmqlet2 = Zmqlet(args2, default_logger)\n req = Request()\n req.request_id = random_identity()\n doc = req.data.docs.add()\n doc.tags['id'] = 2\n msg = Message(None, req, 'tmp', '')\n\n def callback(msg_):\n assert msg_.request.docs[0].tags['id'] == msg.request.data.docs[0].tags['id']\n\n mock = mocker.Mock()\n zmqlet1.send_message(msg)\n time.sleep(1)\n zmqlet2.recv_message(mock)\n validate_callback(mock, callback)\n","sub_path":"tests/unit/test_is_read_message.py","file_name":"test_is_read_message.py","file_ext":"py","file_size_in_byte":2893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"531723501","text":"import time, random\r\nimport pyautogui as pgui\r\nimport webbrowser as web\r\n\r\n# insert links to photos here as strings, ie 'https:\\\\image-location.com'\r\nlinks = []\r\n\r\n# make sure path is set to chrome, or change to preferred browser\r\nbrowserPath = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'\r\n\r\nwhile True:\r\n # shuffles list of links each time through to give a new order. remove this for static list\r\n random.shuffle(links)\r\n for i in range(len(links)):\r\n time.sleep(20) #change to 15\r\n web.get(browserPath).open(links[i])\r\n pgui.hotkey('ctrlleft', 'tab')\r\n pgui.hotkey('ctrlleft', 'w')","sub_path":"randomizedLinkOpener.py","file_name":"randomizedLinkOpener.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"501152652","text":"from xgboost import XGBClassifier, XGBRegressor\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.metrics import r2_score, accuracy_score\nimport pickle\nimport time\n\nx, y=load_iris(return_X_y=True)\n\nx_train, x_test, y_train, y_test=train_test_split(x, y, train_size=0.8)\n\nmodel=XGBClassifier(n_jobs=-1)\nmodel.fit(x_train, y_train)\nscore=model.score(x_test, y_test)\n\nprint('score :', score)\n\nthresholds=np.sort(model.feature_importances_)\nprint(thresholds)\nstart1=time.time()\n\nfor thresh in thresholds :\n selection=SelectFromModel(model, threshold=thresh, prefit=True)\n \n select_x_train=selection.transform(x_train)\n \n selection_model=XGBClassifier(n_jobs=-1)\n selection_model.fit(select_x_train, y_train)\n\n selec_x_test=selection.transform(x_test)\n y_predict=selection_model.predict(selec_x_test)\n\n score=r2_score(y_test, y_predict)\n\n print(\"Thresh=%.3f, n=%d, R2: %.2f%%\" %(thresh, select_x_train.shape[1], score*100.0))\n pickle.save(open(\"./save/xbg_save/iris.pickle.dat\", \"wb\"))\n\nend1=time.time() - start1\nprint(\"잡스 걸린 시간 : \", end1)\n\nprint(model.feature_importances_)\n\n\nmodel2=pickle.load(open(\"./save/xbg_save/iris.pickle.dat\", \"rb\"))\nprint(\"로드 완료\")\nacc2=model2.score(x_test, y_test)\nprint(\"acc : \", acc2)","sub_path":"Study/ml/m37_2_SFM_save_iris.py","file_name":"m37_2_SFM_save_iris.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"430604134","text":"# !/usr/bin/env python\r\n# -- coding: utf-8 --\r\n# @Time : 2020/6/12 13:01\r\n# @Author : liumin\r\n# @File : optimizer.py\r\nfrom copy import deepcopy\r\nimport torch\r\nimport torch.optim as optim\r\n\r\ndef parser_optimizer(cfg, model):\r\n # params = [p for p in model.parameters() if p.requires_grad]\r\n _params = []\r\n # filter(lambda p: p.requires_grad, model.parameters())\r\n for n, p in dict(model.named_parameters()).items():\r\n if p.requires_grad:\r\n _args = deepcopy(cfg.OPTIMIZER.BIAS_PARAMS if \"bias\" in n else cfg.OPTIMIZER.WEIGHT_PARAMS)\r\n _args.pop(\"data\")\r\n _params += [{\"params\": [p], \"lr\": cfg.INIT_LR, **_args}]\r\n if \"bias\" in n:\r\n _params[-1][\"lr\"] *= cfg.OPTIMIZER.BIAS_LR_MULTIPLIER or 1.0\r\n\r\n if cfg.OPTIMIZER.TYPE == \"SGD\":\r\n optimizer = optim.SGD(_params)\r\n elif cfg.OPTIMIZER.TYPE == \"Adam\":\r\n optimizer = optim.Adam(_params)\r\n elif cfg.OPTIMIZER.TYPE == 'RMSprop':\r\n optimizer = optim.RMSprop(_params)\r\n else:\r\n raise ValueError(\"Unsupported optimizer type: {}\".format(cfg.OPTIMIZER.TYPE))\r\n\r\n return optimizer\r\n","sub_path":"src/optimizers/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"105772297","text":"\"\"\"\nNumerical methods for solving diff eqs.\n\"\"\"\n\n\ndef euler_step(f, x0, y0, h):\n \"\"\"\n Perform a step of Euler's method.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n h: the step size.\n\n Returns:\n A tuple (x1, y1).\n \"\"\"\n slope = f(x0, y0)\n return x0 + h, y0 + h * slope\n\n\ndef rk2_step(f, x0, y0, h):\n \"\"\"\n Perform a step of rk2.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n h: the step size.\n\n Returns:\n A tuple (x1, y1).\n \"\"\"\n slope1 = f(x0, y0)\n slope2 = f(x0 + h, y0 + h * slope1)\n slope = (slope1 + slope2) / 2\n return x0 + h, y0 + h * slope\n\n\ndef rk3_step(f, x0, y0, h):\n \"\"\"\n Perform a step of RK3.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n h: the step size.\n\n Returns:\n A tuple (x1, y1).\n \"\"\"\n # https://people.revoledu.com/kardi/tutorial/ODE/Runge%20Kutta%203.htm\n k1 = f(x0, y0)\n k2 = f(x0 + 0.5 * h, y0 + 0.5 * h * k1)\n k3 = f(x0 + h, y0 - k1 * h + 2 * k2 * h)\n return x0 + h, y0 + h * (k1 + 4 * k2 + k3) / 6.0\n\n\ndef third_order_step(f, x0, y0, h):\n \"\"\"\n Perform a step of a third-order method I invented.\n\n See derivations/third_order.jpg.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n h: the step size.\n\n Returns:\n A tuple (x1, y1).\n \"\"\"\n a = 1.0 / 4.0\n b = 3.0 / 4.0\n c = 2.0 / 3.0\n # Get the exact initial slope.\n slope1 = f(x0, y0)\n # Use RK2 to get f(ch) + O(h^3)\n slope2 = f(x0 + c * h, y0 + c * h * slope1)\n slope3 = (slope1 + slope2) / 2\n # Get the slope at (ch, f(ch) + O(h^3)),\n # which is f'(ch) + O(h^3).\n slope4 = f(x0 + c * h, y0 + c * h * slope3)\n # Compute f(h) + O(h^4).\n return x0 + h, y0 + h * (a * slope1 + b * slope4)\n\n\ndef fourth_order_step(f, x0, y0, h):\n \"\"\"\n Perform a step of a fourth-order method I invented.\n\n See derivations/fourth_order_*.jpg.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n h: the step size.\n\n Returns:\n A tuple (x1, y1).\n \"\"\"\n third_a = 1.0 / 4.0\n third_b = 3.0 / 4.0\n third_c = 2.0 / 3.0\n\n fourth_a = 1.0 / 10.0\n fourth_b = 2.0 / 5.0\n fourth_c = 1.0 / 2.0\n fourth_d = 1.0 / 3.0\n fourth_e = 5.0 / 6.0\n\n # Get information needed for RK2.\n slope1 = f(x0, y0)\n slope2 = f(x0 + h, y0 + h * slope1)\n\n def second_order(step):\n b = (step ** 2) / 2.0\n a = step - b\n slope = a * slope1 + b * slope2\n return f(x0 + h * step, y0 + h * slope)\n\n def third_order(step):\n sub_step = third_c * step\n tmp_slope = second_order(sub_step)\n return f(x0 + h * step, y0 + h * step * (third_a * slope1 + third_b * tmp_slope))\n\n df_eh = third_order(fourth_e)\n df_dh = third_order(fourth_d)\n\n return x0 + h, y0 + h * (fourth_a * slope1 + fourth_b * df_eh + fourth_c * df_dh)\n\n\ndef numerical_solve(f, x0, y0, x1, h=0.01, step_fn=rk3_step):\n \"\"\"\n Numerically solve a differential equation.\n\n Args:\n f: the slope as a function of x, y.\n x0: the start x.\n y0: the start y.\n x1: the x to run until.\n h: the step size.\n step_fn: a function to take one step of the\n algorithm.\n\n Returns:\n An iterator of (x, y) tuples along the curve.\n \"\"\"\n x = x0\n y = y0\n yield (x, y)\n while x < x1:\n x, y = step_fn(f, x, y, h)\n yield (x, y)\n","sub_path":"diffeq/numerical.py","file_name":"numerical.py","file_ext":"py","file_size_in_byte":3660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"185722723","text":"import re\r\nitems = [1, 5, 9, 24]\r\na = int(input(\"Search for a number ?\"))\r\nfor i in range(6):\r\n if a == items[i]:\r\n print(\"Found it! Position:\",i+1 )\r\n break\r\n else:\r\n print(\"Bruh,no !\")\r\n break\r\n\r\nprint(\"Sum is:\",sum(items))\r\nb = 0\r\nfor i in range(6):\r\n b=b + items[i]\r\nprint(b)\r\n\r\nc = input(\"Enter numbers, seperated by a space: \")\r\nsplit = c.split(\" \")\r\nd = 0\r\nfor n in split:\r\n d = d + int(n)\r\nprint(d)","sub_path":"minihack3.py","file_name":"minihack3.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"535833378","text":"from typing import List, Tuple, Dict\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom tqdm import tqdm\nfrom torch import Tensor\nfrom torch import nn, optim\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom pathlib import Path\nimport cvae\nimport cgan\nimport utils\nimport dataloader\nimport scripts\nimport matplotlib.pyplot as plt\n\nimport os\nimport time\nimport random\nimport logging\nfrom logging import handlers\n\nlogging.info(torch.__version__)\n\ncfg = {\n 'data_path': '/hd/yx/WSY/UCY/students03/',\n 'model_params': {\n 'model_cnn': \"resnet18\",\n 'scene_weight': 640,\n 'scene_height': 480,\n 'history_num_frames': 8,\n 'history_delta_time': 0.25,\n 'future_num_frames': 12,\n 'model_name': \"CVGN\",\n 'lr_g': 1e-3,\n 'lr_d': 1e-4,\n 'checkpoint_path': '',\n 'train': True,\n 'predict': True,\n },\n 'train_data_loader': {\n 'batch_size': 40,\n 'shuffle': True,\n 'num_workers': 4,\n },\n 'valid_data_loader': {\n 'batch_size': 40,\n 'shuffle': True,\n 'num_workers': 4,\n },\n 'test_data_loader': {\n 'batch_size': 40,\n 'shuffle': False,\n 'num_workers': 4,\n 'sample_nums': 20,\n },\n 'train_params': {\n 'device': 1,\n 'epoch': 2000,\n 'checkpoint_steps': 100,\n 'valid_steps': 2,\n 'log_file_path': '../../log/train_log/cvgn_univ.log',\n 'tensorboard_path': '../../log/tensorboard/cvgn_univ/',\n 'omega': 0.0,\n 'epsilon': 1.0,\n }\n}\n\n\n# 生成器训练\ndef forward_g(scene, his_traj, targets, model_g, model_d, optimizer, scheduler, omega=1.0, epsilon=1.0):\n\n model_g.train()\n preds, conf, context, z_mean, z_var = model_g(scene, his_traj)\n traj_fake = utils.multi2single(preds, targets, conf, mode='best')\n score_fake = model_d(traj_fake.permute(1, 0, 2), context)\n # 判别loss + nll_loss + vae_loss\n g_loss = utils.g_loss(score_fake)\n # nll_loss = utils.pytorch_neg_multi_log_likelihood_batch(targets, preds, conf)\n vae_loss, ade_loss = cvae.loss_cvae(targets, preds, conf, z_mean, z_var)\n # loss = g_loss + nll_loss * omega + vae_loss * epsilon\n # loss = g_loss + nll_loss * omega + vae_loss * epsilon\n loss = g_loss + vae_loss * epsilon\n scheduler.step()\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n return loss, vae_loss, ade_loss, preds, conf\n\n\n# 判别器训练\ndef forward_d(scene, his_traj, targets, model_g, model_d, optimizer, scheduler):\n\n model_d.train()\n preds, confidences, context, _, _ = model_g(scene, his_traj)\n traj_fake = utils.multi2single(preds, targets, confidences, mode='best')\n score_fake = model_d(traj_fake.permute(1, 0, 2), context)\n score_real = model_d(targets.permute(1, 0, 2), context)\n loss = utils.d_loss(score_real, score_fake)\n scheduler.step()\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n return loss\n\n\nif __name__ == '__main__':\n # 训练日志\n logfile = cfg['train_params']['log_file_path']\n logger = logging.getLogger(logfile)\n logger.setLevel(level=logging.INFO)\n sh = logging.StreamHandler() # 往屏幕上输出\n th = handlers.TimedRotatingFileHandler(filename=logfile, when='D', encoding='utf-8')\n # logger.addHandler(sh) # 把对象加到logger里\n logger.addHandler(th)\n\n # 加载数据集，准备device\n DIR_INPUT = cfg[\"data_path\"]\n\n train_cfg = cfg[\"train_data_loader\"]\n train_dataset = dataloader.EthSceneDataset(os.path.join(cfg['data_path'], 'train'))\n train_dataloader = DataLoader(train_dataset, shuffle=train_cfg[\"shuffle\"],\n batch_size=train_cfg[\"batch_size\"], num_workers=train_cfg[\"num_workers\"],\n drop_last=True)\n\n valid_cfg = cfg[\"valid_data_loader\"]\n valid_dataset = dataloader.EthSceneDataset(os.path.join(cfg['data_path'], 'valid'))\n valid_dataloader = DataLoader(valid_dataset, shuffle=valid_cfg[\"shuffle\"], batch_size=valid_cfg[\"batch_size\"],\n num_workers=valid_cfg[\"num_workers\"], drop_last=True)\n\n test_cfg = cfg[\"test_data_loader\"]\n test_dataset = dataloader.EthSceneDataset(os.path.join(cfg['data_path'], 'test'))\n test_dataloader = DataLoader(test_dataset, shuffle=test_cfg[\"shuffle\"], batch_size=test_cfg[\"batch_size\"],\n num_workers=test_cfg[\"num_workers\"], drop_last=True)\n\n # world frame to camera frame 3*3单应性矩阵\n h_matrix = np.genfromtxt(DIR_INPUT + 'H.txt')\n\n device = cfg['train_params']['device']\n torch.cuda.set_device(device)\n\n tensorboard_file = cfg['train_params']['tensorboard_path']\n train_writer = SummaryWriter(tensorboard_file)\n\n # 建立模型\n generator = cvae.CVAE(cnn_model=cfg[\"model_params\"][\"model_cnn\"],\n channels=3, cont_dim=256, v_dim=2)\n discriminator = cgan.discriminator(h_dim=256, cont_dim=256)\n # load weight if there is a pretrained model\n checkpoint_path = cfg[\"model_params\"][\"checkpoint_path\"]\n if checkpoint_path:\n checkpoint = torch.load(checkpoint_path)\n generator.load_state_dict(checkpoint['model_state_dict_g'])\n discriminator.load_state_dict(checkpoint['model_state_dict_d'])\n logger.info(checkpoint_path, \"loaded\")\n generator.cuda()\n discriminator.cuda()\n\n learning_rate_g = cfg[\"model_params\"][\"lr_g\"]\n learning_rate_d = cfg[\"model_params\"][\"lr_d\"]\n optimizer_g = optim.Adam(generator.parameters(), lr=learning_rate_g)\n optimizer_d = optim.Adam(discriminator.parameters(), lr=learning_rate_d)\n\n scheduler_g = optim.lr_scheduler.StepLR(optimizer_g, step_size=3000, gamma=0.5)\n scheduler_d = optim.lr_scheduler.StepLR(optimizer_d, step_size=5000, gamma=0.8)\n logger.info(f'device {device}')\n torch.backends.cudnn.benchmark = True\n\n # train\n if cfg[\"model_params\"][\"train\"]:\n tr_it = iter(train_dataloader)\n tr_it_valid = iter(valid_dataloader)\n epochs = cfg[\"train_params\"][\"epoch\"]\n progress_bar = tqdm(range(1, len(train_dataloader)), mininterval=5.)\n losses_d = []\n losses_g = []\n model_name = cfg[\"model_params\"][\"model_name\"]\n checkpoint_steps = cfg['train_params']['checkpoint_steps']\n valid_steps = cfg['train_params']['valid_steps']\n t_start = time.time()\n torch.set_grad_enabled(True)\n best_valid = [0., 0.]\n omega = cfg['train_params']['omega']\n epsilon = cfg['train_params']['epsilon']\n i = 0\n for epoch_i in range(epochs):\n for _ in progress_bar:\n try:\n data = next(tr_it)\n except StopIteration:\n tr_it = iter(train_dataloader)\n data = next(tr_it)\n scene = data[0].to(device)\n scene = scene.permute(0, 3, 2, 1)\n his_traj = data[3].to(device)\n his_traj = his_traj.permute(1, 0, 2)\n targets = data[4].to(device)\n\n # 判别器训练\n loss_d = forward_d(scene.float(), his_traj.float(), targets.float(),\n generator, discriminator, optimizer_d, scheduler_d)\n loss_d = loss_d.item()\n losses_d.append(loss_d)\n train_writer.add_scalar('train/loss_d', loss_d, i)\n\n # 生成器训练\n loss_g, loss_vae, loss_ade, preds, confidences = forward_g(scene.float(), his_traj.float(),\n targets.float(), generator, discriminator,\n optimizer_g, scheduler_g,\n omega=omega, epsilon=epsilon)\n loss_g = loss_g.item()\n losses_g.append(loss_g)\n train_writer.add_scalar('train/loss_g', loss_g, i)\n # loss_nll = loss_nll.item()\n # train_writer.add_scalar('train_metrics/loss_nll', loss_nll, i)\n loss_vae = loss_vae.item()\n train_writer.add_scalar('train_metrics/loss_vae', loss_vae, i)\n loss_ade = loss_ade.item()\n train_writer.add_scalar('train_metrics/loss_ade', loss_ade, i)\n\n i += 1\n\n if i % checkpoint_steps == 0:\n mean_losses_d = np.mean(losses_d)\n losses_d = []\n train_writer.add_scalar('train_mean/mean_losses_d', mean_losses_d, i)\n mean_losses_g = np.mean(losses_g)\n losses_g = []\n train_writer.add_scalar('train_mean/mean_losses_g', mean_losses_g, i)\n\n timespent = (time.time() - t_start) / 60\n curr_lr_g = optimizer_g.param_groups[0]['lr']\n curr_lr_d = optimizer_d.param_groups[0]['lr']\n train_writer.add_scalar('lr/lr_g', curr_lr_g, i)\n train_writer.add_scalar('lr/lr_d', curr_lr_d, i)\n logger.info('epoch: {}, i: {}, loss_g(avg): {},'\n 'loss_d(avg): {}, time(min):{}'.format(epoch_i, i,\n mean_losses_g, mean_losses_d, timespent))\n\n # valid\n if epoch_i % valid_steps == 0:\n generator.eval()\n valid_ades = []\n valid_fdes = []\n valid_losses = []\n torch.set_grad_enabled(False)\n valid_progress_bar = tqdm(range(1, len(valid_dataloader)), mininterval=5.)\n for j in valid_progress_bar:\n try:\n data_valid = next(tr_it_valid)\n except StopIteration:\n tr_it_valid = iter(valid_dataloader)\n data_valid = next(tr_it_valid)\n scene_valid = data_valid[0].to(device)\n scene_valid = scene_valid.permute(0, 3, 2, 1)\n his_traj_valid = data_valid[3].to(device)\n his_traj_valid = his_traj_valid.permute(1, 0, 2)\n targets_valid = data_valid[4].to(device)\n pred_pixel, conf, context, z_mean, z_var = generator(scene_valid.float(), his_traj_valid.float())\n traj_fake_valid = utils.multi2single(pred_pixel, targets_valid.float(), conf, mode='best')\n score_fake = discriminator(traj_fake_valid.permute(1, 0, 2), context)\n g_loss_valid = utils.g_loss(score_fake)\n # nll_loss_valid = utils.pytorch_neg_multi_log_likelihood_batch(targets_valid, pred_pixel, conf)\n vae_loss_valid, min_l2_loss_valid = cvae.loss_cvae(targets_valid, pred_pixel, conf, z_mean, z_var)\n # loss = g_loss + nll_loss * omega + l2_loss * epsilon\n # valid_loss = g_loss_valid + nll_loss_valid * omega + vae_loss_valid * epsilon\n valid_loss = g_loss_valid + vae_loss_valid * epsilon\n # camera frame to world frame(meter)\n pred = torch.zeros_like(pred_pixel)\n for batch_index in range(pred_pixel.shape[0]):\n for modality in range(pred_pixel.shape[1]):\n for pos_index in range(pred_pixel.shape[2]):\n pred[batch_index][modality][pos_index] = torch.from_numpy(scripts.project(h_matrix,\n pred_pixel[batch_index][modality][pos_index].cpu()))\n # calculate metrics in world frame\n valid_ade = utils._average_displacement_error(data_valid[2].to(device), pred, conf, mode='best')\n valid_fde = utils._final_displacement_error(data_valid[2].to(device), pred, conf, mode='best')\n valid_loss = valid_loss.item()\n valid_ade = valid_ade.item()\n valid_fde = valid_fde.item()\n valid_losses.append(valid_loss)\n valid_ades.append(valid_ade)\n valid_fdes.append(valid_fde)\n mean_loss_valid = np.mean(valid_losses)\n mean_ade_valid = np.mean(valid_ades)\n mean_fde_valid = np.mean(valid_fdes)\n # 仅在模型提升时更新checkpoint\n if mean_ade_valid 0, \"nx must be positive\"\n\t\n\tu_initial = numpy.zeros((3,nx))\n\trho_L, vel_L, p_L = params[:3]\n\trho_R, vel_R, p_R = params[3:]\n\t\n\tdx = (b-a)/(nx-1)\n\ti_barrier = numpy.floor((barrier-a)/dx)\n\t\n\tu_initial[:, :i_barrier] = numpy.array([rho_L, rho_L*vel_L, rho_L*(p_L/((gamma-1)*rho_L) + 0.5*vel_L**2)]).reshape((3,1))\n\tu_initial[:, i_barrier:] = numpy.array([rho_R, rho_R*vel_R, rho_R*(p_R/((gamma-1)*rho_R) + 0.5*vel_R**2)]).reshape((3,1))\n\n\treturn u_initial\n\ndef compute_f(u):\n\t\"\"\"\n\tCompute the vector function f(u)\n\t\n\tParameters\n\t----------\n\tu\t: array of floats\n\t\t\t3 by n array of values of rho, rho*vel, rho*e_T\n\t\t\t\n\tReturns\n\t-------\n\tf\t\t: array of floats\n\t\t\t3 by n array with columns [rho*vel, rho*vel**2 + p, (rho*e_T + p)*vel]\n\t\t\twhere e_T = p/((gamma-1)*rho) + 0.5*vel**2\t\n\t\"\"\"\n\t\n\tf = numpy.zeros_like(u)\n\tp = (gamma - 1)*(u[2,:] - 0.5*(u[1,:]**2)/u[0,:])\n\tf[0,:] = u[1,:]\n\tf[1,:] = (u[1,:]**2)/u[0,:] + p\n\tf[2,:] = (u[1,:]/u[0,:])*(u[2,:] + p)\n\t\n\treturn f\n\t\ndef richtmyer(u, nt, dt, dx):\n\t\"\"\"\n\tComputes the solution to the 1D shock equation using the Richtmyer method\n\t\n\tParameters\n\t---------\n\tu\t\t: array of floats\n\t\t\t3 by nx array of initial values of [rho, u, p]'\n\tnt\t\t: int\n\t\t\tnumber of timesteps to compute over\n\tdt\t\t: float\n\t\t\ttime stepsize\n\tnx\t\t: int\n\t\t\tn\n\t\t\t\n\tReturns\n\t-------\n\tu_1, u2, u3 : arrays of floats\n\t\t\tnt by u_initial.shape[1] array of values of rho, u, p at each time and spatial value\n\t\"\"\"\n\n\tnx = u.shape[1]\n\t\n\tu1 = numpy.zeros((nt,nx))\n\tu2 = numpy.zeros((nt,nx))\n\tu3 = numpy.zeros((nt,nx))\n\tu1[:,:] = u[0,:].copy()\n\tu2[:,:] = u[1,:].copy()\n\tu3[:,:] = u[2,:].copy()\n\t\n\tf_n = compute_f(u)\n\tu_half = numpy.zeros((3,nx-1))\n\tf_half = numpy.zeros((3,nx-1))\n\tu_n = numpy.zeros_like(u)\n\tu_n[:,:] = u.copy()\n\t\n\tfor t in range(1,nt):\n\t\tu_half[:,:] = 0.5*(u[:,1:] + u[:,:-1]) - dt/(2.0*dx)*(f_n[:,1:] - f_n[:,:-1])\n\t\tf_half = compute_f(u_half)\n\t\tu_n[:,1:-1] = u[:,1:-1] - (dt/dx)*(f_half[:,1:] - f_half[:,:-1])\n\t\tu[:,:] = u_n.copy()\n\t\tf_n = compute_f(u_n)\n\t\tu1[t,:] = u[0,:].copy()\n\t\tu2[t,:] = u[1,:].copy()\n\t\tu3[t,:] = u[2,:].copy()\n\t\t\n\treturn u1,u2,u3\t\n\t\t \n\t\t\ndef main():\n\ta = -10.0\n\tb = 10.0\n\tbarrier = 0.0\n\tparams = numpy.array([1.,0.,100000., 0.125, 0., 10000.])\n\tdt = 0.0002\n\tnt = 51\n\tnx = 81\n\tdx = (b-a)/(nx-1)\n\n\t\n\t#compute index at x = 2.5 meters\n\ti = 5*nx/8\n\t\n\tu_initial = compute_u_i(a, b, barrier, nx, params)\n\tu1,u2,u3 = richtmyer(u_initial, nt, dt, dx)\n\n\tfig = pyplot.figure()\n\tax = pyplot.axes(xlim=(a,b), ylim=(0,5),xlabel=('Position'),ylabel=('Density'))\n\tline, = ax.plot([],[],color='#003366', lw=2)\n\n\tdef animate(data):\n\t\tx = numpy.linspace(a,b,nx)\n\t\ty = data\n\t\tline.set_data(x,y)\n\t\treturn line,\n\n\tanim = animation.FuncAnimation(fig, animate, frames=u1, interval=50)\n\tpyplot.show()\n\n\trho = u1[-1,i]\n\tvel = u2[-1,i]/rho\n\tpressure = (gamma-1)*(u3[-1,i] - 0.5*rho*vel**2)\n\tprint(vel)\n\tprint(pressure)\n\tprint(rho)\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"module3/sods.py","file_name":"sods.py","file_ext":"py","file_size_in_byte":3548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"488395363","text":"import sys\nsys.stdin = open(\"문제1_input.txt\")\n\ndef baby_gin(num_lst):\n cnt = 0\n number = [0,0,0,0,0,0,0,0,0,0]\n # num_lst를 순회하면서\n for i in num_lst:\n # 해당 값을 1회 올린다.\n number[i] += 1\n # 모든 카운팅이 끝나고 난 후\n run = 0\n triplet = 0\n # number리스트를 순회하면서\n for j in number:\n # 트리플렛부터 먼저 검사한다\n if j == 3:\n triplet += 1\n cnt = 0\n # run을 검사한다\n # 만약 j번째 카드가 1개 있다면\n elif j == 1:\n # 카운트 횟수를 1회 올려준다.\n cnt += 1\n # 만약 카운트 횟수가 3에 도달했다면\n if cnt == 3:\n # run횟수를 1회 올려주고\n run += 1\n # 카운트 횟수를 초기화한다.\n cnt = 0\n # 연속되지 않았다면\n else:\n # 카운트 횟수를 0으로 해준다.\n cnt = 0\n\n # 베이비진�� 모든 경우의 수\n # run이 2회인 경우\n if run == 2:\n return 1\n # run이 1회이고 triplet이 1회인 경우\n elif run == 1 and triplet == 1:\n return 1\n # triplet이 2회인 경우\n elif triplet == 2:\n return 1\n else:\n return 0\n\n\n\n\n\nfor tc in range(1,int(input())+1):\n num = input()\n num_lst = list()\n for i in range(len(num)):\n num_lst.append(int(num[i]))\n num_lst.sort()\n print(\"#{} {}\".format(tc, baby_gin(num_lst)))","sub_path":"시험전용폴더/Algo1_구미_2반_최준성.py","file_name":"Algo1_구미_2반_최준성.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"238773757","text":"'''\nYou are given an array routes representing bus routes where routes[i] is a bus route that the ith bus repeats forever.\n\nFor example, if routes[0] = [1, 5, 7], this means that the 0th bus travels in the sequence 1 -> 5 -> 7 -> 1 -> 5 -> 7 -> 1 -> ... forever.\nYou will start at the bus stop source (You are not on any bus initially), and you want to go to the bus stop target. You can travel between bus stops by buses only.\n\nReturn the least number of buses you must take to travel from source to target. Return -1 if it is not possible.\n\n \n\nExample 1:\n\nInput: routes = [[1,2,7],[3,6,7]], source = 1, target = 6\nOutput: 2\nExplanation: The best strategy is take the first bus to the bus stop 7, then take the second bus to the bus stop 6.\nExample 2:\n\nInput: routes = [[7,12],[4,5,15],[6],[15,19],[9,12,13]], source = 15, target = 12\nOutput: -1\n \n\nConstraints:\n\n1 <= routes.length <= 500.\n1 <= routes[i].length <= 105\nAll the values of routes[i] are unique.\nsum(routes[i].length) <= 105\n0 <= routes[i][j] < 106\n0 <= source, target < 106\n\n'''\n\nclass Solution:\n def numBusesToDestination(self, routes: List[List[int]], source: int, target: int) -> int:\n if target == source:\n return 0\n \n stops = {}\n times = -1\n \n for bus in range(len(routes)):\n for stop in routes[bus]:\n if not stop in stops:\n stops[stop] = [bus]\n else:\n stops[stop].append(bus)\n\n for bus in stops[source]:\n queue = deque()\n queue.append((bus, 1))\n visited = set()\n \n while queue:\n bus, cnt = queue.pop()\n if bus in visited:\n continue\n else:\n visited.add(bus)\n if target in routes[bus]:\n if times < 0:\n times = cnt\n else:\n times = min(times, cnt)\n else:\n cnt += 1\n for i in routes[bus]:\n for j in stops[i]:\n queue.appendleft((j, cnt))\n \n return times\n \n \n \n \n ","sub_path":"Python3/815. Bus Routes.py","file_name":"815. Bus Routes.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"321298132","text":"# -----------------------------------------------------------\n# Fit and evaluate a set of models\n# -----------------------------------------------------------\n\nimport argparse as arg\nfrom src.models.fitting import ModelFitter\nfrom sklearn.dummy import DummyClassifier # dummy classification to be used as a benchmark\nfrom sklearn.naive_bayes import MultinomialNB # Naive Bayes\nfrom sklearn.linear_model import LogisticRegression # Logistic Regression\n\n\ndef main(train_prop, min_doc_freq):\n\n # definitions of the models we want to run (going forward we may want to add some grid search stuff)\n target_models = {\n 'DummyClassifier': DummyClassifier(random_state=1),\n 'MultinomialNB': MultinomialNB(alpha=0.1),\n 'LogisticRegression': LogisticRegression()\n }\n\n model_fitter = ModelFitter(train_prop=train_prop, min_doc_freq=min_doc_freq, defined_models=target_models)\n model_fitter.evaluate_models()\n\n for model, score in model_fitter.evaluation_score.items():\n print(\"model {} has f1 score of {}\".format(model, score))\n\n\nif __name__ == '__main__':\n ap = arg.ArgumentParser()\n ap.add_argument('--train_prop', type=float, default=0.6, required=False, help='Training proportion')\n ap.add_argument('--min_doc_freq', type=int, default=5, required=False, help='Min document frequency')\n args = ap.parse_args()\n main(train_prop=args.train_prop, min_doc_freq=args.min_doc_freq)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"530677981","text":"###########################################################################\n#\n# Copyright 2019 Google Inc.\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# https://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###########################################################################\n\norderDocumentsListResponse_Schema = [\n {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"\", \n \"name\": \"nextPageToken\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"\", \n \"name\": \"kind\"\n }, \n {\n \"fields\": [\n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"orderId\"\n }, \n {\n \"fields\": {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"approvedByUserProfileIds\"\n }, \n \"type\": \"RECORD\", \n \"name\": \"approvedByUserProfileIds\", \n \"mode\": \"REPEATED\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"\", \n \"name\": \"kind\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"subaccountId\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"DATE\", \n \"description\": \"\", \n \"name\": \"effectiveDate\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"DATETIME\", \n \"description\": \"\", \n \"name\": \"lastSentTime\"\n }, \n {\n \"fields\": {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"\", \n \"name\": \"lastSentRecipients\"\n }, \n \"type\": \"RECORD\", \n \"name\": \"lastSentRecipients\", \n \"mode\": \"REPEATED\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"\", \n \"name\": \"title\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"amendedOrderDocumentId\"\n }, \n {\n \"type\": \"BOOLEAN\", \n \"name\": \"signed\", \n \"mode\": \"NULLABLE\"\n }, \n [\n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"time\"\n }\n ], \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"advertiserId\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"projectId\"\n }, \n {\n \"type\": \"BOOLEAN\", \n \"name\": \"cancelled\", \n \"mode\": \"NULLABLE\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"STRING\", \n \"description\": \"PLANNING_ORDER_TYPE_CHANGE_ORDER, PLANNING_ORDER_TYPE_INSERTION_ORDER\", \n \"name\": \"type\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"id\"\n }, \n {\n \"mode\": \"NULLABLE\", \n \"type\": \"INT64\", \n \"description\": \"\", \n \"name\": \"accountId\"\n }\n ], \n \"type\": \"RECORD\", \n \"name\": \"orderDocuments\", \n \"mode\": \"REPEATED\"\n }\n]\n","sub_path":"starthinker/task/dcm_api/schema/orderDocumentsListResponse.py","file_name":"orderDocumentsListResponse.py","file_ext":"py","file_size_in_byte":3636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"4474784","text":"import gc\nimport os\nimport time\nimport logging\nimport datetime\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport xgboost as xgb\nimport lightgbm as lgb\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom scipy import stats\nfrom sklearn.svm import NuSVR, SVR\nfrom catboost import CatBoostRegressor\nfrom sklearn.kernel_ridge import KernelRidge\nfrom scipy.signal import hann, hilbert, convolve\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.model_selection import KFold, StratifiedKFold, RepeatedKFold, cross_val_score\nfrom sklearn.metrics import roc_auc_score, mean_absolute_error, mean_squared_error\nfrom sklearn.model_selection import GridSearchCV\nfrom tqdm import tqdm\n\nfrom GBDT import myhyperopt\n\n# settings\nwarnings.filterwarnings('ignore')\nnp.random.seed(2019)\nPATH = \"E:/kaggle/kaggle-Lanl_Earthquake_Prediction/\"\n\nclean_idx = [x for x in range(4194, 4651)]\n\n# logger\ndef get_logger():\n FORMAT = '[%(levelname)s]%(asctime)s:%(name)s:%(message)s'\n logging.basicConfig(format=FORMAT)\n logger = logging.getLogger('main')\n logger.setLevel(logging.DEBUG)\n return logger\n\n\nlogger = get_logger()\n\n\ndef read_train_data(nrows=4096):\n # load data\n logger.info('Start read data')\n train_df = pd.read_csv(PATH + 'input/train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64},\n nrows=nrows)\n return train_df\n\n\ndef load_feature_data():\n logger.info('Start read feature data')\n\n # train_X = pd.DataFrame()\n train_features = pd.read_csv(PATH + 'data_sample/train_features.csv')\n train_features1 = pd.read_csv(PATH + 'feature_select/train_features_aug_bigsmall.csv')\n\n # train_features_denoised = pd.read_csv(PATH + 'input/lanl-features/train_features_denoised.csv')\n # train_features_denoised.columns = [f'{i}_denoised' for i in train_features_denoised.columns]\n train_mfcc_features = pd.read_csv(PATH + 'input/lanl-features/mfcc_train40.csv')\n\n # train_X = pd.concat([train_features, train_features1,train_mfcc_features], axis=1) #train_mfcc_features\n\n train_X = train_features # .ix[0:4193,:]\n\n scaler = StandardScaler()\n scaler.fit(train_X)\n scaled_train_X = pd.DataFrame(scaler.transform(train_X), columns=train_X.columns)\n\n # test_X = pd.DataFrame()\n test_features = pd.read_csv(PATH + 'data_sample/test_features.csv')\n test_features1 = pd.read_csv(PATH + 'feature_select/test_features_aug_bigsmall.csv')\n # test_features_denoised = pd.read_csv(PATH + 'input/lanl-features/test_features_denoised.csv')\n # test_features_denoised.columns = [f'{i}_denoised' for i in test_features_denoised.columns]\n test_mfcc_features = pd.read_csv(PATH + 'input/lanl-features/mfcc_test40.csv')\n mfcccol = list(train_mfcc_features.columns)\n\n # test_X = pd.concat([test_features, test_features1,test_mfcc_features], axis=1).drop(['seg_id'], axis=1) #test_mfcc_features.drop(['seg_id'], axis=1) #\n test_X = test_features\n\n scaled_test_X = pd.DataFrame(scaler.transform(test_X), columns=test_X.columns)\n\n train_y = pd.read_csv(PATH + 'data_sample/y.csv') # .ix[0:4193]\n submission = pd.read_csv(PATH + 'input/sample_submission.csv', index_col='seg_id')\n\n return scaled_train_X, scaled_test_X, train_y, submission, mfcccol\n\n\ndef add_trend_feature(arr, abs_values=False):\n idx = np.array(range(len(arr)))\n if abs_values:\n arr = np.abs(arr)\n lr = LinearRegression()\n lr.fit(idx.reshape(-1, 1), arr)\n return lr.coef_[0]\n\n\ndef classic_sta_lta(x, length_sta, length_lta):\n sta = np.cumsum(x ** 2)\n # Convert to float\n sta = np.require(sta, dtype=np.float)\n # Copy for LTA\n lta = sta.copy()\n # Compute the STA and the LTA\n sta[length_sta:] = sta[length_sta:] - sta[:-length_sta]\n sta /= length_sta\n lta[length_lta:] = lta[length_lta:] - lta[:-length_lta]\n lta /= length_lta\n # Pad zeros\n sta[:length_lta - 1] = 0\n # Avoid division by zero by setting zero values to tiny float\n dtiny = np.finfo(0.0).tiny\n idx = lta < dtiny\n lta[idx] = dtiny\n return sta / lta\n\n\ndef create_features(seg_id, seg, X):\n xc = pd.Series(seg['acoustic_data'].values)\n zc = np.fft.fft(xc)\n\n X.loc[seg_id, 'mean'] = xc.mean()\n X.loc[seg_id, 'std'] = xc.std()\n X.loc[seg_id, 'max'] = xc.max()\n X.loc[seg_id, 'min'] = xc.min()\n\n # FFT transform values\n realFFT = np.real(zc)\n imagFFT = np.imag(zc)\n X.loc[seg_id, 'Rmean'] = realFFT.mean()\n X.loc[seg_id, 'Rstd'] = realFFT.std()\n X.loc[seg_id, 'Rmax'] = realFFT.max()\n X.loc[seg_id, 'Rmin'] = realFFT.min()\n X.loc[seg_id, 'Imean'] = imagFFT.mean()\n X.loc[seg_id, 'Istd'] = imagFFT.std()\n X.loc[seg_id, 'Imax'] = imagFFT.max()\n X.loc[seg_id, 'Imin'] = imagFFT.min()\n X.loc[seg_id, 'Rmean_last_5000'] = realFFT[-5000:].mean()\n X.loc[seg_id, 'Rstd__last_5000'] = realFFT[-5000:].std()\n X.loc[seg_id, 'Rmax_last_5000'] = realFFT[-5000:].max()\n X.loc[seg_id, 'Rmin_last_5000'] = realFFT[-5000:].min()\n X.loc[seg_id, 'Rmean_last_15000'] = realFFT[-15000:].mean()\n X.loc[seg_id, 'Rstd_last_15000'] = realFFT[-15000:].std()\n X.loc[seg_id, 'Rmax_last_15000'] = realFFT[-15000:].max()\n X.loc[seg_id, 'Rmin_last_15000'] = realFFT[-15000:].min()\n\n X.loc[seg_id, 'mean_change_abs'] = np.mean(np.diff(xc))\n X.loc[seg_id, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(xc) / xc[:-1]))[0])\n X.loc[seg_id, 'abs_max'] = np.abs(xc).max()\n # X.loc[seg_id, 'abs_min'] = np.abs(xc).min()\n\n X.loc[seg_id, 'std_first_50000'] = xc[:50000].std()\n X.loc[seg_id, 'std_last_50000'] = xc[-50000:].std()\n X.loc[seg_id, 'std_first_10000'] = xc[:10000].std()\n X.loc[seg_id, 'std_last_10000'] = xc[-10000:].std()\n\n X.loc[seg_id, 'avg_first_50000'] = xc[:50000].mean()\n X.loc[seg_id, 'avg_last_50000'] = xc[-50000:].mean()\n X.loc[seg_id, 'avg_first_10000'] = xc[:10000].mean()\n X.loc[seg_id, 'avg_last_10000'] = xc[-10000:].mean()\n\n X.loc[seg_id, 'min_first_50000'] = xc[:50000].min()\n X.loc[seg_id, 'min_last_50000'] = xc[-50000:].min()\n X.loc[seg_id, 'min_first_10000'] = xc[:10000].min()\n X.loc[seg_id, 'min_last_10000'] = xc[-10000:].min()\n\n X.loc[seg_id, 'max_first_50000'] = xc[:50000].max()\n X.loc[seg_id, 'max_last_50000'] = xc[-50000:].max()\n X.loc[seg_id, 'max_first_10000'] = xc[:10000].max()\n X.loc[seg_id, 'max_last_10000'] = xc[-10000:].max()\n\n X.loc[seg_id, 'max_to_min'] = xc.max() / np.abs(xc.min())\n X.loc[seg_id, 'max_to_min_diff'] = xc.max() - np.abs(xc.min())\n X.loc[seg_id, 'count_big'] = len(xc[np.abs(xc) > 500])\n X.loc[seg_id, 'sum'] = xc.sum()\n\n X.loc[seg_id, 'mean_change_rate_first_50000'] = np.mean(np.nonzero((np.diff(xc[:50000]) / xc[:50000][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_last_50000'] = np.mean(np.nonzero((np.diff(xc[-50000:]) / xc[-50000:][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_first_10000'] = np.mean(np.nonzero((np.diff(xc[:10000]) / xc[:10000][:-1]))[0])\n X.loc[seg_id, 'mean_change_rate_last_10000'] = np.mean(np.nonzero((np.diff(xc[-10000:]) / xc[-10000:][:-1]))[0])\n\n X.loc[seg_id, 'q95'] = np.quantile(xc, 0.95)\n X.loc[seg_id, 'q99'] = np.quantile(xc, 0.99)\n X.loc[seg_id, 'q05'] = np.quantile(xc, 0.05)\n X.loc[seg_id, 'q01'] = np.quantile(xc, 0.01)\n\n X.loc[seg_id, 'abs_q95'] = np.quantile(np.abs(xc), 0.95)\n X.loc[seg_id, 'abs_q99'] = np.quantile(np.abs(xc), 0.99)\n X.loc[seg_id, 'abs_q05'] = np.quantile(np.abs(xc), 0.05)\n X.loc[seg_id, 'abs_q01'] = np.quantile(np.abs(xc), 0.01)\n\n X.loc[seg_id, 'trend'] = add_trend_feature(xc)\n X.loc[seg_id, 'abs_trend'] = add_trend_feature(xc, abs_values=True)\n X.loc[seg_id, 'abs_mean'] = np.abs(xc).mean()\n X.loc[seg_id, 'abs_std'] = np.abs(xc).std()\n\n X.loc[seg_id, 'mad'] = xc.mad()\n X.loc[seg_id, 'kurt'] = xc.kurtosis()\n X.loc[seg_id, 'skew'] = xc.skew()\n X.loc[seg_id, 'med'] = xc.median()\n\n X.loc[seg_id, 'Hilbert_mean'] = np.abs(hilbert(xc)).mean()\n X.loc[seg_id, 'Hann_window_mean'] = (convolve(xc, hann(150), mode='same') / sum(hann(150))).mean()\n X.loc[seg_id, 'classic_sta_lta1_mean'] = classic_sta_lta(xc, 500, 10000).mean()\n X.loc[seg_id, 'classic_sta_lta2_mean'] = classic_sta_lta(xc, 5000, 100000).mean()\n X.loc[seg_id, 'classic_sta_lta3_mean'] = classic_sta_lta(xc, 3333, 6666).mean()\n X.loc[seg_id, 'classic_sta_lta4_mean'] = classic_sta_lta(xc, 10000, 25000).mean()\n X.loc[seg_id, 'Moving_average_700_mean'] = xc.rolling(window=700).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_1500_mean'] = xc.rolling(window=1500).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_3000_mean'] = xc.rolling(window=3000).mean().mean(skipna=True)\n X.loc[seg_id, 'Moving_average_6000_mean'] = xc.rolling(window=6000).mean().mean(skipna=True)\n ewma = pd.Series.ewm\n X.loc[seg_id, 'exp_Moving_average_300_mean'] = (ewma(xc, span=300).mean()).mean(skipna=True)\n X.loc[seg_id, 'exp_Moving_average_3000_mean'] = ewma(xc, span=3000).mean().mean(skipna=True)\n X.loc[seg_id, 'exp_Moving_average_30000_mean'] = ewma(xc, span=6000).mean().mean(skipna=True)\n no_of_std = 2\n X.loc[seg_id, 'MA_700MA_std_mean'] = xc.rolling(window=700).std().mean()\n X.loc[seg_id, 'MA_700MA_BB_high_mean'] = (\n X.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X.loc[seg_id, 'MA_700MA_BB_low_mean'] = (\n X.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, 'MA_700MA_std_mean']).mean()\n X.loc[seg_id, 'MA_400MA_std_mean'] = xc.rolling(window=400).std().mean()\n X.loc[seg_id, 'MA_400MA_BB_high_mean'] = (\n X.loc[seg_id, 'Moving_average_700_mean'] + no_of_std * X.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X.loc[seg_id, 'MA_400MA_BB_low_mean'] = (\n X.loc[seg_id, 'Moving_average_700_mean'] - no_of_std * X.loc[seg_id, 'MA_400MA_std_mean']).mean()\n X.loc[seg_id, 'MA_1000MA_std_mean'] = xc.rolling(window=1000).std().mean()\n\n X.loc[seg_id, 'iqr'] = np.subtract(*np.percentile(xc, [75, 25]))\n X.loc[seg_id, 'q999'] = np.quantile(xc, 0.999)\n X.loc[seg_id, 'q001'] = np.quantile(xc, 0.001)\n X.loc[seg_id, 'ave10'] = stats.trim_mean(xc, 0.1)\n\n for windows in [10, 100, 1000]:\n x_roll_std = xc.rolling(windows).std().dropna().values\n x_roll_mean = xc.rolling(windows).mean().dropna().values\n\n X.loc[seg_id, 'ave_roll_std_' + str(windows)] = x_roll_std.mean()\n X.loc[seg_id, 'std_roll_std_' + str(windows)] = x_roll_std.std()\n X.loc[seg_id, 'max_roll_std_' + str(windows)] = x_roll_std.max()\n X.loc[seg_id, 'min_roll_std_' + str(windows)] = x_roll_std.min()\n X.loc[seg_id, 'q01_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.01)\n X.loc[seg_id, 'q05_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.05)\n X.loc[seg_id, 'q95_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.95)\n X.loc[seg_id, 'q99_roll_std_' + str(windows)] = np.quantile(x_roll_std, 0.99)\n X.loc[seg_id, 'av_change_abs_roll_std_' + str(windows)] = np.mean(np.diff(x_roll_std))\n X.loc[seg_id, 'av_change_rate_roll_std_' + str(windows)] = np.mean(\n np.nonzero((np.diff(x_roll_std) / x_roll_std[:-1]))[0])\n X.loc[seg_id, 'abs_max_roll_std_' + str(windows)] = np.abs(x_roll_std).max()\n\n X.loc[seg_id, 'ave_roll_mean_' + str(windows)] = x_roll_mean.mean()\n X.loc[seg_id, 'std_roll_mean_' + str(windows)] = x_roll_mean.std()\n X.loc[seg_id, 'max_roll_mean_' + str(windows)] = x_roll_mean.max()\n X.loc[seg_id, 'min_roll_mean_' + str(windows)] = x_roll_mean.min()\n X.loc[seg_id, 'q01_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.01)\n X.loc[seg_id, 'q05_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.05)\n X.loc[seg_id, 'q95_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.95)\n X.loc[seg_id, 'q99_roll_mean_' + str(windows)] = np.quantile(x_roll_mean, 0.99)\n X.loc[seg_id, 'av_change_abs_roll_mean_' + str(windows)] = np.mean(np.diff(x_roll_mean))\n X.loc[seg_id, 'av_change_rate_roll_mean_' + str(windows)] = np.mean(\n np.nonzero((np.diff(x_roll_mean) / x_roll_mean[:-1]))[0])\n X.loc[seg_id, 'abs_max_roll_mean_' + str(windows)] = np.abs(x_roll_mean).max()\n\n\ndef feature_engineering(train_df):\n # features engineering\n logger.info('Features engineering')\n rows = 150000\n segments = int(np.floor(train_df.shape[0] / rows))\n print(\"Number of segments: \", segments)\n train_X = pd.DataFrame(index=range(segments), dtype=np.float64)\n train_y = pd.DataFrame(index=range(segments), dtype=np.float64, columns=['time_to_failure'])\n\n # process train data\n logger.info('Process train data')\n for seg_id in range(segments):\n seg = train_df.iloc[seg_id * rows:seg_id * rows + rows]\n create_features(seg_id, seg, train_X)\n train_y.loc[seg_id, 'time_to_failure'] = seg['time_to_failure'].values[-1]\n\n scaler = StandardScaler()\n scaler.fit(train_X)\n scaled_train_X = pd.DataFrame(scaler.transform(train_X), columns=train_X.columns)\n\n # process test data\n logger.info('Process test data')\n submission = pd.read_csv(PATH + 'input/sample_submission.csv', index_col='seg_id')\n test_X = pd.DataFrame(columns=train_X.columns, dtype=np.float64, index=submission.index)\n\n for seg_id in test_X.index:\n seg = pd.read_csv(PATH + 'input/test/' + seg_id + '.csv')\n create_features(seg_id, seg, test_X)\n scaled_test_X = pd.DataFrame(scaler.transform(test_X), columns=test_X.columns)\n\n del train_df\n gc.collect()\n\n scaled_train_X.to_csv(PATH + 'GBDT/feature_extra/train_feature.csv', index=False)\n scaled_test_X.to_csv(PATH + 'GBDT/feature_extra/test_feature.csv', index=False)\n train_y.to_csv(PATH + 'GBDT/feature_extra/train_y.csv', index=False)\n\n return scaled_train_X, scaled_test_X, train_y, submission\n\n\ndef data_augmentation(train, aug_ratio=0.5):\n a = np.arange(0, train.shape[1]) #\n # initialise aug dataframe - remember to set dtype!\n train_aug = pd.DataFrame(index=train.index, columns=train.columns, dtype='float64')\n\n for i in tqdm(range(0, len(train))): # 样本数量4194\n # ratio of features to be randomly sampled\n AUG_FEATURE_RATIO = aug_ratio\n # to integer count\n AUG_FEATURE_COUNT = np.floor(train.shape[1] * AUG_FEATURE_RATIO).astype('int16')\n\n # randomly sample half of columns that will contain random values #如果是ndarray数组，随机样本在该数组获取（取数据元素）,如果是整型数据随机样本生成类似np.arange(n)\n aug_feature_index = np.random.choice(train.shape[1], AUG_FEATURE_COUNT, replace=False)\n aug_feature_index.sort()\n\n # obtain indices for features not in aug_feature_index #\n feature_index = np.where(np.logical_not(np.in1d(a, aug_feature_index)))[0]\n\n # first insert real values for features in feature_index #将未被增强的特征按原始数据保存下来\n train_aug.iloc[i, feature_index] = train.iloc[i, feature_index]\n\n # random row index to randomly sampled values for each features #从原始样本中随机抽取与增强特征数量相同的样本 （即增强的特征只作用于部分样本）\n rand_row_index = np.random.choice(len(train), len(aug_feature_index), replace=True)\n\n # for each feature being randomly sampled, extract value from random row in train\n for n, j in enumerate(aug_feature_index): # 即增强的特征 只更新 n 个样本 n=增强特征数\n train_aug.iloc[i, j] = train.iloc[rand_row_index[n], j]\n\n return train_aug # 过采样产生的数据，将其与train_x拼接融合\n\n\ndef run_model_xgb(scaled_train_X, scaled_test_X, train_y, feature_col):\n logger.info('Run xgb model')\n n_fold = 10\n folds = KFold(n_splits=n_fold, shuffle=True, random_state=42)\n train_columns = feature_col\n random_seed = 4126\n predictions = np.zeros(len(scaled_test_X))\n preds_train = np.zeros(len(scaled_train_X))\n\n maes = []\n tr_maes = []\n\n for fold_, (trn_idx, val_idx) in enumerate(folds.split(scaled_train_X, train_y.values)):\n print('working fold %d' % fold_)\n strLog = \"fold {}\".format(fold_)\n print(strLog)\n\n X_tr, X_val = scaled_train_X.iloc[trn_idx], scaled_train_X.iloc[val_idx]\n y_tr, y_val = train_y.iloc[trn_idx], train_y.iloc[val_idx]\n\n clf = xgb.XGBRegressor(n_estimators=10000,\n learning_rate=0.1,\n max_depth=6,\n subsample=0.9,\n colsample_bytree=0.67,\n reg_lambda=1.0, # seems best within 0.5 of 2.0\n # gamma=1,\n random_state=random_seed,\n n_jobs=12,\n verbosity=-1)\n clf.fit(X_tr, y_tr)\n preds = clf.predict(scaled_test_X) # , num_iteration=model.best_iteration_)\n predictions += preds / folds.n_splits\n preds = clf.predict(scaled_train_X) # , num_iteration=model.best_iteration_)\n preds_train += preds / folds.n_splits\n\n preds = clf.predict(X_tr)\n mae = mean_absolute_error(y_tr, preds)\n print('Tr MAE: %.6f' % mae)\n maes.append(mae)\n\n preds = clf.predict(X_val)\n mae = mean_absolute_error(y_val, preds)\n print('MAE: %.6f' % mae)\n tr_maes.append(mae)\n\n\ndef run_model_lgbm(params, scaled_train_X, scaled_test_X, train_y, res):\n logger.info('Run lgbm model')\n n_fold = 5\n folds = KFold(n_splits=n_fold, shuffle=True, random_state=2013)\n # train_columns = feature_col\n\n scores = []\n\n oof = np.zeros(len(scaled_train_X))\n predictions = np.zeros(len(scaled_test_X))\n feature_importance_df = pd.DataFrame()\n\n\n feat = scaled_train_X.columns\n\n # run model\n # a = folds.split(scaled_train_X, train_y.values)\n for fold_, (trn_idx, val_idx) in enumerate(folds.split(scaled_train_X, train_y.values)):\n print(\"fold {}\".format(fold_))\n logger.info(\"fold {}\".format(fold_))\n\n val_idx = val_idx[val_idx < 4193]\n\n X_tr, X_val = scaled_train_X.iloc[trn_idx], scaled_train_X.iloc[val_idx]\n y_tr, y_val = train_y.iloc[trn_idx], train_y.iloc[val_idx]\n\n clf = lgb.LGBMRegressor(**params, n_estimators=200000, n_jobs=-1)\n\n clf.fit(X_tr, y_tr, eval_set=[(X_tr, y_tr), (X_val, y_val)], eval_metric='mae', verbose=1000,\n early_stopping_rounds=400)\n\n oof[val_idx] = clf.predict(X_val, num_iteration=clf.best_iteration_)\n # feature importance\n\n # fold_importance_df = pd.DataFrame()\n # fold_importance_df[\"Feature\"] = train_columns\n # fold_importance_df[\"importance\"] = clf.feature_importances_[:len(train_columns)]\n # fold_importance_df[\"fold\"] = fold_ + 1\n # feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n\n # predictions\n predictions += clf.predict(scaled_test_X, num_iteration=clf.best_iteration_) / folds.n_splits\n scores.append(mean_absolute_error(y_val, oof[val_idx]))\n res.ix[0,feat] = np.mean(scores)\n res.ix[1, feat] = np.std(scores)\n res.to_csv('998cv_rank.csv')\n\n # feature_importance_df = feature_importance_df.groupby(['Feature']).mean()\n # feature_importance_df.to_csv('E:/kaggle/kaggle-Lanl_Earthquake_Prediction/data_sample/feature_importance.csv')\n strLog = 'CV mean score: {0:.4f}, std: {1:.4f}.'.format(np.mean(scores), np.std(scores))\n print(strLog)\n logger.info(strLog)\n\n # return oof, predictions, feature_importance_df, mean_absolute_error(train_y.values, oof)\n\n\ndef plt_feature_importance(feature_importance_df):\n logger.info('Plot feature importance')\n cols = (feature_importance_df[[\"Feature\", \"importance\"]]\n .groupby(\"Feature\")\n .mean()\n .sort_values(by=\"importance\", ascending=False)[:200].index)\n best_features = feature_importance_df.loc[feature_importance_df.Feature.isin(cols)]\n # d_features = best_features.groupby(['Feature']).mean()\n # d_features.to_csv('best_features.csv')\n plt.figure(figsize=(14, 26))\n sns.barplot(x=\"importance\", y=\"Feature\", data=best_features.sort_values(by=\"importance\", ascending=False))\n plt.title('LightGBM Features (averaged over folds)')\n plt.tight_layout()\n plt.savefig('lgbm_importances.png')\n\n\ndef submit(submission, predictions, i):\n # submission\n logger.info('Submisison')\n submission.time_to_failure = predictions\n submission.to_csv('./Lightgbm_result/submission_' + str(i) + '_auc.csv', index=True)\n\n\ndef main(nrows=None):\n # train_df = read_train_data(nrows)\n # scaled_train_X, scaled_test_X, train_y, submission = feature_engineering(train_df)\n scaled_train_X, scaled_test_X, train_y, submission, mfcccols = load_feature_data()\n # feature_importance_df = pd.read_csv('feature_importance.csv')\n #\n # cols = list((feature_importance_df[[\"Feature\", \"importance\"]]\n # .groupby(\"Feature\")\n # .mean()\n # .sort_values(by=\"importance\", ascending=False)[:].index))[:200]\n # cols.extend(scaled_train_X.columns[-20:])\n # dd = pd.read_excel(PATH + 'input/lanl-features/features_select2.xlsx',header=None)\n # dd.columns = ['feat']\n # cols = list(dd['feat']) + list(mfcccols)\n\n # best_feat = pd.read_csv('best_features.csv')\n # best_feat1 = best_feat[best_feat['importance'] >= 1]\n # cols1 = list(best_feat1['feat'])\n\n # sele = pd.read_csv(PATH + '/feat_val/feat_auc_now998.csv').T\n # sele.columns = ['mean', 'std']\n # sele = sele.sort_values('mean', ascending=False)\n # selec = sele.copy()\n # sele_hold = sele[sele['mean'] < 0.8]\n # #\n # # # hold_auc = pd.Series(list(sele_hold.index))\n # # # hold_auc.to_csv('E:/kaggle/kaggle-Lanl_Earthquake_Prediction/feat_val/hold_feat.csv',index=False)\n # #\n # sele_hold = list(sele_hold.index)\n # cols = sele_hold\n #\n # #\n # scaled_train_X = scaled_train_X[cols]\n # scaled_test_X = scaled_test_X[cols]\n\n # l = [0.5]\n # res = []\n # for i in l:\n #\n # train_X_aug = data_augmentation(scaled_train_X,i)\n # train_y_aug = data_augmentation(train_y,i)\n # train_all = pd.concat([scaled_train_X, train_X_aug])\n # y_all = pd.concat([train_y, train_y_aug])\n\n # lgbm_params = myhyperopt.quick_hyperopt(train_all, y_all, 'lgbm', 2500)\n\n lgbm_params = {'num_leaves': 60,\n 'min_data_in_leaf': 79,\n 'objective': 'gamma',\n 'max_depth': -1,\n 'learning_rate': 0.02,\n \"boosting\": \"gbdt\",\n \"bagging_freq\": 5,\n \"bagging_fraction\": 0.8126672064208567,\n \"bagging_seed\": 1024,\n \"metric\": 'mae',\n \"verbosity\": -1,\n 'reg_alpha': 0.1302650970728192,\n 'reg_lambda': 0.3603427518866501,\n 'feature_fraction': 0.2,\n 'colsample_bytree': 1.0\n }\n\n features = scaled_train_X.columns\n res = pd.DataFrame(columns=features, index=[0, 1])\n\n for i in tqdm(range(len(features))):\n feat = features[i]\n pan = scaled_train_X[feat].unique()\n if len(pan) <= 30: continue\n\n run_model_lgbm(lgbm_params, scaled_train_X[[feat]], scaled_test_X[[feat]], train_y,\n res)\n\n # run_model_xgb(scaled_train_X,scaled_test_X, train_y,scaled_train_X.columns)\n # submit(submission, predictions, 'seed')\n # # res.append(mae)\n # plt_feature_importance(feature_importance)\n # plt.figure(figsize=(16, 8))\n # plt.plot(train_y, color='g', label='y_train')\n # plt.plot(oof, color='r', label='lgb')\n # plt.legend()\n # plt.title('Predictions vs actual')\n # plt.show()\n # print(res)\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"adversarial_validation/feat_ligbm.py","file_name":"feat_ligbm.py","file_ext":"py","file_size_in_byte":24277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"465764295","text":"\n\n#calss header\nclass _OPERATIVE():\n\tdef __init__(self,): \n\t\tself.name = \"OPERATIVE\"\n\t\tself.definitions = [u'a worker, especially one who is skilled in working with their hands: ', u'a person who works secretly for an organization: ']\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/_operative.py","file_name":"_operative.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"316610176","text":"# log.py\n#\n#\n\n\"\"\" logging infrastructure. \"\"\"\n\n__copyright__ = \"Copyright 2015, Bart Thate\"\n\nfrom meds.defines import BOLD, BLA, BLUE, RED, GREEN, YELLOW, ENDC, homedir, logdir, datefmt, format, format_large, LEVELS\nfrom meds.misc import cdir, j\n\nimport logging.handlers\nimport logging\nimport os\n\nclass Formatter(logging.Formatter):\n\n def format(self, record):\n target = str(record.msg)\n if not target: target = \" \"\n if target[0] in [\">\", ]: target = \"%s%s%s%s\" % (BLUE, target[0], ENDC, target[1:])\n elif target[0] in [\"<\", ]: target = \"%s%s%s%s\" % (GREEN, target[0], ENDC, target[1:])\n elif target[0] in [\"!\", ]: target = \"%s%s%s%s\" % (BLUE, target[0], ENDC, target[1:])\n elif target[0] in [\"#\", ]: target = \"%s%s%s%s\" % (BLA, target[0], ENDC, target[1:])\n elif target[0] in [\"^\", ]: target = \"%s%s%s%s\" % (YELLOW, target[0], ENDC, target[1:])\n elif target[0] in [\"-\", ]: target = \"%s%s%s%s\" % (BOLD, target[0], ENDC, target[1:])\n elif target[0] in [\"&\", ]: target = \"%s%s%s%s\" % (RED, target[0], ENDC, target[1:])\n record.msg = target\n return logging.Formatter.format(self, record)\n\nclass FormatterClean(logging.Formatter):\n\n def format(self, record):\n target = str(record.msg)\n if not target: target = \" \"\n if target[0] in [\">\", \"<\", \"!\", \"#\", \"^\", \"-\", \"&\"]: target = target[2:]\n record.msg = target\n return logging.Formatter.format(self, record)\n\ndef log(level, error):\n l = LEVELS.get(str(level).lower(), logging.NOTSET)\n logging.log(l, error)\n\ndef loglevel(loglevel, colors=True):\n logger = logging.getLogger(\"\")\n if colors: formatter = Formatter(format, datefmt=datefmt)\n else: formatter = FormatterClean(format, datefmt=datefmt)\n level = LEVELS.get(str(loglevel).lower(), logging.NOTSET)\n filehandler = None\n logger.setLevel(level)\n if logger.handlers:\n for handler in logger.handlers: logger.removeHandler(handler)\n if not os.path.exists(logdir): cdir(logdir)\n try: filehandler = logging.handlers.TimedRotatingFileHandler(j(logdir, \"meds.log\"), 'midnight')\n except Exception as ex: logging.error(ex)\n ch = logging.StreamHandler()\n ch.setLevel(level)\n if colors: ch.setFormatter(formatter)\n else: ch.setFormatter(formatter)\n logger.addHandler(ch)\n if filehandler:\n ch.setFormatter(formatter)\n filehandler.setLevel(level)\n logger.addHandler(filehandler)\n global enabled\n enabled = True\n return logger\n","sub_path":"meds/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487238565","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom oslo_policy import policy\n\nfrom neutron.conf.policies import base\n\n\nCOLLECTION_PATH = '/routers'\nRESOURCE_PATH = '/routers/{id}'\n\nACTION_POST = [\n {'method': 'POST', 'path': COLLECTION_PATH},\n]\nACTION_PUT = [\n {'method': 'PUT', 'path': RESOURCE_PATH},\n]\nACTION_DELETE = [\n {'method': 'DELETE', 'path': RESOURCE_PATH},\n]\nACTION_GET = [\n {'method': 'GET', 'path': COLLECTION_PATH},\n {'method': 'GET', 'path': RESOURCE_PATH},\n]\n\n\nrules = [\n policy.DocumentedRuleDefault(\n 'create_router',\n base.RULE_ANY,\n 'Create a router',\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:distributed',\n base.RULE_ADMIN_ONLY,\n 'Specify ``distributed`` attribute when creating a router',\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:ha',\n base.RULE_ADMIN_ONLY,\n 'Specify ``ha`` attribute when creating a router',\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:external_gateway_info',\n base.RULE_ADMIN_OR_OWNER,\n 'Specify ``external_gateway_info`` information when creating a router',\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:external_gateway_info:network_id',\n base.RULE_ADMIN_OR_OWNER,\n ('Specify ``network_id`` in ``external_gateway_info`` information '\n 'when creating a router'),\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:external_gateway_info:enable_snat',\n base.RULE_ADMIN_ONLY,\n ('Specify ``enable_snat`` in ``external_gateway_info`` information '\n 'when creating a router'),\n ACTION_POST\n ),\n policy.DocumentedRuleDefault(\n 'create_router:external_gateway_info:external_fixed_ips',\n base.RULE_ADMIN_ONLY,\n ('Specify ``external_fixed_ips`` in ``external_gateway_info`` '\n 'information when creating a router'),\n ACTION_POST\n ),\n\n policy.DocumentedRuleDefault(\n 'get_router',\n base.RULE_ADMIN_OR_OWNER,\n 'Get a router',\n ACTION_GET\n ),\n policy.DocumentedRuleDefault(\n 'get_router:distributed',\n base.RULE_ADMIN_ONLY,\n 'Get ``distributed`` attribute of a router',\n ACTION_GET\n ),\n policy.DocumentedRuleDefault(\n 'get_router:ha',\n base.RULE_ADMIN_ONLY,\n 'Get ``ha`` attribute of a router',\n ACTION_GET\n ),\n\n policy.DocumentedRuleDefault(\n 'update_router',\n base.RULE_ADMIN_OR_OWNER,\n 'Update a router',\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:distributed',\n base.RULE_ADMIN_ONLY,\n 'Update ``distributed`` attribute of a router',\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:ha',\n base.RULE_ADMIN_ONLY,\n 'Update ``ha`` attribute of a router',\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:external_gateway_info',\n base.RULE_ADMIN_OR_OWNER,\n 'Update ``external_gateway_info`` information of a router',\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:external_gateway_info:network_id',\n base.RULE_ADMIN_OR_OWNER,\n ('Update ``network_id`` attribute of ``external_gateway_info`` '\n 'information of a router'),\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:external_gateway_info:enable_snat',\n base.RULE_ADMIN_ONLY,\n ('Update ``enable_snat`` attribute of ``external_gateway_info`` '\n 'information of a router'),\n ACTION_PUT\n ),\n policy.DocumentedRuleDefault(\n 'update_router:external_gateway_info:external_fixed_ips',\n base.RULE_ADMIN_ONLY,\n ('Update ``external_fixed_ips`` attribute of '\n '``external_gateway_info`` information of a router'),\n ACTION_PUT\n ),\n\n policy.DocumentedRuleDefault(\n 'delete_router',\n base.RULE_ADMIN_OR_OWNER,\n 'Delete a router',\n ACTION_DELETE\n ),\n\n policy.DocumentedRuleDefault(\n 'add_router_interface',\n base.RULE_ADMIN_OR_OWNER,\n 'Add an interface to a router',\n [\n {\n 'method': 'PUT',\n 'path': '/routers/{id}/add_router_interface',\n },\n ]\n ),\n policy.DocumentedRuleDefault(\n 'remove_router_interface',\n base.RULE_ADMIN_OR_OWNER,\n 'Remove an interface from a router',\n [\n {\n 'method': 'PUT',\n 'path': '/routers/{id}/remove_router_interface',\n },\n ]\n ),\n]\n\n\ndef list_rules():\n return rules\n","sub_path":"neutron/conf/policies/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":5309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"416559978","text":"from django.db import models\nfrom django.contrib.auth.models import Group\nfrom django.core import serializers\nfrom django.core.files.base import File\nfrom django.utils.timezone import now\n\nfrom periods.models import AcademicYear\nfrom university_structure.models import College\n\n#### For data dump\nimport tempfile\n\nimport os\nimport os.path\nfrom datetime import datetime\n\n\n######################################################################################################################\n####################### Committee Data\n\nclass Committee(models.Model):\n committee_id = models.BigAutoField(primary_key=True, verbose_name=\"Campus ID\")\n committee_name_ar = models.CharField(max_length=500, verbose_name=\"Committee Arabic Name\")\n committee_name_en = models.CharField(max_length=500, verbose_name=\"Committee Name\")\n committee_name_en_small = models.CharField(max_length=500, verbose_name='Committee Small Name',\n help_text='The small name and the academic year should be unique for a given committee')\n college = models.ForeignKey(College, on_delete=models.CASCADE, verbose_name='College')\n\n academic_year = models.ForeignKey(AcademicYear, on_delete=models.CASCADE, verbose_name='Academic Year',\n help_text='The small name and the academic year should be unique for a given committee')\n\n committee_head_group = models.ForeignKey(Group, verbose_name=\"Head Group\", on_delete=models.CASCADE,\n related_name='committees_heading', null=True, blank=True,\n help_text='Keep it blank to create a group automatically')\n committee_members_group = models.ForeignKey(Group, verbose_name=\"Members Group\", on_delete=models.CASCADE,\n related_name='committees_member', null=True, blank=True,\n help_text='Keep it blank to create a group automatically')\n committee_customers_group = models.ForeignKey(Group, verbose_name=\"Customers Group\", on_delete=models.CASCADE,\n related_name='committees_customer', null=True, blank=True,\n help_text='Keep it blank to create a group automatically')\n\n def __str__(self):\n return str(\n self.academic_year) + '| College__' + self.college.college_name_en_small + '| committee__' + self.committee_name_en_small\n\n def save(self, *args, **kwargs):\n if self.committee_head_group is None:\n _group = Group()\n _group.name = str(self) + '__head'\n _group.save()\n self.committee_head_group = _group\n\n if self.committee_members_group is None:\n _group = Group()\n _group.name = str(self) + '__members'\n _group.save()\n self.committee_members_group = _group\n\n if self.committee_customers_group is None:\n _group = Group()\n _group.name = str(self) + '__custmers'\n _group.save()\n self.committee_customers_group = _group\n\n super().save(*args, **kwargs)\n\n class Meta:\n ordering = ['college', 'academic_year', 'committee_name_en', ]\n verbose_name_plural = \"Committees\"\n verbose_name = \"Committee\"\n unique_together = [['committee_name_en_small', 'academic_year']]\n # constraints = [\n # models.UniqueConstraint(fields=['committee_name_en_small', 'academic_year'],\n # name='committee_academic_year'),\n # ]\n indexes = [\n models.Index(fields=['committee_name_ar', ]),\n models.Index(fields=['committee_name_en', ]),\n models.Index(fields=['committee_name_en_small', ]),\n models.Index(fields=['academic_year', ]),\n models.Index(fields=['college', ]),\n ]\n\n\n###############################################################################################\n######################### Data dump\n\n\ndef create_path(instance, filename):\n _datetime = datetime.now()\n _year = _datetime.strftime(\"%Y\")\n _mounth = _datetime.strftime(\"%m\")\n _day = _datetime.strftime(\"%d\")\n return 'dump/committees/{0}/{1}/{2}/{3}.json'.format(_year, _mounth, _day, filename)\n\n\nclass DataFile(models.Model):\n data_file_id = models.BigAutoField(primary_key=True)\n data_dump_date = models.DateTimeField(default=now, editable=False, blank=True)\n data_file_committee = models.FileField(upload_to=create_path, verbose_name='Committees Dump File',\n null=True, blank=True,\n help_text='Keep it blank to create a dump file automatically')\n\n def delete(self, *args, **kwargs):\n self.data_file_committee.delete()\n super().delete(*args, **kwargs)\n\n def save(self, *args, **kwargs):\n try:\n if os.path.isfile(self.data_file_committee.path):\n pass\n except:\n # save academic years\n fo1 = tempfile.NamedTemporaryFile()\n data = serializers.serialize(\"json\", Committee.objects.all())\n out = open(fo1.name, \"w\")\n out.write(data)\n out.close()\n self.data_file_committee.save(os.path.basename(fo1.name), File(fo1))\n\n super().save(*args, **kwargs)\n\n def __str__(self):\n return '[Committees] dump_' + str(self.data_dump_date)\n\n class Meta:\n ordering = ['data_dump_date']\n verbose_name_plural = \"Dumps\"\n verbose_name = \"Dump\"\n indexes = [\n models.Index(fields=['data_dump_date', ]),\n ]\n","sub_path":"committees/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"499344791","text":"# -*- coding: utf-8 -*-\n\nimport os\nfrom os.path import abspath, basename, dirname, join, normpath\n\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS as TCP\n\n#BASE_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n########## PATH CONFIGURATION\n# Absolute filesystem path to the Django project directory:\nDJANGO_ROOT = dirname(dirname(abspath(__file__)))\n\nENV = os.environ.get('ENV', None)\n\nADMINS = (\n ('Nahuel Chaves', 'nahuel.chaves@gmail.com'),\n)\n\nMANAGERS = ADMINS\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# In a Windows environment this must be set to your system time zone.\nTIME_ZONE = 'America/Argentina/Ushuaia'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'es'\n\nLOCALE_PATHS = (DJANGO_ROOT + '/configs/locale',)\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale.\nUSE_L10N = True\n\n# If you set this to False, Django will not use timezone-aware datetimes.\nUSE_TZ = True\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/var/www/example.com/media/\"\n#MEDIA_ROOT = BASE_ROOT + '/media/'\n\nMEDIA_ROOT = normpath(join(DJANGO_ROOT, 'media'))\nMEDIA_URL = '/media/'\n\n########## STATIC FILE CONFIGURATION\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#static-root\nSTATIC_ROOT = normpath(join(DJANGO_ROOT, 'assets'))\n\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#static-url\nSTATIC_URL = '/static/'\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n#STATIC_ROOT = BASE_ROOT + '/static/'\n\n# URL prefix for static files.\n#STATIC_URL = '/static/'\n\n# Additional locations of static files\nSTATICFILES_DIRS = (\n normpath(join(DJANGO_ROOT, 'static')),\n)\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder'\n)\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 'im1mh^q^r@*p++w^8k%ke&cml_1rva5idpzon-e&ziiwy2-15o'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader'\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.middleware.common.CommonMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.gzip.GZipMiddleware',\n 'pipeline.middleware.MinifyHTMLMiddleware',\n 'debug_toolbar.middleware.DebugToolbarMiddleware',\n #Uncomment the next line for simple clickjacking protection:\n # 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'cent11.urls'\n\n# Python dotted path to the WSGI application used by Django's runserver.\nWSGI_APPLICATION = 'cent11.wsgi.application'\n\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#template-dirs\nTEMPLATE_DIRS = (\n normpath(join(DJANGO_ROOT, 'templates')),\n)\n\n\n\nDJANGO_APPS = (\n 'suit',\n 'suitlocale',\n 'suit_redactor',\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'pipeline',\n 'debug_toolbar',\n 'model_utils',\n 'south',\n 'django_extensions',\n 'autocomplete_light',\n 'django_extensions',\n 'cities_light',\n 'bootstrap3',\n 'smuggler',\n\n)\n\n# Apps specific for this project go here.\nLOCAL_APPS = (\n 'academica',\n)\n\n# See: https://docs.djangoproject.com/en/dev/ref/settings/#installed-apps\nINSTALLED_APPS = DJANGO_APPS + LOCAL_APPS\n\nSESSION_SERIALIZER = 'django.contrib.sessions.serializers.JSONSerializer'\n\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error when DEBUG=False.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'filters': {\n 'require_debug_false': {\n '()': 'django.utils.log.RequireDebugFalse'\n }\n },\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'filters': ['require_debug_false'],\n 'class': 'django.utils.log.AdminEmailHandler'\n }\n },\n 'loggers': {\n 'django.request': {\n 'handlers': ['mail_admins'],\n 'level': 'ERROR',\n 'propagate': True,\n },\n }\n}\n\n## Pipeline\n\nSTATICFILES_STORAGE = 'pipeline.storage.PipelineStorage'\nPIPELINE_JS_COMPRESSOR = 'pipeline.compressors.slimit.SlimItCompressor'\nPIPELINE_CSS_COMPRESSOR = 'pipeline.compressors.cssmin.CSSMinCompressor'\nPIPELINE_CSSMIN_BINARY = 'cssmin'\nPIPELINE_COMPILERS = (\n 'pipeline.compilers.sass.SASSCompiler',\n)\nPIPELINE_SASS_BINARY = '/usr/bin/sass'\n\nPIPELINE_CSS = {\n 'admin': {\n 'source_filenames':\n (\n 'stylesheets/admin.css',\n ),\n 'output_filename': 'stylesheets/admin.min.css',\n 'extra_context': {\n 'media': 'screen',\n },\n },\n}\n\nPIPELINE_JS = {\n 'core': {\n 'source_filenames':\n (\n 'javascript/jquery-1.10.2.js',\n ),\n 'output_filename': 'javascript/jquery-1.10.2.min.js',\n }\n}\n\nSUIT_CONFIG = {\n 'ADMIN_NAME': 'CENT 11',\n 'HEADER_DATE_FORMAT': 'l, d \\d\\e F Y', # Saturday, 16th March 2013\n 'LIST_PER_PAGE': 15,\n 'SEARCH_URL': '',\n 'MENU': (\n #'sites',\n {'label': 'Gestion',\n 'icon': 'icon-briefcase',\n 'models': (\n 'academica.estructura',\n 'academica.curso',\n 'academica.semestre',\n 'academica.hora',\n )},\n {'label': 'RRHH', 'icon':'icon-user', 'models': ('academica.persona', 'academica.documento')},\n '-',\n {'app': 'auth', 'label':'Seguridad', 'icon':'icon-lock', 'models': ('user', 'group')},\n\n {'label':\n 'Settings',\n 'icon': 'icon-cog',\n 'models': (\n 'academica.ciclo',\n 'academica.carrera',\n 'academica.materia',\n 'academica.rol',\n )},\n {'label':\n 'Ubicacion',\n 'icon': 'icon-map-marker',\n 'models': (\n 'cities_light.country',\n 'cities_light.region',\n 'cities_light.city',\n 'academica.barrio'\n )},\n {'label': 'Ayuda', 'icon':'icon-question-sign', 'url': '/admin/ayuda/'},\n ),\n}\n\nTEMPLATE_CONTEXT_PROCESSORS = TCP + (\n 'django.core.context_processors.request',\n)\n\nSOUTH_MIGRATION_MODULES = {\n 'cities_light': 'cities_light.south_migrations',\n}\n\n\n\nDATE_FORMAT = \"%d-%m-%Y\"\n\nCITIES_LIGHT_CITY_SOURCES = [\"http://download.geonames.org/export/dump/cities1000.zip\"]\n\nROSETTA_REQUIRES_AUTH = True\n\nBOOTSTRAP3 = {\n 'css_url': '/static/css/bootstrap.min.css',\n 'js_url': '/static/js/bootstrap.min.js',\n 'jquery_url': '/static/js/jquery-1.11.0.min.js',\n 'javascript_url': '/static/js/bootstrap.min.js',\n 'include_jquery': True\n}\n\nSITE_ID = 1\n\nif ENV == 'development' or ENV is None:\n IS_DEVELOPMENT = True\n from configs.dev import *\n\nelif ENV == 'profiling':\n IS_PROFILING = True\n from configs.prof import *\n\nelif ENV == 'production':\n IS_PRODUCTION = True\n from configs.prod import *\n","sub_path":"dev/cent11/configs/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":8225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"519966367","text":"# import necessary packages\nimport progressbar\n# import quaternion\nimport datetime\nimport argparse\nimport pandas as pd\nimport numpy as np\nimport os\n\n# import ros packages\nimport rosbag\nimport roslib\nimport rospy\n\n# import message types\nfrom sensor_msgs.msg import Joy #, CompressedImage\nfrom nav_msgs.msg import Odometry\n# from p2os_msgs.msg import SonarArray\n\ndef extract_data(args):\n ''' Extracts data from .bag file given. '''\n\n # open the bag file\n print('Loading .bag file')\n bag = rosbag.Bag(args['bag'], 'r')\n\n # assumption:\n # the matching message will have been received within 150 ms of the pivot message\n # this is used to narrow the search for the matching message\n t_tol = rospy.Duration(secs=0, nsecs=150000000) # 150000000 ns = 150 ms\n\n # start and end time for the data extraction\n start_at = rospy.Time(secs=int(bag.get_start_time()) + 1, nsecs=0) # 1 second offset for the start\n end_at = rospy.Time(secs=int(bag.get_end_time()) - 1, nsecs=0) # -1 second offset for the end\n\n # pose is the slowest topic\n pose_bagmsgs = bag.read_messages(topics='pose', start_time=start_at, end_time=end_at)\n\n # joy is faster and is out of sync, which is why a tolerance is needed\n joy_bagmsgs = bag.read_messages(topics='joy', start_time=start_at-t_tol, end_time=end_at+t_tol)\n\n # load the messages into memory\n print('Loading messages into memory')\n pose_bagmsgs = [p for p in pose_bagmsgs]\n joy_bagmsgs = [j for j in joy_bagmsgs]\n\n # last messages saved. this will be used to obtain incremental\n # measures and ground truth for joystick measures\n last_pose_msg = None\n last_joy_msg = None\n second_last_pose_msg = None\n second_last_joy_msg = None\n\n # setup the progress bar\n c = 0\n c_max = len(pose_bagmsgs)\n bar = progressbar.ProgressBar(max_value=c_max)\n\n # create pandas dataframe for the data\n data = pd.DataFrame(columns=[\n 'dtns', # 'dts', 'dtns'\n # 'dx', 'dy', 'dq.w', 'dq.z'\n 'x', 'y', ####\n 'vx', 'dvx', 'vz', 'dvz',\n 'jx', 'djx', 'jy', 'djy', # 'jb', 'djb'\n 'jx_gt', 'jy_gt' # 'jb_gt'\n ])\n\n # counter for the joy list\n joy_idx = 0\n\n # read messages from the pose topic\n print('Processing and saving data')\n for pose_bagmsg in pose_bagmsgs:\n\n # get the pose message and it's timestamp\n pose_msg = pose_bagmsg.message\n pose_t = pose_bagmsg.timestamp # pose_msg.header.stamp # when it was generated/captured\n\n # read from the joy topic\n joy_msg = None\n min_diff = rospy.Duration(secs=10, nsecs=0) # starting the minimum with 10 seconds\n for joy_bagmsg in joy_bagmsgs[joy_idx:]:\n joy_t = joy_bagmsg.timestamp # joy_bagmsg.message.header.stamp\n diff_t = abs(pose_t-joy_t)\n if diff_t < min_diff or joy_msg is None:\n joy_msg = joy_bagmsg.message\n min_diff = diff_t\n joy_idx += 1\n else:\n break\n\n # to proceed, we need a last message for pose and joy\n if last_pose_msg is not None and last_joy_msg is not None:\n\n # and we also need a second last to get the ground truth\n if second_last_pose_msg is not None and second_last_joy_msg is not None:\n\n # extract the relevant data from the messages\n row = {}\n\n # datetime\n row['datetime'] = datetime.datetime.fromtimestamp(pose_t.secs) \\\n + datetime.timedelta(microseconds=pose_t.nsecs//1000)\n\n # time\n ti = last_pose_msg.header.stamp - second_last_pose_msg.header.stamp\n # row['dts'] = ti.secs\n row['dtns'] = ti.nsecs\n\n # position\n '''\n po = second_last_pose_msg.pose.pose.position\n pn = last_pose_msg.pose.pose.position\n row['dx'] = pn.x - po.x\n row['dy'] = pn.y - po.y\n '''\n row['x'] = last_pose_msg.pose.pose.position.x\n row['y'] = last_pose_msg.pose.pose.position.y\n\n # orientation\n '''\n qo = second_last_pose_msg.pose.pose.orientation\n qn = last_pose_msg.pose.pose.orientation\n qo = np.quaternion(qo.w, qo.x, qo.y, qo.z)\n qn = np.quaternion(qn.w, qn.x, qn.y, qn.z)\n qi = qn/qo\n row['dq.w'] = qi.w\n row['dq.z'] = qi.z\n '''\n\n # velocity\n row['vx'] = last_pose_msg.twist.twist.linear.x\n row['dvx'] = last_pose_msg.twist.twist.linear.x - second_last_pose_msg.twist.twist.linear.x\n row['vz'] = last_pose_msg.twist.twist.angular.z\n row['dvz'] = last_pose_msg.twist.twist.angular.z - second_last_pose_msg.twist.twist.angular.z\n\n # joy (data)\n row['jx'] = last_joy_msg.axes[0]\n row['djx'] = last_joy_msg.axes[0] - second_last_joy_msg.axes[0]\n row['jy'] = last_joy_msg.axes[1]\n row['djy'] = last_joy_msg.axes[1] - second_last_joy_msg.axes[1]\n # row['jb'] = last_joy_msg.buttons[0]\n # row['djb'] = last_joy_msg.buttons[0] - second_last_joy_msg.buttons[0]\n\n # joy (ground-truth)\n row['jx_gt'] = joy_msg.axes[0]\n row['jy_gt'] = joy_msg.axes[1]\n # row['jb_gt'] = joy_msg.buttons[0]\n \n # append the row to the data\n data = data.append(pd.DataFrame(row, index=[0]), ignore_index=True, sort=False)\n\n # save the messages for the next iteration\n second_last_pose_msg = last_pose_msg\n second_last_joy_msg = last_joy_msg\n\n # save the messages for the next iteration\n last_pose_msg = pose_msg\n last_joy_msg = joy_msg\n\n # update the progress\n c += 1\n bar.update(c)\n\n # mark the processing as finished\n bar.finish()\n\n # save the data to disk\n set_folder = os.path.join('data', args['dest'], args['set'])\n if not os.path.isdir(set_folder):\n os.makedirs(set_folder)\n if args['append']:\n data = pd.concat([pd.read_csv(os.path.join(set_folder, 'data.csv')), data])\n data.to_csv(path_or_buf=os.path.join(set_folder, 'data.csv'), header=True, index=False)\n \n # close the bag file\n bag.close()\n\ndef main():\n ''' Main function. '''\n\n # setup the argument parser\n ap = argparse.ArgumentParser()\n ap.add_argument('--bag', type=str,\n help='path to bag file')\n ap.add_argument('--set', type=str,\n help='indicates set (train, val, test)')\n ap.add_argument('--dest', type=str,\n help='destination folder')\n ap.add_argument('--append', action='store_true',\n help='append data to existing .csv')\n args = vars(ap.parse_args())\n\n # print the arguments\n print('File: {}'.format(args['bag']))\n print('Set: {}'.format(args['set']))\n\n # extract the data from bag the file\n extract_data(args)\n\n# if it is the main file\nif __name__ == '__main__':\n main()\n","sub_path":"extract_data.py","file_name":"extract_data.py","file_ext":"py","file_size_in_byte":7204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"611397848","text":"#!/usr/bin/python3\n\"\"\"\nextend your Python script to export data in the JSON format\n\"\"\"\nimport json\nimport requests\n\nif __name__ == \"__main__\":\n set_id = set()\n request = requests.get(\"https://jsonplaceholder.typicode.com/posts\")\n data = request.json()\n for user in data:\n set_id.add(user.get(\"userId\"))\n file_export = {}\n url = \"https://jsonplaceholder.typicode.com/users/{}\"\n url1 = \"https://jsonplaceholder.typicode.com/todos?userId={}\"\n for user in set_id:\n rq_users = requests.get(url.format(user))\n rq_name = rq_users.json().get(\"username\")\n rq_users = requests.get(url1.format(user))\n data_request = rq_users.json()\n file_export['{}'.format(user)] = []\n for task in data_request:\n file_export['{}'.format(user)].append({\n \"task\": task.get('title'),\n 'completed': task.get('completed'),\n 'username': rq_name\n })\n with open('todo_all_employees.json', mode='w') as file:\n json.dump({int(x): file_export[x] for x in file_export.keys()},\n file, sort_keys=True)\n","sub_path":"0x15-api/3-dictionary_of_list_of_dictionaries.py","file_name":"3-dictionary_of_list_of_dictionaries.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"283726856","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\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-i686/egg/Kamaelia/Support/DVB/CRC.py\n# Compiled at: 2008-10-19 12:19:52\n\"\"\"CRC algorithm used to verify the integrity of data in DVB transport streams.\n\"\"\"\n\ndef __MakeCRC32(polynomial=79764919, initial=4294967295):\n \"\"\" MakeCRC32([polynomial][,inital]) -> (string -> 32bit CRC of binary string data)\n \n Returns a function that calculatees the 32 bit CRC of binary data in a\n string, using the specified CRC polynomial and initial value.\n \"\"\"\n xorvals = []\n for x in range(0, 256):\n crc = long(x) << 24\n for bit in range(7, -1, -1):\n z32 = crc >> 31\n crc = crc << 1\n if z32:\n crc = crc ^ polynomial\n crc = crc & 4294967295\n\n xorvals.append(crc & 4294967295)\n\n def fastcrc32(data):\n crc = 4294967295\n for byte in data:\n byte = ord(byte)\n xv = xorvals[(byte ^ crc >> 24)]\n crc = xv ^ (crc & 16777215) << 8\n\n return crc\n\n return fastcrc32\n\n\n__dvbcrc = __MakeCRC32(polynomial=79764919)\ndvbcrc = lambda data: not __dvbcrc(data)","sub_path":"pycfiles/Kamaelia-0.6.0-py2.5/CRC.py","file_name":"CRC.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"131781450","text":"#!/usr/bin/env python3\n\"\"\"\nthat builds a dense block as described in Densely\nConnected Convolutional Networks:\n\"\"\"\n\nimport tensorflow.keras as K\n\n\ndef dense_block(X, nb_filters, growth_rate, layers):\n \"\"\"\n ARGs:\n\n X is the output from the previous layer\n nb_filters is an integer representing the number of filters in X\n growth_rate is the growth rate for the dense block\n layers is the number of layers in the dense block\n\n Returns: The concatenated output of each layer within the\n Dense Block and the number of filters within the concatenated\n outputs, respectively\n \"\"\"\n shortcut = X\n kernel = K.initializers.he_normal()\n for i in range(layers):\n \"\"\"the bottleneck layers used for DenseNet-B\"\"\"\n \"\"\"\n DenseNet-B: 1x1 conv bottleneck before 3x3 conv\n \"\"\"\n\n \"\"\" bottleneck convolution block\"\"\"\n X = K.layers.BatchNormalization(axis=3)(shortcut)\n X = K.layers.Activation('relu')(X)\n inter_channel = growth_rate * 4\n X = K.layers.Conv2D(filters=(inter_channel),\n kernel_size=(1, 1),\n padding=\"same\",\n kernel_initializer=kernel)(X)\n \"\"\" end of bottleneck convolution block \"\"\"\n\n X = K.layers.BatchNormalization(axis=3)(X)\n X = K.layers.Activation('relu')(X)\n X = K.layers.Conv2D(filters=growth_rate,\n kernel_size=(3, 3),\n padding=\"same\",\n kernel_initializer=kernel)(X)\n\n shortcut = K.layers.Concatenate()([shortcut, X])\n nb_filters += growth_rate\n return shortcut, nb_filters\n","sub_path":"supervised_learning/0x08-deep_cnns/5-dense_block.py","file_name":"5-dense_block.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"147323634","text":"\n\"\"\"\nCompany Template\ndata is taken from database randomly\ndata set up into php file \n\"\"\"\nfrom random_address import randomAddress\nimport mysql.connector, shutil, random, os, uuid\n\ndef get_data_db():\n try:\n connection = mysql.connector.connect(host='34.66.139.63',\n database='content_db',\n user='root',\n password='secret',\n )\n\n sql_select_Query = \"SELECT * FROM company_content ORDER BY RAND() LIMIT 1\"\n cursor = connection.cursor()\n cursor.execute(sql_select_Query)\n # get all records\n record = cursor.fetchall()\n\n except mysql.connector.Error as e:\n print(\"Error reading data from MySQL table\", e)\n finally:\n if connection.is_connected():\n connection.close()\n cursor.close()\n print(\"MySQL connection is closed\")\n return record\n\ndef setCompanyTemplate(website_name, country, theme):\n\taddressInfo = randomAddress(country)\n\trow = get_data_db()\n\tif theme == \"dark\":\n\t\tfileName = f\"company_dark/source/config/config_cont.php\"\n\telse:\n\t\tfileName = f\"company_light/source/config/config_cont.php\"\n\n\tf = open(fileName,'w+')\n\tf.write(\"\")\n\tf.close()\n\n \ndef setNewsTemplate(contentRequest):\n\tfileName = f\"news_website/news/config.php\"\n\n\tf = open(fileName,'w+')\n\tf.write(\"\")\n\tf.close()\n\n\ndef setBlogTemplate(contentRequest):\n\tfileName = f\"blog_website/blog/app/includes/config.php\"\n\n\tf = open(fileName,'w+')\n\tf.write(\"\")\n\tf.close()","sub_path":"guiDocker/app/template_variables.py","file_name":"template_variables.py","file_ext":"py","file_size_in_byte":4615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"31607761","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Nov 6 08:29:52 2019\r\n\r\n@author: 19158\r\n\"\"\"\r\n\r\n# Adjacency list representation of graphs\r\nimport graph_AM as graph # Replace line 3 by this one to demonstrate adjacy maxtrix implementation\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nfrom scipy.interpolate import interp1d\r\n\r\nclass Edge:\r\n def __init__(self, dest, weight=1):\r\n self.dest = dest \r\n self.weight = weight\r\n \r\nclass Graph:\r\n # Constructor\r\n def __init__(self, vertices, weighted=False, directed = False):\r\n self.al = [[] for i in range(vertices)]\r\n self.weighted = weighted\r\n self.directed = directed\r\n self.representation = 'AL'\r\n \r\n def insert_edge(self,source,dest,weight=1):\r\n if source >= len(self.al) or dest>=len(self.al) or source <0 or dest<0:\r\n print('Error, vertex number out of range')\r\n elif weight!=1 and not self.weighted:\r\n print('Error, inserting weighted edge to unweighted graph')\r\n else:\r\n self.al[source].append(Edge(dest,weight)) \r\n if not self.directed:\r\n self.al[dest].append(Edge(source,weight))\r\n \r\n def delete_edge_(self,source,dest):\r\n i = 0\r\n for edge in self.al[source]:\r\n if edge.dest == dest:\r\n self.al[source].pop(i)\r\n return True\r\n i+=1 \r\n return False\r\n \r\n def delete_edge(self,source,dest):\r\n if source >= len(self.al) or dest>=len(self.al) or source <0 or dest<0:\r\n print('Error, vertex number out of range')\r\n else:\r\n deleted = self.delete_edge_(source,dest)\r\n if not self.directed:\r\n deleted = self.delete_edge_(dest,source)\r\n if not deleted: \r\n print('Error, edge to delete not found') \r\n \r\n \r\n def display(self):\r\n print('[',end='')\r\n for i in range(len(self.al)):\r\n print('[',end='')\r\n for edge in self.al[i]:\r\n print('('+str(edge.dest)+','+str(edge.weight)+')',end='')\r\n print(']',end=' ') \r\n print(']') \r\n \r\n def draw(self):\r\n scale = 30\r\n fig, ax = plt.subplots()\r\n for i in range(len(self.al)):\r\n for edge in self.al[i]:\r\n d,w = edge.dest, edge.weight\r\n if self.directed or d>i:\r\n x = np.linspace(i*scale,d*scale)\r\n x0 = np.linspace(i*scale,d*scale,num=5)\r\n diff = np.abs(d-i)\r\n if diff == 1:\r\n y0 = [0,0,0,0,0]\r\n else:\r\n y0 = [0,-6*diff,-8*diff,-6*diff,0]\r\n f = interp1d(x0, y0, kind='cubic')\r\n y = f(x)\r\n s = np.sign(i-d)\r\n ax.plot(x,s*y,linewidth=1,color='k')\r\n if self.directed:\r\n xd = [x0[2]+2*s,x0[2],x0[2]+2*s]\r\n yd = [y0[2]-1,y0[2],y0[2]+1]\r\n yd = [y*s for y in yd]\r\n ax.plot(xd,yd,linewidth=1,color='k')\r\n if self.weighted:\r\n xd = [x0[2]+2*s,x0[2],x0[2]+2*s]\r\n yd = [y0[2]-1,y0[2],y0[2]+1]\r\n yd = [y*s for y in yd]\r\n ax.text(xd[2]-s*2,yd[2]+3*s, str(w), size=12,ha=\"center\", va=\"center\")\r\n ax.plot([i*scale,i*scale],[0,0],linewidth=1,color='k') \r\n ax.text(i*scale,0, str(i), size=20,ha=\"center\", va=\"center\",\r\n bbox=dict(facecolor='w',boxstyle=\"circle\"))\r\n ax.axis('off') \r\n ax.set_aspect(1.0)\r\n \r\n#---------------------------------------------------------------------------------------------------\r\n \r\n def method1(self, binary_number): \r\n nums = list()\r\n for i in binary_number:\r\n nums.append(i)\r\n \r\n #nums[0] represents Fox --- nums[1] represents Chicken --- nums[2] represents Grain --- nums[3] represents me \r\n if nums[0] == '0' and nums[1] == '0' and nums[2] == '0' and nums[3] == '1': \r\n #I cant leave them alone\r\n return 0\r\n \r\n if (nums[0] == '0' and nums[1] == '0' and nums[3] == '1') or (nums[0] == '1' and nums[1] == '1' and nums[3] == '0'):\r\n #checking if fox and chicken are alone\r\n return 0\r\n \r\n if (nums[1] == '0' and nums[2] == '0' and nums[3] == '1') or (nums[1] == '1' and nums[2] == '1' and nums[3] == '0'):\r\n #checking if chicken and grain are alone\r\n return 0\r\n return nums\r\n \r\n \r\n def as_AM(self): #hugo\r\n for i in self.al:\r\n print(i)\r\n \r\n \r\n def problem(self):\r\n valid = 0\r\n list_of_valids = list()\r\n \r\n binar = '0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111'\r\n binar = binar.split(' ')\r\n \r\n for index in range(len(binar)):\r\n binary_number = binar[index]\r\n \r\n valid = self.method1(binary_number)\r\n \r\n if valid != 0:\r\n list_of_valids.append(valid)\r\n \r\n print(list_of_valids)\r\n gra = Graph(len(list_of_valids))\r\n g.insert_edge(0,1)\r\n \r\n return\r\n\r\n \r\n def as_EL(self):\r\n g = []\r\n for source in range(len(self.al)):\r\n for dest in range(len(self.al[source])):\r\n g.append([source,dest])\r\n return g\r\n ","sub_path":"graph_AL.py","file_name":"graph_AL.py","file_ext":"py","file_size_in_byte":5668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"51254732","text":"import cv2\nimport time\n\n\nclass Video:\n def __init__(self, name=None, camera=0):\n super(Video, self).__init__()\n self.video = None\n self.total_frames = 0\n self.video_name = None\n self.frame = 0\n self.frame_pos = -1\n self.camera = camera\n\n def load(self, name, camera=0):\n self.video = cv2.VideoCapture(name)\n self.video_name = name\n self.camera = camera\n while not self.video.isOpened():\n print(\"Wait for the header...\\nVideo name :\", self.name)\n cv2.waitKey(1000)\n self.video = cv2.VideoCapture(name)\n self.frame = 0#158000\n self.frame_pos = -1\n self.video.set(cv2.CAP_PROP_POS_FRAMES, self.frame)\n self.total_frames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))\n\n def set_position(self, pos):\n # pos = self.frame - pos\n # if pos < 0:\n # pos = 0\n self.frame = pos\n self.video.set(cv2.CAP_PROP_POS_FRAMES, pos)\n\n def take_point(self, index):\n if index < 0:\n index = 0\n self.frame = index\n return index\n\n def get_frames(self, block, step=1):\n frames = []\n flag = False\n for i in range(0, (block * step), step):\n self.video.set(cv2.CAP_PROP_POS_FRAMES, self.frame)\n flag, image = self.video.read()\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n # cv2.imshow('image',image)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n if not flag or self.frame_pos == self.video.get(cv2.CAP_PROP_POS_MSEC):\n raise IndexError\n else:\n frame = {'Index': self.frame, 'Image': image}\n frames.append(frame)\n self.frame += step\n self.frame_pos = self.video.get(cv2.CAP_PROP_POS_MSEC)\n return frames\n\n\n def get_frame(self, nb):\n flag = False\n dic = {}\n while flag == False:\n self.video.set(cv2.CAP_PROP_POS_FRAMES, self.frame + nb - 1)\n flag, image = self.video.read()\n if not flag:\n raise IndexError\n else:\n name = str(self.total_frames) + str(self.camera) + \".\" + str(self.frame)\n dic = {'Frame' : int(self.frame), 'Name' : name, 'Image': image}\n self.frame += nb\n break\n return dic\n\n\n def stop(self):\n self.video.release()\n","sub_path":"Sources/Common/Video.py","file_name":"Video.py","file_ext":"py","file_size_in_byte":2480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"67670833","text":"import pandas as pd\r\nfrom strsimpy.jaro_winkler import JaroWinkler\r\njarowinkler = JaroWinkler()\r\n\r\ndf_species=pd.read_csv('../Datasets/species.csv', usecols=['Park Name', 'Common Names', 'Category', 'Occurrence', 'Record Status'])\r\n\r\n# -----------STRUCTURED DATA PREPROCESSING--------------\r\ndf_species=df_species[df_species[\"Record Status\"]==\"Approved\"]\r\ndf_species=df_species[df_species[\"Occurrence\"]==\"Present\"]\r\n\r\ndf_species['Common Names']=df_species['Common Names'].str.split(\",\")\r\ndf_species['Common Names']= df_species['Common Names'].map(lambda x: sorted(list(x))[0].strip())\r\n\r\n\r\n\r\n\r\ndf_species.loc[df_species['Common Names'].str.split().str.len() > 1, 'Common Names'] = df_species['Common Names'].str.split().str[-1]\r\n\r\ndf_species.drop_duplicates(inplace=True)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nuniq_np_sp=set(df_species['Park Name'].unique())\r\n\r\ndf_final=pd.read_csv('../Datasets/er_alltrails.csv')\r\nuniq_np_fin=set(df_final['national_park'].unique())\r\n\r\nintersec=uniq_np_sp.intersection(uniq_np_fin)\r\n\r\ndiff=uniq_np_sp.difference(intersec)\r\n\r\n# df_species['Park Name'].replace(\"Sequoia and Kings Canyon National Parks\", \"Sequoia National Park\", inplace=True)\r\n# a=sorted(uniq_np_fin)\r\n# df_species['Park Name'].replace(\"Katmai National Park and Preserve\", \"Katmai National Park\", inplace=True)\r\n\r\ndf_species['Park Name'].replace(\"Wrangell - St Elias National Park and Preserve\", \"Wrangell–St. Elias National Park and Preserve\", inplace=True)\r\n\r\n# df_species['Park Name'].replace(\"Denali National Park and Preserve\", \"Denali National Park, Alaska\", inplace=True)\r\n\r\n\r\n# -------ENTITY RESOLUTION-----------\r\ner_dict={}\r\nfor i in list(diff):\r\n pn=i.split()[0]\r\n for j in list(df_final['national_park'].unique()):\r\n if pn==j.split()[0]: #BLOCKING\r\n sim=jarowinkler.similarity(i,j)\r\n if i not in er_dict.keys():\r\n er_dict[i]=(j,sim)\r\n elif er_dict[i][1]my_thres:\r\n df_species['Park Name'].replace(key,value[0], inplace=True)\r\n\r\n\r\n# REMOVING THOSE NP NOT IN ALLTRAILS\r\nfor np in diff:\r\n df_species.drop(df_species.loc[df_species['Park Name']==np].index, inplace=True)\r\n\r\ndf_species.rename(columns={\"Park Name\":\"national_park\"}, inplace=True)\r\n\r\n\r\n\r\n\r\ndf_species.reset_index(inplace=True)\r\ndf_species.drop('index', axis=1, inplace=True)\r\n\r\ndf_species.to_csv('../Datasets/er_struct.csv', index=False) \r\n","sub_path":"Part2_DataCleaning_ER_IE/er_struct.py","file_name":"er_struct.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"571588944","text":"import collections\n\ndef tablePrint(data):\n for i, row in enumerate(data):\n for j, value in enumerate(row):\n print('%-8s' % round(value,3), end = ' ')\n print('\\n')\n \ndef dumpclean(obj):\n if type(obj) == dict or type(obj) == collections.OrderedDict:\n for k, v in obj.items():\n if hasattr(v, '__iter__'):\n print(k)\n dumpclean(v)\n else:\n print('%s : %-8s' % (k[:3], round(v,3)), end=\"\")\n print('\\n')\n elif type(obj) == list:\n for v in obj:\n if hasattr(v, '__iter__'):\n dumpclean(v)\n else:\n print(v)\n print('\\n')\n else:\n print(round(obj,3))\n \ndef objectListPrint(data):\n for i, row in enumerate(data):\n print(row)\n","sub_path":"hu/farago/eum2/calculator/Helper.py","file_name":"Helper.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"650277529","text":"import os\r\nfrom flask import Blueprint, Flask, render_template\r\nfrom flask import request\r\nfrom werkzeug.utils import secure_filename\r\nimport re\r\n\r\npdfsearch=Blueprint(\"pdfsearch\", __name__, static_folder=\"static\", template_folder=\"templates\")\r\n\r\nimport torch\r\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\r\n\r\nfrom pdfminer.high_level import extract_pages\r\nfrom pdfminer.layout import LTTextContainer\r\nfrom collections import OrderedDict\r\n\r\nname = \"mrm8488/bert-small-finetuned-squadv2\"\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(name,)\r\n\r\nmodel = AutoModelForQuestionAnswering.from_pretrained(name)\r\n\r\nmodel.to('cuda')\r\n\r\ndef answer_question(question, answer_text):\r\n '''\r\n Takes a `question` string and an `answer` string and tries to identify \r\n the words within the `answer` that can answer the question. Prints them out.\r\n '''\r\n \r\n # tokenize the input text and get the corresponding indices\r\n token_indices = tokenizer.encode(question, answer_text)\r\n\r\n # Search the input_indices for the first instance of the `[SEP]` token.\r\n sep_index = token_indices.index(tokenizer.sep_token_id)\r\n\r\n seg_one = sep_index + 1\r\n\r\n # The remainders lie in the second segment.\r\n seg_two = len(token_indices) - seg_one\r\n \r\n # Construct the list of 0s and 1s.\r\n segment_ids = [0]*seg_one + [1]*seg_two\r\n\r\n # get the answer for the question\r\n start_scores, end_scores = model(torch.tensor([token_indices]), # The tokens representing our input combining question and answer.\r\n token_type_ids=torch.tensor([segment_ids])) # The segment IDs to differentiate question from answer\r\n\r\n # Find the tokens with the highest `start` and `end` scores.\r\n answer_begin = torch.argmax(start_scores)\r\n answer_end = torch.argmax(end_scores)\r\n\r\n # Get the string versions of the input tokens.\r\n indices_tokens = tokenizer.convert_ids_to_tokens(token_indices)\r\n \r\n answer = indices_tokens[answer_begin:answer_end+1]\r\n #remove special tokens\r\n answer = [word.replace(\"▁\",\"\") if word.startswith(\"▁\") else word for word in answer] #use this when using model \"twmkn9/albert-base-v2-squad2\"\r\n answer = \" \".join(answer).replace(\"[CLS]\",\"\").replace(\"[SEP]\",\"\").replace(\" ##\",\"\")\r\n \r\n return answer\r\n\r\nclass DocumentReader:\r\n def __init__(self, pretrained_model_name_or_path='mrm8488/bert-small-finetuned-squadv2'):\r\n self.READER_PATH = pretrained_model_name_or_path\r\n self.tokenizer = AutoTokenizer.from_pretrained(self.READER_PATH)\r\n self.model = AutoModelForQuestionAnswering.from_pretrained(self.READER_PATH)\r\n self.model = self.model.to('cuda')\r\n self.max_len = self.model.config.max_position_embeddings\r\n self.chunked = False\r\n\r\n def tokenize(self, question, text):\r\n self.inputs = self.tokenizer.encode_plus(question, text, add_special_tokens=True, return_tensors=\"pt\").to('cuda')\r\n self.input_ids = self.inputs[\"input_ids\"].tolist()[0]\r\n\r\n if len(self.input_ids) > self.max_len:\r\n self.inputs = self.chunkify()\r\n self.chunked = True\r\n\r\n def chunkify(self):\r\n \"\"\" \r\n Break up a long article into chunks that fit within the max token\r\n requirement for that Transformer model. \r\n\r\n Calls to BERT / RoBERTa / ALBERT require the following format:\r\n [CLS] question tokens [SEP] context tokens [SEP].\r\n \"\"\"\r\n\r\n # create question mask based on token_type_ids\r\n # value is 0 for question tokens, 1 for context tokens\r\n qmask = self.inputs['token_type_ids'].lt(1)\r\n qt = torch.masked_select(self.inputs['input_ids'], qmask)\r\n chunk_size = self.max_len - qt.size()[0] - 1 # the \"-1\" accounts for\r\n # having to add an ending [SEP] token to the end\r\n\r\n # create a dict of dicts; each sub-dict mimics the structure of pre-chunked model input\r\n chunked_input = OrderedDict()\r\n for k,v in self.inputs.items():\r\n q = torch.masked_select(v, qmask)\r\n c = torch.masked_select(v, ~qmask)\r\n chunks = torch.split(c, chunk_size)\r\n \r\n for i, chunk in enumerate(chunks):\r\n if i not in chunked_input:\r\n chunked_input[i] = {}\r\n\r\n thing = torch.cat((q, chunk))\r\n if i != len(chunks)-1:\r\n if k == 'input_ids':\r\n thing = torch.cat((thing, torch.tensor([102]).to('cuda')))\r\n else:\r\n thing = torch.cat((thing, torch.tensor([1]).to('cuda')))\r\n\r\n chunked_input[i][k] = torch.unsqueeze(thing, dim=0)\r\n return chunked_input\r\n\r\n def get_answer(self):\r\n if self.chunked:\r\n answer = ''\r\n for k, chunk in self.inputs.items():\r\n a = self.model(**chunk)\r\n\r\n\r\n answer_start_scores = a[0]\r\n answer_end_scores = a[1]\r\n answer_start = torch.argmax(answer_start_scores)\r\n answer_end = torch.argmax(answer_end_scores) + 1\r\n\r\n ans = self.convert_ids_to_string(chunk['input_ids'][0][answer_start:answer_end])\r\n if ans != '[CLS]':\r\n answer += ans + \" \"\r\n return answer\r\n else:\r\n a = self.model(**self.inputs)\r\n\r\n\r\n answer_start_scores = a[0]\r\n answer_end_scores = a[1] \r\n answer_start = torch.argmax(answer_start_scores) # get the most likely beginning of answer with the argmax of the score\r\n answer_end = torch.argmax(answer_end_scores) + 1 # get the most likely end of answer with the argmax of the score\r\n \r\n return self.convert_ids_to_string(self.inputs['input_ids'][0][\r\n answer_start:answer_end])\r\n\r\n def convert_ids_to_string(self, input_ids):\r\n return self.tokenizer.convert_tokens_to_string(self.tokenizer.convert_ids_to_tokens(input_ids))\r\n\r\nreader = DocumentReader(\"deepset/bert-base-cased-squad2\") \r\n\r\n@pdfsearch.route('/uploader', methods = ['GET', 'POST']) \r\n@pdfsearch.route('/pdfsearch', methods=['GET', 'POST'])\r\ndef index():\r\n \r\n if request.method == 'POST':\r\n f = request.files['file']\r\n f.filename = \"sample.pdf\"\r\n f.save(f.filename)\r\n form = request.form\r\n result = []\r\n # bert_abstract = form['paragraph']\r\n question = form['question']\r\n text = \"\"\r\n for page_layout in extract_pages(\"sample.pdf\"):\r\n for element in page_layout:\r\n if isinstance(element, LTTextContainer):\r\n res = element.get_text()\r\n res1 = re.sub(r'[^\\w\\s]', ' ', str(res))\r\n res1 = re.sub(r\"^\\s+|\\s+$\", \"\", res1) # leading and trailing spaces\r\n res1 = re.sub(' +', ' ', res1) #removing multiple spaces\r\n text+=res1\r\n reader.tokenize(question,text)\r\n result.append(question)\r\n result.append(reader.get_answer())\r\n return render_template(\"pdfsearch.html\",result = result)\r\n\r\n return render_template(\"pdfsearch.html\")\r\n","sub_path":"pdfsearch.py","file_name":"pdfsearch.py","file_ext":"py","file_size_in_byte":7173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"420985441","text":"from queue import Queue\n\nclass Node:\n\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n\nclass BinaryTree:\n def __init__(self, root = None):\n self.root = root\n\n def add(self, value):\n if self.root == None:\n self.root = Node(value)\n return\n cur_node = self.root\n while cur_node:\n\n #Go left\n if value == cur_node.value:\n return\n elif value < cur_node.value:\n if cur_node.left:\n cur_node = cur_node.left\n else:\n cur_node.left = Node(value)\n return\n else:\n if cur_node.right:\n cur_node = cur_node.right\n else:\n cur_node.right = Node(value)\n return\n \n def delete(self, value):\n cur_node = self.root\n self.root = self.delete_node(self.root, value)\n\n def delete_node(self, node, value):\n if node == None:\n return None\n\n # go left\n if value < node.value:\n node.left = self.delete_node(node.left, value)\n\n # go right\n elif value > node.value:\n node.right = self.delete_node(node.right, value)\n\n # this is the node to delete\n else:\n\n #if just has one child then put it in its place\n if node.left == None:\n node = node.right\n return node\n elif node.right == None:\n node = node.left\n return node\n\n #if has two children, then replace with smallest node above it\n min_value = self.min_value(node.right)\n node.value = min_value\n node.right = self.delete_node(node.right, min_value)\n return node\n\n def min_value(self, node):\n if node == None:\n return None\n while node.left:\n node = node.left\n return node.value \n\n def search(self, value):\n cur_node = self.root\n while cur_node:\n if cur_node.value == value:\n return True\n if value < cur_node.value:\n cur_node = cur_node.left\n else:\n cur_node = cur_node.right\n return False\n\n def level_order(self):\n queue = Queue()\n cur_node = self.root\n while cur_node:\n print(cur_node.value)\n if cur_node.left:\n queue.enqueue(cur_node.left)\n if cur_node.right:\n queue.enqueue(cur_node.right)\n cur_node = queue.dequeue()\n\n\n def in_order(self):\n if self.root:\n self.in_order_recur(self.root)\n\n def in_order_recur(self, node):\n if node.left:\n self.in_order_recur(node.left)\n print(node.value)\n if node.right:\n self.in_order_recur(node.right)\n\n def pre_order(self):\n if self.root:\n self.pre_order_recur(self.root)\n\n def pre_order_recur(self, node):\n print(node.value)\n if node.left:\n self.pre_order_recur(node.left)\n if node.right:\n self.pre_order_recur(node.right)\n\n def post_order(self):\n if self.root:\n self.pre_order_recur(self.root)\n\n def post_order_recur(self, node):\n if node.left:\n self.post_order_recur(node.left)\n if node.right:\n self.post_order_recur(node.right)\n print(node.value)\n\nif __name__ == '__main__':\n tree = BinaryTree()\n\n import numpy as np\n np.random.seed(1)\n random_nums = np.random.randint(0,20,size=(20,))\n for each in random_nums:\n tree.add(each)\n\n print('Level order traversal')\n tree.level_order()\n\n print('In order traversal')\n tree.in_order()\n\n print('\\nPre order traversal')\n tree.pre_order()\n\n print('\\nPost order traversal')\n tree.post_order()\n\n print(tree.search(5))\n print(tree.search(22))\n\n tree.delete(5)\n tree.delete(11)\n tree.delete(6)\n tree.delete(22)\n tree.delete(9)\n print('\\nIn order after deleting 5, 6, 9, 11')\n tree.in_order()\n\n for each in random_nums:\n tree.delete(each)\n\n print('\\nIn order after deleting all numbers')\n tree.in_order()\n","sub_path":"python/data_structures_algorithms/binary_search_tree.py","file_name":"binary_search_tree.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"310064380","text":"import json\nimport Algorithmia\nimport os\nimport traceback\n\nALGORITHMIA_KEY = os.environ.get('ALGORITHMIA', False)\nALGORITHMIA_DATA_STORE_LOCATION =\\\n os.environ.get('ALGORITHMIA_DATA_STORE_LOCATION', False)\n\n\ndef run():\n f = open('../data/whisky.json', 'r')\n raw_json = f.read()\n f.close()\n\n whisky = json.loads(raw_json)\n\n output = []\n\n for url, data in whisky.iteritems():\n output.append(data['description'])\n\n input = [output, \"xxBeGiN142xx\", \"xxEnD142xx\",\n ALGORITHMIA_DATA_STORE_LOCATION]\n\n client = Algorithmia.client(ALGORITHMIA_KEY)\n\n try:\n algo = client.algo(\n 'ngram/GenerateTrigramFrequencies/0.1.1')\n except Exception as exception:\n print(traceback.format_exc(exception))\n print(\"Unable to upload data.\")\n\n\nif __name__ == \"__main__\":\n\n if not ALGORITHMIA_KEY:\n print('Please export your Algorithmia key using' +\n '\"export ALGORITHMIA=MY_KEY\"')\n run()\n","sub_path":"generate_trigram_data.py","file_name":"generate_trigram_data.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"335613950","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Date: 2019-07-15\n\nimport numpy as np \nfrom matplotlib import pyplot as plt\n\npi = np.pi\nsin = np.sin\ncos = np.cos\n\nt_min = -2*pi; t_max = 2*pi; t_steps = 200\nt = np.linspace( t_min, t_max, t_steps+1 )\n# sin and cos function with period T=5\nx1 = sin( 2*pi*t/5 )\nx2 = cos( 2*pi*t/5 )\n\nlines = plt.plot( t, x1 )\nplt.setp( lines, 'linewidth', 2.0, 'label', r'$sin(\\frac{2\\pi}{5} t)$' )\nlines = plt.plot( t, x2 )\nplt.setp( lines, 'linewidth', 2.0, 'label', r'$cos(\\frac{2\\pi}{5} t)$' )\nplt.xlabel( 'Time t' )\nplt.ylabel( 'x(t)' )\nplt.axis( [t_min, t_max, -1.25, 1.25] )\nplt.grid( True )\nplt.legend( bbox_to_anchor=(0.95, 1.1), loc='upper right' )\nplt.savefig( 'plot_6.png' )\n#plt.savefig( 'plot_6.pdf', format='pdf', bbox_inches='tight' )\nplt.show()\n\n\n##############################################################################\n","sub_path":"func_plot-2/test_plot-6.py","file_name":"test_plot-6.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"472742854","text":"# -*- coding: utf-8 -*-\n# prog-2-05.py\n\nimport pandas as pd\nimport numpy as np\nimport re\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\n# ニューラルネットワークの構築のためにインポート\nimport tensorflow as tf\nimport keras\nfrom keras.utils.np_utils import to_categorical\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Embedding, Input, Flatten\nfrom tensorflow.keras.layers import Activation, Reshape\nfrom tensorflow.keras.layers import Dense, Concatenate, Dropout\nfrom keras.layers.merge import concatenate\nfrom tensorflow.keras.optimizers import Adam\n# 可視化用にインポート\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\n# 散布図のテキストラベル位置調整のため\nget_ipython().system('pip3 install adjustText')\nfrom adjustText import adjust_text\n# ラベルエンコーディングのためにインストール\nget_ipython().system('pip install category_encoders')\n# category_encodersをインポート\nimport category_encoders as cate_enc\nimport collections as colle\n\n# データの準備\ndef prepare():\n get_ipython().system('unzip travel-insurance.zip')\n\n# エンティティ埋め込みクラス\nclass EntityEmbedder:\n def __init__(self, input_dims, emb_dims, output_dim):\n # 各特徴量の入力次元数\n self.dims = input_dims\n # 各特徴量の埋め込み次元数\n self.embdims = emb_dims\n # 出力次元数（ラベルの種類数）\n self.output_dim = output_dim\n self.dropout_rate = 0.2\n self.activation = 'relu'\n self.optimizer = 'Adam'\n self.loss = 'binary_crossentropy'\n self.weights = None\n self.buildEmbModel()\n \n # モデルの構築(ラベルエンコーディング後のデータを入力）\n def buildEmbModel(self):\n inputs, embeds = [], []\n for i, (input_dim, emb_dim) in enumerate(zip(self.dims, self.embdims)):\n input_c = Input(shape=(1,), name='in{}'.format(i+1))\n # 埋め込み(Embedding)層の定義\n embed = Embedding(input_dim=input_dim, \n output_dim=emb_dim, \n input_length=None, \n name='emb{}'.format(i+1))(input_c)\n output = Reshape(\\\n target_shape=(emb_dim,))(embed)\n inputs.append(input_c) \n embeds.append(output)\n # 埋め込み層の出力を連結する\n out = Concatenate(name='conc_layer', axis=-1)(embeds) \n out = Dropout(self.dropout_rate)(out) \n # 隠れ層のユニット数\n hd = [8]\n for n in range(len(hd)): \n out = Dense(hd[n])(out) \n out = Activation(self.activation)(out)\n out = Dense(self.output_dim)(out)\n out = Activation('softmax')(out)\n self.model = Model(inputs=inputs, outputs=out)\n self.model.compile(optimizer=self.optimizer, \n loss=self.loss,\n metrics=['accuracy'])\n self.model.summary()\n\n # 学習を行うメソッド（入力ベクトルは特徴量の数だけある）\n def fit(self, X, y, epochs=30, shuffle=True, batch_size=5):\n self.model.fit( X, y, \n epochs = epochs, shuffle=shuffle, batch_size=batch_size, verbose=1)\n # 学習済みネットワークの重みを格納\n self.weights = self.model.get_weights()\n # 埋め込み層の出力を連結したベクトルを取得するための\n # メソッドを定義する\n inputs = [self.model.get_layer('in%d' % (i+1)).input for i in range(len(self.dims))] \n # 埋め込み層からの出力を連結する層の出力を取得\n self.get_hidden_out = Model(inputs=self.model.inputs, outputs=self.model.get_layer('conc_layer').output)\n\n # 順序エンコーディングベクトルovから\n # エンティティ埋め込みベクトルを取得して返す\n def get_vector(self, ov):\n vec = self.get_hidden_out(ov)\n return vec\n\n # 特徴量（列）番号(fid)とカテゴリーID(cid)を渡すと、\n # そのカテゴリの特徴量ベクトルを返す\n def get_embedding(self, fid, cid):\n emb = self.weights[fid][cid, :]\n return emb\n\n# クラスごとのデータ数をそろえる\ndef resampling(newX, y, lim, labels):\n fc = [0] * len(labels)\n nX, nY = [], []\n for i in range(len(y)):\n if fc[y[i]] == lim:\n continue\n fc[y[i]] += 1\n nX.append(newX[i])\n nY.append(y[i])\n return nX, nY\n\n# 前処理\n# データフレームの作成、クラスの偏りを修正\ndef preprocess():\n # 旅行者のクレームの有無のデータ\n df = pd.read_csv('travel insurance.csv', encoding='utf-8')\n print(df)\n features = ['Agency','Agency Type', 'Distribution Channel', \n 'Product Name', 'Destination']\n labels = [0,1]\n target_names=['No', 'Yes']\n df['Claim'].replace({'Yes':1, 'No':0}, inplace=True)\n # データの少ないクラスに合わせるため、\n # 各クラスのうち少数派クラスのデータ数をlimに格納\n y_bool = df['Claim'] == 1\n n_bool = df['Claim'] == 0\n lim = y_bool.sum()\n if y_bool.sum() > n_bool.sum():\n lim = n_bool.sum()\n y = df['Claim'].values\n df.drop('Claim', axis=1, inplace=True)\n df = pd.DataFrame(df, columns=features)\n df.fillna('N')\n n_features = len(df.columns)\n print('Num of Features {}'.format(n_features))\n return df, y, lim, labels, features, target_names\n\n# ラベルエンコーディング\ndef ordinal_encoding(df, features, lim, y, labels, encoder):\n input_dims = []\n newX = np.array([])\n # ラベルエンコーディングのクラスインスタンスを生成\n if encoder == None:\n encoder = cate_enc.OrdinalEncoder(cols=features, handle_unknown='value', handle_missing='value')\n df_enc = encoder.fit_transform(df)\n else:\n df_enc = encoder.fit_transform(df)\n newX = df_enc.values\n dl = {}\n for i in range(len(newX)):\n n = 0\n for j in range(len(newX[i])):\n if not n in dl:\n dl[n] = []\n dl[n].append(newX[i][j])\n n+=1\n for n,v in dl.items():\n cnt = colle.Counter(v)\n mc = cnt.most_common()\n kinds = len(mc)\n input_dims.append(kinds)\n # クラスの偏りを無くすために少数派クラスの\n # 件数limに揃える\n nX, nY = resampling(newX, y, lim, labels)\n # カテゴリのIDを0から開始するように変換する\n nX = np.array(nX)\n nX = np.reshape(nX, (len(nX), len(nX[0]),))\n nY = np.array(nY)\n nY = np.reshape(nY, (len(nY), 1, ))\n nX = np.asarray(list(map(lambda x: x-1, nX)))\n return nX, nY, input_dims, encoder\n\ndef conv_form(X, input_dims):\n nX = []\n for i,id in enumerate(input_dims):\n x = np.asarray(X[:,i], dtype=np.int32)\n x = np.asarray([j for j in x],\\\n dtype=np.int32).reshape((len(x),1))\n nX.append(x)\n return nX\n\n# エンティティ埋め込みの学習\ndef convertByEntityEmbedding(X_train, y_train, X_test, y_test, labels, input_dims):\n y_train = to_categorical(y_train, num_classes=len(labels))\n X_train = np.array(X_train)\n X_train = conv_form(X_train, input_dims)\n y_test = to_categorical(y_test, num_classes=len(labels))\n X_test = np.array(X_test)\n X_test = conv_form(X_test, input_dims)\n\n # 埋め込みベクトルの次元数を2に設定する\n emb_dims = []\n for id in input_dims:\n emb_dims.append(2)\n output_dim = len(labels)\n ee = EntityEmbedder(input_dims, emb_dims, output_dim)\n # epochs = 7 で学習\n ee.fit(X_train, y_train, epochs=7)\n # 学習したモデルから、埋め込みベクトルを取得する\n x_trainvect = ee.get_vector(X_train)\n x_testvect = ee.get_vector(X_test)\n return x_trainvect, x_testvect, y_train, y_test, ee\n\n# SVMによる評価(エンティティ埋め込み有り)\ndef predict_by_SVM(x_trainvect, x_testvect, y_train, y_test, target_names):\n y_train_new, y_test_new = [], []\n i = 0\n for yf in y_train:\n y_train_new.append(np.argmax(yf))\n for yf in y_test:\n y_test_new.append(np.argmax(yf))\n svm = SVC()\n svm.fit(x_trainvect, y_train_new)\n y_pred = svm.predict(x_testvect)\n print('---With entity embedding---')\n print(classification_report(y_test_new, y_pred,target_names=target_names))\n\n# SVMによる評価(エンティティ埋め込み無し)\ndef predict_by_SVM_withoutEE(X_train, X_test, y_train, y_test, target_names):\n y_train_new, y_test_new = [], []\n X_train = np.reshape(X_train, (len(X_train), len(X_train[0])))\n y_train = np.reshape(y_train, (len(y_train) ))\n X_test = np.reshape(X_test, (len(X_test), len(X_test[0])))\n y_test = np.reshape(y_test, (len(y_test) ))\n svm = SVC()\n svm.fit(X_train, y_train)\n y_pred = svm.predict(X_test)\n print('---Without entity embedding---')\n print(classification_report(y_test, y_pred, target_names=target_names))\n\n# エンティティ埋め込み結果を可視化\ndef makeGraph(data, texts, cate):\n if len(data) > 20:\n p = np.random.permutation(len(data))\n data = data[p[:20]]\n texts = texts[p[:20]]\n for (dim1,dim2,label) in zip(data[:,0], data[:,1], texts):\n plt.plot(dim1, dim2, '.' )\n ptxt = [plt.text(x, y, lb, ha='center', va='center') for x,y,lb in zip(data[:,0], data[:,1], texts)]\n adjust_text(ptxt, arrowprops=dict(arrowstyle='->', color='blue'))\n cate = re.sub(r'\\s+', '_', cate)\n plt.title('2D plot of feature: {}'.format(cate))\n plt.savefig('./data-fig_{}.png'.format(cate), dpi=400)\n plt.show()\ndef main():\n prepare()\n df, y, lim, labels, features, target_names = preprocess()\n # ラベルエンコーディングしたベクトル形式に変換\n X_train, X_test, y_train, y_test = train_test_split(\n \t\t\t\t\tdf.loc[:,features].values, y, train_size=0.9, random_state=10)\n df_train = pd.DataFrame(X_train, columns=features)\n cc = [0, 0]\n for yv in y_train:\n cc[yv] += 1\n lim_train = np.min(cc)\n X_train, y_train, input_dims_train, enc = ordinal_encoding(\n df_train, features, lim_train, y_train, labels, None)\n df_test = pd.DataFrame(X_test, columns=features)\n cc = [0, 0]\n for yv in y_test:\n cc[yv] += 1\n lim_test = np.min(cc)\n X_test, y_test, input_dims, _ = ordinal_encoding(\n df_test, features, \n lim_test, y_test, labels, enc)\n \n # エンティティ埋め込み無しで、SVMによる予測\n predict_by_SVM_withoutEE(X_train, X_test, \\\n y_train, y_test, target_names)\n # カテゴリ特徴量をラベルエンコーディングしたデータを\n # エンティティ埋め込みベクトルに変換するために\n # 教師あり学習を行い、エンティティ埋め込みベクトルを取得\n x_trainvect, x_testvect, y_train, y_test, ee = convertByEntityEmbedding(\n \t\t\t X_train, y_train, X_test, y_test, labels, input_dims_train)\n # SVMで学習・予測結果の評価\n predict_by_SVM(x_trainvect, x_testvect,\n y_train, y_test, target_names)\n # 各カテゴリの埋め込みベクトルを可視化する\n for i in range(len(features)):\n veclist, texts = [], []\n obm = enc.mapping[i]['mapping']\n for idx, kv in zip(obm.index, obm):\n if kv < 0:\n continue\n embv = ee.get_embedding(i, kv-1)\n veclist.append(embv)\n texts.append(idx)\n veclist = np.asarray(veclist, dtype=np.float32)\n texts = np.asarray(texts)\n makeGraph(veclist, texts, features[i])\n\nif __name__ == '__main__':\n main()","sub_path":"py/prog2-05.py","file_name":"prog2-05.py","file_ext":"py","file_size_in_byte":12058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"168949958","text":"#-Begin-preamble-------------------------------------------------------\n#\n# CERN\n#\n# European Organization for Nuclear Research\n#\n#\n# This file is part of the code:\n#\n# PyECLOUD Version 8.6.0\n#\n#\n# Main author: Giovanni IADAROLA\n# BE-ABP Group\n# CERN\n# CH-1211 GENEVA 23\n# SWITZERLAND\n# giovanni.iadarola@cern.ch\n#\n# Contributors: Eleonora Belli\n# Philipp Dijkstal\n# Lorenzo Giacomel\n# Lotta Mether\n# Annalisa Romano\n# Giovanni Rumolo\n# Eric Wulff\n#\n#\n# Copyright CERN, Geneva 2011 - Copyright and any other\n# appropriate legal protection of this computer program and\n# associated documentation reserved in all countries of the\n# world.\n#\n# Organizations collaborating with CERN may receive this program\n# and documentation freely and without charge.\n#\n# CERN undertakes no obligation for the maintenance of this\n# program, nor responsibility for its correctness, and accepts\n# no liability whatsoever resulting from its use.\n#\n# Program and documentation are provided solely for the use of\n# the organization to which they are distributed.\n#\n# This program may not be copied or otherwise distributed\n# without permission. This message must be retained on this and\n# any other authorized copies.\n#\n# The material cannot be sold. CERN should be given credit in\n# all references.\n#\n#-End-preamble---------------------------------------------------------\n\nfrom numpy import sqrt, exp, take\nfrom numpy.random import rand\nimport numpy as np\nfrom .sec_emission_model_ECLOUD import SEY_model_ECLOUD\nfrom scipy.constants import e as qe\n\ndef yield_fun2(E, costheta, Emax, del_max, R0, E0):\n\n s = 1.35\n\n del_max_tilde = del_max * exp(0.5 * (1. - costheta))\n E_max_tilde = Emax * (1. + 0.7 * (1. - costheta))\n\n x = E / E_max_tilde\n\n true_sec = del_max_tilde * (s * x) / (s - 1. + x**s)\n reflected = R0 * ((sqrt(E) - sqrt(E + E0)) / (sqrt(E) + sqrt(E + E0)))**2.\n\n delta = true_sec + reflected\n ref_frac = 0. * delta\n mask_non_zero = (delta > 0)\n ref_frac[mask_non_zero] = reflected[mask_non_zero] / delta[mask_non_zero]\n\n return delta, ref_frac\n\n\nclass SEY_model_ECLOUD_non_unif(SEY_model_ECLOUD):\n \n def __init__(self, chamb, Emax, del_max, R0, E0=150.,\n E_th=None, sigmafit=None, mufit=None,\n switch_no_increase_energy=0, thresh_low_energy=None, secondary_angle_distribution=None,\n ):\n\n if chamb.chamb_type != 'polyg':\n raise ValueError(\"\"\"ECLOUD_nunif can be used only with chamb_type='polyg'!!!\"\"\")\n\n self.E_th = E_th\n self.sigmafit = sigmafit\n self.mufit = mufit\n self.switch_no_increase_energy = switch_no_increase_energy\n self.thresh_low_energy = thresh_low_energy\n self.secondary_angle_distribution = secondary_angle_distribution\n\n if secondary_angle_distribution is not None:\n from . import electron_emission\n self.angle_dist_func = electron_emission.get_angle_dist_func(secondary_angle_distribution)\n else:\n self.angle_dist_func = None\n\n self.del_max_segments = np.float_(chamb.del_max_segments)\n self.R0_segments = np.float_(chamb.R0_segments)\n self.Emax_segments = np.float_(chamb.Emax_segments)\n\n self.del_max_segments[chamb.del_max_segments < 0.] = del_max\n self.R0_segments[chamb.R0_segments < 0.] = R0\n self.Emax_segments[chamb.Emax_segments < 0.] = Emax\n\n self.E0 = E0\n\n print('Secondary emission model: ECLOUD non uniform E0=%f'%self.E0)\n\n def SEY_process(self, nel_impact, E_impact_eV, costheta_impact, i_impact):\n\n Emax_mp = take(self.Emax_segments, i_impact)\n del_max_mp = take(self.del_max_segments, i_impact)\n R0_mp = take(self.R0_segments, i_impact)\n\n yiel, ref_frac = yield_fun2(E_impact_eV, costheta_impact, Emax_mp, del_max_mp, R0_mp, E0=self.E0)\n flag_elast = (rand(len(ref_frac)) < ref_frac)\n flag_truesec = ~(flag_elast)\n nel_emit = nel_impact * yiel\n\n return nel_emit, flag_elast, flag_truesec\n\n\nclass SEY_model_ECLOUD_non_unif_charging(SEY_model_ECLOUD_non_unif):\n \n def __init__(self, chamb, Emax, del_max, R0, E0=150.,\n E_th=None, sigmafit=None, mufit=None,\n switch_no_increase_energy=0, thresh_low_energy=None, secondary_angle_distribution=None, \n ):\n\n super(SEY_model_ECLOUD_non_unif_charging, self).__init__(chamb, Emax, del_max, R0, E0,\n E_th, sigmafit, mufit,\n switch_no_increase_energy, thresh_low_energy, secondary_angle_distribution, \n )\n print('Secondary emission model: ECLOUD non uniform E0=%f, with charging'%self.E0)\n \n self.chamb = chamb\n self.Q_segments = 0. * self.del_max_segments\n self.flag_charging = np.int_(chamb.flag_charging)>0\n self.Q_max_segments = np.float_(chamb.Q_max_segments)\n self.EQ_segments = np.float_(chamb.EQ_segments)\n self.tau_segments = np.float_(chamb.tau_segments)\n\n\n def SEY_process(self, nel_impact, E_impact_eV, costheta_impact, i_impact):\n \n Emax_mp = take(self.Emax_segments, i_impact)\n del_max_mp = take(self.del_max_segments, i_impact)\n R0_mp = take(self.R0_segments, i_impact)\n\n yiel, ref_frac = yield_fun2(E_impact_eV, costheta_impact, Emax_mp, del_max_mp, R0_mp, E0=self.E0)\n \n mask_charging = np.take(self.flag_charging, i_impact) \n if np.any(mask_charging):\n Q_charging = np.take(self.Q_segments, i_impact[mask_charging])\n Q_max = np.take(self.Q_max_segments, i_impact[mask_charging])\n EQ = np.take(self.EQ_segments, i_impact[mask_charging])\n \n Q_charging[Q_charging<0.] = 0.\n Q_charging[Q_charging>Q_max] = Q_max[Q_charging>Q_max]\n \n yiel[mask_charging] = yiel[mask_charging] * (1. - Q_charging/Q_max) + (1. - np.exp(-E_impact_eV[mask_charging]/EQ))*(Q_charging/Q_max)\n \n # This would preserve also the ener\n # ref_frac[mask_charging] = ref_frac[mask_charging] * (1. - Q_charging/Q_max) + Q_charging/Q_max\n \n flag_elast = (rand(len(ref_frac)) < ref_frac)\n flag_truesec = ~(flag_elast)\n \n nel_emit = nel_impact * yiel\n\n for i_edg, flag_Q in enumerate(self.flag_charging):\n\n if flag_Q:\n mask_impact_here = (i_impact == i_edg)\n n_impact_here = np.sum(nel_impact[mask_impact_here])\n n_emit_here = np.sum(nel_emit[mask_impact_here])\n\n self.Q_segments[i_edg] += (n_impact_here - n_emit_here)*(-qe)/self.chamb.L_edg[i_edg]\n\n return nel_emit, flag_elast, flag_truesec\n\n def SEY_model_evol(self, Dt): \n mask_evol = np.logical_and(self.flag_charging, self.tau_segments>0.)\n self.Q_segments[mask_evol]*=np.exp(-Dt/self.tau_segments[mask_evol])\n\n\n\n\n\n\n","sub_path":"sec_emission_model_ECLOUD_nunif.py","file_name":"sec_emission_model_ECLOUD_nunif.py","file_ext":"py","file_size_in_byte":7500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"319605811","text":"import numpy\nfrom PIL import Image\nfrom OpenGL.GL import (\n glGenTextures, glBindTexture, GL_TEXTURE_2D, glPixelStorei, glDeleteTextures,\n GL_UNPACK_ALIGNMENT, glTexParameterf, GL_TEXTURE_MIN_FILTER,\n GL_TEXTURE_MAG_FILTER, glTexImage2D, GL_RGBA, GL_UNSIGNED_BYTE, GL_LINEAR)\n\n\nclass TextureType:\n \"\"\"\n TextureType is a class extended by any class intending to be used like\n a Texture or TextureRegion.\n\n Extenders need to make sure these instance variables are set:\n\n :ivar width: The default draw width of the image (normally the width in\n pixels). This is used by SpriteBatch when no draw width is given.\n :ivar height: The default draw height of the image (normally the height in\n pixels). This is used by SpriteBatch when no draw height is given.\n :ivar textureId: This is the OpenGL texture id that contains the image.\n :ivar frame: This is the 'frame' value of an animation. If this is not a\n part of an animation, this should be None.\n :ivar u1: The u-coordinate of the left part of the image.\n :ivar v1: The v-coordinate of the top or bottom of the image. If the camera\n has the y-axis going down (default), v1 should be the top of the image.\n :ivar u2: The u-coordinate of the right part of the image.\n :ivar v2: The v-coordinate of the top or bottom of the image. This should\n be the side opposite of `v1`.\n :ivar widthPercent: A value between 0 and 1 that indicates how much width\n the region of the texture takes of the original. This value is used to\n calculate U values when subdividing a region.\n :ivar heightPercent: A value between 0 and 1 that indicates how much height\n the region of the texture takes of the original. This value is used to\n calculate V values when subdividing a region.\n :ivar xOffset: Offsets this texture when drawn on the image's x-axis.\n :ivar yOffset: Offsets this texture when drawn on the image's y-axis.\n :ivar patches: The patches to use when creating a NinePatch from this\n texture. If this is None, the patches for NinePatch will have to be\n provided manually. This list/tuple should be 4 items in the order of\n left, right, top, and bottom patch.\n \"\"\"\n\n def flip(self):\n \"\"\"Flip the image vertically.\"\"\"\n self.v1, self.v2 = self.v2, self.v1\n\n def subdivide(self, x, y, width, height, safety=True) -> 'sapol.g2d.TextureRegion':\n \"\"\"\n Subdivide this image into a new TextureRegion.\n\n :param x: The x-coordinate of the beginning of the subdivide relative\n to the left of the image.\n :param y: The y-coordinate of the beginning of the subdivide relative\n to the top of the image.\n :param width: The width of the subdivide. This value may be negative\n (resulting in a horizontally flipped selection).\n :param height: The height of the subdivide. This value may be negative\n (resulting in a vertically flipped selection).\n :param safety: Whether or not to allow going beyond the bounds of the\n image.\n :return: The newly created TextureRegion.\n :rtype: TextureRegion\n\n :raise ValueError: If the new subdivide goes beyond the bounds and\n safety is set to true.\n \"\"\"\n if safety:\n if x + width < 0 or x < 0:\n raise RuntimeError(\"Subdivide goes beyond the left of image.\")\n if x + width > self.width or x > self.width:\n raise RuntimeError(\"Subdivide goes beyond the right of image.\")\n if y + height < 0 or y < 0:\n raise RuntimeError(\"Subdivide goes beyond the top of image.\")\n if y + height > self.height or y > self.height:\n raise RuntimeError(\"Subdivide goes beyond the bottom of image.\")\n return TextureRegion(self, x, y, width, height)\n\n\nclass Texture(TextureType):\n \"\"\"\n Texture is the class to load a texture into OpenGL. This texture can\n be from a file or from data already loaded. All parameters are named-only.\n To load a texture you must have one of the following:\n\n :param filename: The path to the file to load.\n\n OR\n\n :param imageData: The image data to load the texture from.\n :param dataWidth: The width of the image data.\n :param dataHeight: The height of the image data.\n\n OR\n\n :param textureId: The id of the already loaded texture.\n :param forcedWidth: See below\n :param forcedHeight: See below\n\n You can also specify any of these parameters by name:\n\n :param forcedWidth: Force the width variable to this size.\n :param forcedHeight: Force the height variable to this size.\n :param minFilter: The OpenGL value for the minimize filter.\n :param magFilter: The OpenGL value for the maximize filter.\n :param flipped: Whether or not the image is flipped vertically. This is\n needed if the y-axis is down (Default: True).\n\n See :class:`TextureType` for instance\n variables.\n \"\"\"\n\n def __init__(self, *, imageData=None, dataWidth=None, dataHeight=None,\n filename=None, textureId=None, forcedWidth=None, forcedHeight=None,\n minFilter: None=GL_LINEAR, magFilter: None=GL_LINEAR,\n flipped=True, patches=None):\n if imageData is not None and filename is not None:\n raise ValueError(\n \"Both imageData and filename parameters given, only one permitted.\")\n self.widthPercent = 1\n self.heightPercent = 1\n self.patches = patches\n self.u1 = 0\n self.u2 = 1\n if not flipped:\n self.v1 = 1\n self.v2 = 0\n else:\n self.v1 = 0\n self.v2 = 1\n\n self.frame = None\n self.xOffset = 0\n self.yOffset = 0\n if filename is not None:\n image = Image.open(filename)\n imageData = numpy.array(list(image.getdata()), numpy.uint8)\n dataWidth = image.width\n dataHeight = image.height\n elif textureId is not None:\n self.textureId = textureId\n\n if forcedWidth is not None:\n self.width = forcedWidth\n else:\n raise ValueError(\"textureId requires forcedWidth parameter.\")\n\n if forcedHeight is not None:\n self.height = forcedHeight\n else:\n raise ValueError(\"textureId requires forcedHeight parameter.\")\n return\n elif imageData is None:\n raise ValueError(\"Texture initialized without imageData, filename,\"\n \" or textureId.\")\n elif dataWidth is None:\n raise ValueError(\"Cannot use imageData without dataWidth parameter.\")\n elif dataHeight is None:\n raise ValueError(\"Cannot use imageData without dataHeight parameter.\")\n\n if forcedWidth is not None:\n self.width = forcedWidth\n else:\n self.width = dataWidth\n\n if forcedHeight is not None:\n self.height = forcedHeight\n else:\n self.height = dataHeight\n\n self.textureId = glGenTextures(1)\n glBindTexture(GL_TEXTURE_2D, self.textureId)\n glPixelStorei(GL_UNPACK_ALIGNMENT, 1)\n glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, minFilter)\n glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, magFilter)\n glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, dataWidth, dataHeight, 0, GL_RGBA,\n GL_UNSIGNED_BYTE, imageData)\n\n def free(self):\n \"\"\"\n Free the image from memory. You can not use the image after freeing\n it. Any TextureRegions or anything relying on the same textureId will\n also no longer work.\n \"\"\"\n glDeleteTextures(self.textureId)\n\n\nclass TextureRegion(TextureType):\n \"\"\"\n TextureRegion contains data for a region along a normal Texture object.\n A TextureRegion allows one to display parts of a texture instead of the\n whole thing. Having one image full of many different regions to draw is\n more efficient than having a separate image for each.\n\n :param texture: The parent TextureType object.\n :param x: The x-coordinate of the left part of the image.\n :param y: The y-coordinate of the top or bottom part of the image. If\n either flipped is True or height is negative, this is the top of the\n image. If both or neither are true, this is the bottom of the image.\n :param width: The width of the new region in pixels.\n :param height: The height of the new region in pixels.\n\n The following parameters may be specified by name:\n\n :param flipped: Whether or not the image should be flipped vertically. If\n None is given, this will default to whether the parent object is flipped.\n :param frame: The frame value of this region. This is used for ordering\n frames in Animation.\n :param xOffset: Offsets this region when drawn on its x-axis.\n :param yOffset: Offsets this region when drawn on its y-axis.\n\n :ivar texture: The original texture object this region is made from.\n\n See :class:`TextureType` for more instance\n variables.\n \"\"\"\n\n def __init__(self, texture, x, y, width, height, *, frame=None,\n flipped=None, xOffset=0, yOffset=0, patches=None):\n self.texture = texture\n self.textureId = texture.textureId\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.frame = frame\n self.xOffset = xOffset\n self.yOffset = yOffset\n self.patches = patches\n\n self.u1 = x / texture.width\n self.v1 = y / texture.height\n self.u2 = (x + width) / texture.width\n self.v2 = (y + height) / texture.height\n self.widthPercent = width / texture.width\n self.heightPercent = height / texture.height\n\n if flipped is None:\n if texture.v1 > texture.v2:\n self.flip()\n elif flipped:\n self.flip()\n\n\nclass NinePatch:\n \"\"\"\n NinePatch splits a texture into 9 different 'patches' which are then stretched\n in different ways to fill an area. The corner patches are not stretched at all.\n The bottom and top middle patches are stretched horizontally. The left and\n right middle patches are stretched vertically. The center patch is stretched\n in both directions. Doing this allows a texture like the background of a\n button to fill an area, without looking weird or having to use a different\n texture for every button.\n\n NinePatch can either accept 1, 5, or 9 arguments:\n\n * If only 1 argument is provided, it is expected to extend TextureType, and\n to have its patches instance variable set.\n * If 5 arguments are provided, it is expected that the first extends TextureType,\n and the next for arugments are the left, right, top, and bottom patches\n in that order.\n * If 9 arguments are provided, it is expected that all nine arguments extend\n TextureType (normally these should be TextureRegions). These will be\n directly made to represent the 9 patches from left->right, top->bottom.\n \"\"\"\n\n def __init__(self, *args):\n if len(args) == 1:\n texture = args[0]\n if texture.patches is None:\n raise RuntimeError(\"To create a patch from a TextureType, it requires\"\n \" the TextureType to have the patches attribute set.\")\n args = (texture, *texture.patches)\n if len(args) == 5:\n texture = args[0]\n width = texture.width\n height = texture.height\n left, right, top, bottom = args[1], args[2], args[3], args[4]\n middleWidth = width - left - right\n middleHeight = height - top - bottom\n bottomOffset = height - bottom\n rightOffset = width - right\n # Patches are created from the upper left to the lower right:\n self._patches = [\n texture.subdivide(0, 0, left, top),\n texture.subdivide(left, 0, middleWidth, top),\n texture.subdivide(rightOffset, 0, right, top),\n texture.subdivide(0, top, left, middleHeight),\n texture.subdivide(left, top, middleWidth, middleHeight),\n texture.subdivide(rightOffset, top, right, middleHeight),\n texture.subdivide(0, bottomOffset, left, bottom),\n texture.subdivide(left, bottomOffset, middleWidth, bottom),\n texture.subdivide(rightOffset, bottomOffset, right, bottom)\n ]\n elif len(args) == 9:\n self._patches = list(args)\n else:\n raise RuntimeError(\"NinePatch expects 1, 5, or 9 arguments, got {}\"\n .format(len(args)))\n self._recalculateSides()\n\n def _recalculateSides(self):\n self._left = min(patch.width for patch in self._patches[::3])\n self._right = min(patch.width for patch in self._patches[2::3])\n self._top = min(patch.height for patch in self._patches[0:3])\n self._bottom = min(patch.height for patch in self._patches[6:9])\n\n def draw(self, spriteBatch: 'sapol.g2d.SpriteBatch', x, y, width, height, *,\n unpatchWidth=False, unpatchHeight=False, **kwargs):\n \"\"\"\n Draw the NinePatch.\n\n :param spriteBatch: The SpriteBatch to draw with.\n :param x: The x-location of the start of the patch.\n :param y: The y-location of the start of the patch.\n :param width: The width to fill.\n :param height: The height to fill.\n\n Must be provided by name:\n\n :param unpatchedWidth: Forces the width of the patch to not operate as\n normally. This will not draw the center patches and cause the left\n and right patches to be stretched to fill the space. This normally\n only happens if the width to fill is smaller than the NinePatch is\n made to fill.\n :param unpatchHeight: Forces the height of the patch to not operate as\n normally much like unpatchWidth.\n :param kwargs: Any extra arguments to pass to SpriteBatch's\n :py:meth:`draw()`.\n \"\"\"\n if width < self._left + self._right:\n unpatchWidth = True\n if height < self._top + self._bottom:\n unpatchHeight = True\n\n widths = None\n leftRightWidth = self._left + self._right\n if not unpatchWidth:\n widths = (self._left, width - leftRightWidth, self._right)\n else:\n widths = (width * self._left / leftRightWidth, 0,\n width * self._right / leftRightWidth)\n xOffsets = (0, widths[0], widths[0] + widths[1])\n if \"xOrigin\" in kwargs:\n xOffsets = tuple(oldOffset + kwargs[\"xOrigin\"] for oldOffset in xOffsets)\n del kwargs[\"xOrigin\"]\n\n heights = None\n topBottomHeight = self._top + self._bottom\n if not unpatchHeight:\n heights = (self._top, height - topBottomHeight, self._bottom)\n else:\n heights = (height * self._top / topBottomHeight, 0,\n height * self._bottom / topBottomHeight)\n yOffsets = (0, heights[0], heights[0] + heights[1])\n if \"yOrigin\" in kwargs:\n yOffsets = tuple(oldOffset + kwargs[\"yOrigin\"] for oldOffset in yOffsets)\n del kwargs[\"yOrigin\"]\n\n for i, patch in enumerate(self._patches):\n px = i % 3\n py = i // 3\n spriteBatch.draw(patch, x, y, xOrigin=-xOffsets[px], yOrigin=-yOffsets[py],\n width=widths[px], height=heights[py], **kwargs)\n","sub_path":"sapol/g2d/texture.py","file_name":"texture.py","file_ext":"py","file_size_in_byte":15847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"490540516","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef findContrastValue(xInput, xMin, xMax, yMin, yMax):\n\n # set to black if on left of stretched region\n if xInput < xMin:\n return 0;\n \n #squeeze values to right of region\n elif xInput > xMax:\n squeezedSlope = (255 - yMax)/(255 - xMax) * (xInput - xMax) + yMax\n return squeezedSlope\n\n #inside contrast range\n else :\n #caclulate slope for contrast\n stretchedSlope = (yMax - yMin)/(xMax - xMin) * (xInput - xMin) + yMin\n return stretchedSlope\n\n\n# Load an color image in grayscale\nimg = cv2.imread('/Users/eric/Desktop/CVProjects/ImProc/overview.jpg',0)\n\n#dim of original image\nwidth = np.size(img, 1)\nheight = np.size(img, 0)\n\n\n#initiate contrast images\ncontrast_img = np.zeros((height, width), np.uint8)\n\n#initiate histogram array\nhistogram = np.zeros((256))\n\nfor i in range(height) :\n for j in range(width):\n contrast_img[i][j] = findContrastValue(img[i][j], 210, 250, 140, 250)\n histogram[contrast_img[i][j]] += 1\n\nprint(width*height)\nprint(histogram.sum())\n\n#lab and show histogram\nplt.plot(img)\nplt.plot(histogram)\nplt.title(\"Intensity Histogram\")\nplt.ylabel(\"Amount of Pixlels\")\nplt.xlabel(\"Intensity\")\nplt.show()\n\n#show images\ncv2.imshow('image',img)\ncv2.imshow('contrast_image', contrast_img)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()","sub_path":"CVPython/PythonFiles/contrast.py","file_name":"contrast.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"514697052","text":"#!/usr/bin/env python\n\nimport sys\nimport os\nimport argparse\nimport logging\n\nimport numpy as np\nfrom tqdm import tqdm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom torch.nn.utils import clip_grad_norm_\nfrom torch.backends import cudnn\n\nimport models\nimport load_so_data as so_data\n\navailable_models = {\n 'baseline_count': models.BaselineVAECount,\n # 'cd_linear_count': models.LinearParametricVAECount,\n # 'personalised_linear_count': models.LinearParametricPlusSteerParamVAECount,\n 'full_parameterised_count': models.FullParameterisedVAECount,\n 'full_personalised_parameterised_count': models.FullParameterisedPlusSteerParamVAECount,\n 'baseline_flow_count': models.NormalizingFlowBaseline,\n 'fp_flow_count': models.NormalizingFlowFP,\n 'full_personalised_normalizing_flow': models.NormalizingFlowFP_PlusSteer\n }\n\ndef isnan(x):\n return x != x\n\ndef raise_cuda_error():\n raise ValueError('You wanted to use cuda but it is not available. '\n 'Check nvidia-smi and your configuration. If you do '\n 'not want to use cuda, pass the --no-cuda flag.')\n\ndef setup_cuda(seed, device):\n if device.index:\n device_str = f\"{device.type}:{device.index}\"\n else:\n device_str = f\"{device.type}\"\n\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = device_str\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n # This does make things slower :(\n torch.backends.cudnn.benchmark = False\n\nloss_fn = lambda x1,x2,x3: models.ZeroInflatedPoisson_loss_function(x1,x2,x3)\n\ndef get_loader_params(args):\n # Loading Parameters\n loader_params = {\n 'batch_size': int(args.batch_size),\n 'shuffle': True,\n 'num_workers': 8\n }\n dset_params = {\n 'window_length': args.window_length,\n 'badge_focus': 'strunk_white',\n 'out_dim': 0,\n 'data_path': args.input,\n 'badge_threshold': 80,\n 'badges_to_avoid': [],\n 'ACTIONS': [0]\n }\n return loader_params, dset_params\n\ndef main(args):\n # TODO: add checkpointing\n use_cuda = not args.no_cuda and torch.cuda.is_available()\n if not args.no_cuda and not use_cuda:\n raise_cuda_error()\n\n device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n if use_cuda:\n logging.info(f'Using device: {torch.cuda.get_device_name()}')\n\n # For reproducibility:\n # c.f. https://pytorch.org/docs/stable/notes/randomness.html\n if args.seed is None:\n args.seed = torch.randint(0, 2 ** 32, (1,)).item()\n logging.info(f'You did not set --seed, {args.seed} was chosen')\n\n if use_cuda:\n setup_cuda(args.seed, device)\n\n config_args = [str(vv) for kk, vv in vars(args).items()\n if kk in ['batch_size', 'lr', 'gamma', 'seed']]\n model_name = '_'.join(config_args)\n\n if not os.path.exists(args.output):\n logging.info(f'{args.output} does not exist, creating...')\n os.makedirs(args.output)\n\n loader_params, dset_params = get_loader_params(args=args)\n\n dset_train = so_data.StackOverflowDatasetIncCounts(\n dset_type='train',\n subsample=15000,\n **dset_params,\n self_initialise=True\n )\n scalers = dset_train.get_scalers()\n dset_valid = so_data.StackOverflowDatasetIncCounts(\n dset_type='validate',\n subsample=5000,\n centered=True,\n **dset_params,\n scaler_in=scalers[0],\n scaler_out=scalers[1])\n\n train_loader = DataLoader(dset_train, **loader_params)\n valid_loader = DataLoader(dset_valid, **loader_params)\n\n print(args.model_name)\n model_class = available_models[args.model_name]\n\n dset_shape = dset_train.data_shape\n model = model_class(\n obsdim=dset_shape[0] * dset_shape[1],\n outdim=dset_shape[0],\n device=device,\n proximity_to_badge=True\n ).to(device)\n\n model_name = 'strunk_white-' + args.model_name + \"-\" + model_name + '.pt'\n PATH_TO_MODEL = args.output+'/models/'+model_name\n\n if os.path.exists(PATH_TO_MODEL):\n model.load_state_dict(torch.load(PATH_TO_MODEL, map_location=device))\n\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.gamma)\n\n if not os.path.exists(f'{args.output}/logs/'):\n os.mkdir(f'{args.output}/logs/')\n\n log_fh = open(f'{args.output}/logs/{model_name}.log', 'w')\n best_loss = sys.float_info.max\n\n results_file = open(f'{args.output}/results.csv', 'a')\n results_file.write(f'{model_name},{args.batch_size},{args.lr},{args.gamma},'\n f'{args.seed},')\n\n count_valid_not_improving = 0\n\n for epoch in tqdm(range(1, args.epochs + 1), disable=use_cuda):\n\n loss = train(args, model, device, train_loader, optimizer, epoch)\n vld_loss = test(args, model, device, valid_loader)\n\n print(f'{epoch},{loss},{vld_loss}', file=log_fh)\n scheduler.step()\n\n results_file.write(f'{vld_loss},')\n\n if vld_loss < best_loss:\n # only save the model if it is performing better on the validation set\n best_loss = vld_loss\n torch.save(model.state_dict(),\n f\"{args.output}/models/{model_name}.best.pt\")\n count_valid_not_improving = 0\n\n # early stopping\n else:\n count_valid_not_improving += 1\n\n if count_valid_not_improving > args.early_stopping_lim:\n print(f'Early stopping implemented at epoch #: {epoch}')\n break\n\n results_file.write('\\n')\n torch.save(model.state_dict(), f\"{args.output}/models/{model_name}.final.pt\")\n\n results_file.close()\n log_fh.close()\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n model.train()\n train_loss = 0\n correct = 0\n beta = 1\n\n for batch_idx, (data) in enumerate(train_loader):\n data = [d.to(device) for d in data]\n dat_in, dat_kern, dat_out, dat_prox, dat_badge_date = data\n\n optimizer.zero_grad()\n # Model computations\n recon_batch, latent_loss = model(dat_in,\n kernel_data=dat_kern,\n dob=dat_badge_date,\n prox_to_badge=dat_prox)\n loss = loss_fn(recon_batch, dat_out, beta * latent_loss)\n loss.backward()\n # TODO: clip grad norm here?\n optimizer.step()\n\n train_loss += loss.item()\n\n if batch_idx % args.log_interval == 0 and not args.quiet:\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, batch_idx * len(data), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader), loss.item()))\n return train_loss/len(train_loader.dataset)\n\ndef test(args, model, device, valid_loader):\n model.eval()\n test_loss = 0\n\n with torch.no_grad():\n for batch_idx, (data) in enumerate(valid_loader):\n data = [d.to(device) for d in data if type(d) == torch.Tensor]\n dat_in, dat_kern, dat_out, dat_prox, dat_badge_date = data\n\n recon_batch, latent_loss = model(dat_in,\n kernel_data=dat_kern,\n dob=dat_badge_date,\n prox_to_badge=dat_prox)\n\n loss = loss_fn(recon_batch, dat_out, latent_loss)\n test_loss += loss.item()\n\n test_loss /= len(valid_loader.dataset)\n print('\\nTest set: Average loss: {:.4f}'.format(test_loss))\n\n return test_loss\n\ndef construct_parser():\n # Training settings\n parser = argparse.ArgumentParser(description='PyTorch main script to run inference detailed here: '\n 'https://arxiv.org/abs/2002.06160'\n 'on the population of users who achieved Strunk & White')\n parser.add_argument('--batch-size', type=int, default=128, metavar='N',\n help='input batch size for training (default: 128)')\n parser.add_argument('--early-stopping-lim', type=int, default=10, metavar='N',\n help='Early stopping implemented after N epochs with no improvement '\n '(default: 10)')\n parser.add_argument('--test-batch-size', type=int, default=1000,\n metavar='N', help='input batch size for testing '\n '(default: 1000)')\n parser.add_argument('--epochs', type=int, default=1000, metavar='N',\n help='number of epochs to train (default: 1000)')\n parser.add_argument('--lr', type=float, default=0.001, metavar='LR',\n help='learning rate (default: 0.001)')\n parser.add_argument('--gamma', type=float, default=0.9, metavar='M',\n help='Learning rate step gamma (default: 0.9)')\n parser.add_argument('--no-cuda', action='store_true', default=False,\n help='disables CUDA training')\n parser.add_argument('--quiet', action='store_true', default=False,\n help='Limits the about of output to std.out')\n parser.add_argument('--seed', type=int, default=None, metavar='S',\n help='random seed (default: random number)')\n parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n help='how many batches to wait before logging training status (default: 10)')\n parser.add_argument('--window-length', type=int, default=35, metavar='N',\n help='how long is the window that is considered before / after a badge (default: 35)')\n parser.add_argument('-M', '--model-name', default=\"full_personalised_normalizing_flow\", required=False,\n help='Choose the model to run')\n parser.add_argument('-i', '--input', required=True, help='Path to the input data for the model to read')\n parser.add_argument('-o', '--output', required=True, help='Path to the directory to write output to')\n return parser\n\nif __name__ == \"__main__\":\n parser = construct_parser()\n args = parser.parse_args()\n main(args)\n","sub_path":"src/main_strunk_white_count_data.py","file_name":"main_strunk_white_count_data.py","file_ext":"py","file_size_in_byte":10740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299528920","text":"'''\nhttps://www.hackerrank.com/contests/w26/challenges/best-divisor\n'''\n\n#!/bin/python3\n\nimport sys\n\ndef getBestDivisor(n):\n maxSum = 1\n bestI = 1\n for i in range(1,n+1):\n if(isDivisor(i, n)):\n #print(i, \"isDivisor\")\n sum = calcSum(i)\n if(sum > maxSum):\n maxSum = sum\n bestI = i\n return bestI\n\ndef isDivisor(i, n):\n return (n % i == 0)\n\ndef calcSum(n):\n sum = 0\n while n > 0:\n sum += n%10\n n = (int)(n/10)\n #print (\"sum =\", sum)\n return sum\n\nn = int(input().strip())\nprint(getBestDivisor(n))","sub_path":"Contests/WeekOfCode26/BestDivisor.py","file_name":"BestDivisor.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"90588170","text":"#!/usr/bin/env python2\n\nimport biplist\nimport distutils.dir_util as distutils\nimport filecmp\nimport glob\nimport plistlib\nimport os.path\nimport shutil\n\n\nHOME_DIR = os.path.expanduser('~')\nCORE_PREFS_NAME = 'com.runningwithcrayons.Alfred-Preferences.plist'\nUSER_PREFS_NAME = 'Alfred.alfredpreferences'\nDEFAULT_USER_PREFS_DIR = os.path.join(\n HOME_DIR, 'Library', 'Application Support', 'Alfred 2')\nPKG_RESOURCES = ('icon.png', 'yvs/__init__.py', 'yvs/shared.py', 'yvs/data')\n\n\n# Get path to directory containing Alfred's user preferences\ndef get_user_prefs_dir():\n\n core_prefs = biplist.readPlist(\n os.path.join(HOME_DIR, 'Library', 'Preferences', CORE_PREFS_NAME))\n\n # If user is syncing their preferences using a syncing service\n if 'syncfolder' in core_prefs:\n return os.path.expanduser(core_prefs['syncfolder'])\n else:\n return DEFAULT_USER_PREFS_DIR\n\n\n# Get path to Alfred's user preferences file\ndef get_user_prefs_path():\n\n return os.path.join(get_user_prefs_dir(), USER_PREFS_NAME)\n\n\n# Get path to installed workflow\ndef get_workflow_path():\n\n yvs_packages = glob.glob(\n os.path.join(get_user_prefs_path(), 'workflows', '*', 'yvs'))\n\n if len(yvs_packages) == 0:\n raise OSError('YouVersion Suggest in not installed locally')\n\n return os.path.dirname(yvs_packages[0])\n\n\n# Get path to installed workflow's info.plist file\ndef get_workflow_info_path(workflow_path):\n\n return os.path.join(workflow_path, 'info.plist')\n\n\n# Parse the info.plist file at the given path\ndef get_workflow_info(info_path):\n\n return plistlib.readPlist(info_path)\n\n\n# Get the file content of a module withini the project\ndef get_module_content(module_name):\n\n filename = '{}.py'.format(module_name.replace('.', '/'))\n with open(filename, 'r') as file:\n return file.read()\n\n\n# Get the name of a module by parsing it from the module content\ndef get_module_name(module_content):\n\n first_line = module_content.split('\\n', 1)[0]\n module_name = first_line[1:].strip()\n return module_name\n\n\n# Update content of all scripts in workflow info object\ndef update_workflow_objects(info):\n\n updated_objects = False\n\n for obj in info['objects']:\n\n if 'script' in obj['config']:\n\n module_name = get_module_name(obj['config']['script'])\n new_module_content = get_module_content(module_name)\n\n if new_module_content != obj['config']['script']:\n obj['config']['script'] = new_module_content\n print('Updated {}'.format(module_name))\n updated_objects = True\n\n return updated_objects\n\n\n# Recursively check if two directories are exactly equal in terms of content\ndef dirs_are_equal(dir_path, dest_dir_path):\n\n dirs_cmp = filecmp.dircmp(dir_path, dest_dir_path)\n if len(dirs_cmp.left_only) > 0 or len(dirs_cmp.right_only) > 0:\n return False\n\n match, mismatch, errors = filecmp.cmpfiles(\n dir_path, dest_dir_path, dirs_cmp.common_files, shallow=False)\n if len(mismatch) > 0 or len(errors) > 0:\n return False\n\n for common_dir in dirs_cmp.common_dirs:\n new_dir_path = os.path.join(dir_path, common_dir)\n new_dest_dir_path = os.path.join(dest_dir_path, common_dir)\n if not dirs_are_equal(new_dir_path, new_dest_dir_path):\n return False\n\n return True\n\n\n# Check if resource (file or directory) is equal to destination resource\ndef resources_are_equal(resource_path, dest_resource_path):\n\n try:\n return dirs_are_equal(resource_path, dest_resource_path)\n except OSError:\n # Compare files if they are not directories\n try:\n return filecmp.cmp(resource_path, dest_resource_path)\n except OSError:\n # Resources are not equal if either does not exist\n return False\n\n\n# Copy package resource (file or directory) to corresponding destination path\ndef copy_resource(resource_path, dest_resource_path):\n\n try:\n distutils.copy_tree(resource_path, dest_resource_path)\n except distutils.DistutilsFileError:\n shutil.copy(resource_path, dest_resource_path)\n\n\n# Copy all package resources (files or directories) to installed workflow\ndef copy_pkg_resources(workflow_path):\n\n updated_resources = False\n\n for resource_path in PKG_RESOURCES:\n\n dest_resource_path = os.path.join(workflow_path, resource_path)\n # Only copy resources if content has changed\n if not resources_are_equal(resource_path, dest_resource_path):\n copy_resource(resource_path, dest_resource_path)\n print('Updated {}'.format(resource_path))\n updated_resources = True\n\n return updated_resources\n\n\n# Write info.plist object to file if content has updated\ndef save_info(info, info_path, updated_workflow=True):\n\n if updated_workflow:\n plistlib.writePlist(info, info_path)\n print('Updated info.plist')\n\n\ndef main():\n\n workflow_path = get_workflow_path()\n info_path = get_workflow_info_path(workflow_path)\n info = get_workflow_info(info_path)\n updated_objects = update_workflow_objects(info)\n updated_resources = copy_pkg_resources(workflow_path)\n updated_workflow = updated_objects or updated_resources\n save_info(info, info_path, updated_workflow)\n if updated_workflow:\n print('Updated workflow successfully')\n else:\n print('Workflow has not changed')\n\nif __name__ == '__main__':\n main()\n","sub_path":"utilities/update_workflow.py","file_name":"update_workflow.py","file_ext":"py","file_size_in_byte":5447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"297831234","text":"import gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\n\n# use creds to create a client to interact with the Google Drive API\nscope = ['https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive']\ncreds = ServiceAccountCredentials.from_json_keyfile_name('evocative-tower-300912-dc0f7ff590fb.json', scope)\nclient = gspread.authorize(creds)\n\nsheet = client.open(\"Bröllopsplanering\").worksheets()[-1]\n\n# Extract and print all of the values\nlist_of_guests = sheet.get_all_values()\n\ntexed_list_of_guests = []\n\nfor i, (id, name, desc, table) in enumerate(list_of_guests):\n texed_list_of_guests.append(' \\\\noindent\\\\begin{minipage}{\\\\textwidth}\\\\centering\\\\scriptsize %s %s \\\\tiny \\\\\\\\ \\\\emph{%s}\\\\end{minipage} \\\\newline \\\\par \\n' % (id.replace('#', '\\#'), name, desc.replace('#', '\\#')))\n\n\nwith open('preamble.tex') as f:\n preamble = f.readlines()\n\nwith open('postamble.tex') as f:\n postamble = f.readlines()\n\nwith open('menu_to_print.tex', 'w') as f:\n f.writelines(preamble+texed_list_of_guests+postamble)\n\n\n","sub_path":"program/create_menu.py","file_name":"create_menu.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"638750282","text":"from django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.parsers import JSONParser\nfrom django.http.response import JsonResponse\nfrom django.contrib.auth.models import User\n\nfrom .serializer import UserSerializer\n\n@csrf_exempt\ndef UserApi(request, username=''):\n if request.method == \"GET\":\n user = User.objects.all()\n user_serializer = UserSerializer(user, many=True)\n return JsonResponse(user_serializer.data, safe=False)\n\n elif request.method == \"POST\":\n user = JSONParser().parse(request)\n user_serializer = UserSerializer(data=user)\n if user_serializer.is_valid():\n user_serializer.save()\n return JsonResponse(True, safe=False)\n return JsonResponse(False, safe=False)\n\n elif request.method == \"PUT\":\n user_data = JSONParser().parse(request)\n user = User.objects.get(username=user_data['username'])\n user_serializer = UserSerializer(user, data=user_data)\n if user_serializer.is_valid():\n user_serializer.save()\n return JsonResponse(True, safe=False)\n return JsonResponse(False, safe=False)\n\n elif request.method == \"DELETE\":\n user = User.objects.get(username=user_data['username'])\n user.delete()\n return JsonResponse(\"Record deleted successfully!\", safe=False)\n","sub_path":"backend/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"430112723","text":"import os\nimport sys\nimport csv\nimport pandas as pd\nimport numpy as np\nfrom data_types import get_dtypes_dict\n\ndef strip_flags(df, col):\n if col in df.columns:\n df[col] = df[col].replace(0, '')\n return df\n\ndef strip_value(df, val):\n \"\"\"Return data frame with \"unavailable\" numeric value removed\n \"\"\"\n df = df.replace(val, np.nan)\n df = df.round(3)\n return df\n\nif __name__ == '__main__':\n\n region = sys.argv[1]\n dtypes = get_dtypes_dict(region)\n flag_cols = [ 'r', 'u', 's', 't', 'mg', 'ch', 'm', 'e', 'c', 'bie' ]\n\n # Read the data dictionary from stdin\n data_df = pd.read_csv(sys.stdin, dtype=dtypes)\n\n # strip the value to create output\n output_df = strip_value(data_df, -999)\n for col in flag_cols:\n output_df = strip_flags(output_df, col)\n\n output_df.to_csv(sys.stdout, index=False)\n","sub_path":"scripts/strip_values.py","file_name":"strip_values.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"276919370","text":"\"\"\" Import all needed modules\"\"\"\nfrom PyQt5 import QtWidgets\nfrom view import Authorization_view\nfrom model import AuthorizationModel\n\n\nif __name__ == \"__main__\":\n\n def press_reg_button_reflect(local_ui: Authorization_view, local_model): # Interaction with clicked registration button\n local_model.registration_data(local_ui)\n local_model.new_window(True)\n\n\n def press_aut_button_reflect(local_ui: Authorization_view, local_model): # Interaction with clicked authorization button\n local_model.authorization(local_ui)\n local_model.new_window(False)\n local_model.clear_data(local_ui)\n\n\n\n import sys\n app = QtWidgets.QApplication(sys.argv)\n authorization = QtWidgets.QWidget()\n ui = Authorization_view(authorization)\n\n \"\"\" Components Events adding \"\"\"\n model = AuthorizationModel(ui)\n\n ui.registation_button.clicked.connect( lambda: press_reg_button_reflect(ui, model))\n\n ui.authorization_button.clicked.connect(lambda: press_aut_button_reflect(ui, model))\n\n authorization.show()\n sys.exit(app.exec_())\n\n","sub_path":"authorization_form/controler.py","file_name":"controler.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487186198","text":"#!/usr/bin/python3\r\n# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\nAuteur : Stanislas Gueniffey(000377223)\r\nCours : INFO-F-106\r\nProjet : Partie 4\r\n\"\"\"\r\n\r\nfrom random import choice, random, randrange\r\n\r\n\r\ndef nameIsValid(name, otherNames):\r\n \"\"\"Fonction-reference pour determiner si un nom 'name' est valable et disponible\r\n etant donne l'ensemble des noms deja utilises otherNames.\"\"\"\r\n return (name.isalnum() and len(name)<25 and not(name in otherNames))\r\n\r\n \r\ndef saveNetwork(filename, netFrame):\r\n \"\"\"Prend en parametre un fichier filename ou le reseau doit etre sauvegarde.\r\n Ecrit dans ce fichier l'etat du reseau de netFrame\"\"\"\r\n netw_file = open(filename, 'w')\r\n \r\n for user in netFrame.getAllUsers(): #Itère sur ensemble des utilisateurs\r\n netw_file.write(str(user)) #Représentation en string de l'utilisateur\r\n netw_file.write(\"\\n\")\r\n \r\n netw_file.close()\r\n \r\n\r\ndef loadNetwork(filename, netFrame, simConfig):\r\n \"\"\"Prend en parametre un fichier filename decrivant le reseau a charger et l'instance\r\n de la classe gérant les parametres de simulation. Retourne un dictionnaire representant le\r\n reseau a charger et une liste des strings representant les statistiques des simulations \r\n effectuees auparavant. Un fichier corrompu entraine le retour d'element vides du meme type.\"\"\"\r\n errorFlag = False #Vrai si une erreur grave survient\r\n \r\n NETW = {} #DICT DE RETOUR {utilisateurs:{amis:configurations}{\"user_rumors\":rumeurs}}\r\n STATS = [] #LISTE DE RETOUR (lignes de statistiques)\r\n \r\n netw_file = open(filename, 'r')\r\n print(\"\\nNetwork loading...\")\r\n \r\n default_rumors = simConfig.defaultRumors\r\n default_config = simConfig.defaultConfig\r\n \r\n names = [] #Contient les noms d'utilisateurs dans l'ordre d'apparition\r\n configs = [] #Contient à l'index i une liste de tuples (nom d'un ami de i, config)\r\n \r\n ###LECTURE DU FICHIER###\r\n try:\r\n got_to_stats = False #Devient vrai quand on atteint l'en-tête statistique\r\n for line in netw_file.readlines():\r\n line = line.strip()\r\n if line == \"\": #En-tête statistiques atteinte\r\n got_to_stats = True\r\n \r\n elif len(line) > 2 and not got_to_stats: #Avant statistiques, lignes non \"vides\"\r\n lineData = line.split(':')\r\n userName = lineData[0].strip()\r\n \r\n if not nameIsValid(userName, NETW.keys()): #Evite les doublons & noms non valables\r\n print(\"User %s could not be added because its username was taken\"%userName)\r\n else:\r\n if len(lineData) == 2: #NOM + AMIS (rétrocompatibilité)\r\n userRumors = default_rumors\r\n friendsData = [data.strip() for data in lineData[1].split(';')]\r\n else: #NOM + RUMEURS + AMIS\r\n userRumors = eval(lineData[1])\r\n friendsData = [data.strip() for data in lineData[2].split(';')]\r\n \r\n #Encore les rumeurs dans le dictionnaire avec clé fixée != nom valable\r\n NETW[userName] = {\"user_rumors\":userRumors} #Ebauche de Network\r\n names.append(userName) #Structure temp. pour verifications\r\n \r\n conf_line = [] #Liste temp. a ajouter aux configs\r\n user_friends = set() #Ensemble des noms d'amis, pour éviter les doublons\r\n \r\n for data in friendsData: #data peut être NOM ou NOM + (CONFIG)\r\n try:\r\n friend_name, friend_config = data.split('(') #Retire déjà \"(\"\r\n \r\n except: #Si data = nom\r\n if nameIsValid(data, user_friends): #Si le nom est valable\r\n user_friends.add(data)\r\n conf_line.append((data, default_config)) #Param. par défaut\r\n \r\n else: #Si data = nom + (config)\r\n friend_name = friend_name.strip()\r\n if nameIsValid(friend_name, user_friends):#Si le nom est valable\r\n user_friends.add(friend_name)\r\n \r\n #Lit la configuration ([:-1] pour retirer parenthèse fermante)\r\n full_config = simConfig.expandConfig(friend_config[:-1])\r\n if full_config!=None: #Si la configuration est valable\r\n conf_line.append((friend_name, full_config))\r\n else: #Sinon, donne paramètres par défaut\r\n print(\"Default config had to be used for friends %s,%s\"%\\\r\n (userName, friend_name))\r\n conf_line.append((friend_name, default_config))\r\n \r\n configs.append(conf_line)\r\n \r\n elif got_to_stats: #Après en-tête statistiques\r\n STATS.append(line)\r\n except:\r\n print(\"Network file is corrupted or invalid\")\r\n errorFlag = True\r\n \r\n \r\n ###CREATION DU RESEAU & VERIFICATIONS###\r\n else:\r\n nbUsers = len(names) #Nombre d'utilisateurs\r\n if nbUsers == 0:\r\n print(\"Network file defines 0 users\")\r\n errorFlag = True\r\n else: #Si le nombre d'utilisateurs n'est pas nul\r\n \r\n for user_index in range(nbUsers): #Pour chaque utilisateur d'index user_index\r\n user_name = names[user_index]\r\n \r\n for friend_index in range(len(configs[user_index])): #Pour chacun de ses amis\r\n friend_name = configs[user_index][friend_index][0] #Nom de l'ami\r\n friend_conf = configs[user_index][friend_index][1] #Config. de l'amitié\r\n \r\n if friend_name not in NETW.keys(): #Ami n'existe pas\r\n print(\"%s's friend %s is not a user\"\\\r\n %(usr_names[user_index],friend_name))\r\n elif friend_name == user_name: #Amitié avec soi-même\r\n print(\"User %s can't be friend with himself\"%friend_name)\r\n else: #Tout va bien\r\n NETW[user_name][friend_name] = friend_conf #Encode l'amitié\r\n #Si l'amitié n'est pas (encore) symétrique, lui donne la config par défaut\r\n if user_name not in NETW[friend_name].keys():\r\n NETW[friend_name][user_name] = default_config\r\n \r\n print(\"\\nNetwork successfully loaded\")\r\n netw_file.close()\r\n return ({}, []) if errorFlag else (NETW, STATS)\r\n\r\n\r\ndef update(sim, netFrame, rumorType):\r\n \"\"\"Effectue les transmissions des rumeurs correspondant a une etape de la simulation dans le \r\n reseau represente par netFrame pour le type de rumeur rumorType et en fonction des parametres\r\n de la simulation contenus dans la classe sim.\r\n Retourne le nombre d'utilisateurs mis au courant de la rumeur pendant cette etape.\"\"\"\r\n \r\n ###CHOIX DES PERSONNES A INFORMER###\r\n peopleToInform = {} #cles= gens a informer; valeurs= dict{rumeur: raconteurs}\r\n \r\n for talker in netFrame.getAwareUsers(rumorType): #Pour chaque personne au courant\r\n allFriends = list(talker.getFriends()) #Ensemble des amis\r\n friends_chosen = [] #Amis choisis pour raconter la rumeur\r\n toldRumor = talker.getRumor(rumorType) #Rumeur connue\r\n \r\n if allFriends: #Si cette liste n'est pas vide\r\n if sim.curFriendChoice == \"random\": #Choisit un ami au hasard\r\n friends_chosen = [choice(allFriends)]\r\n else:#sim.curFriendChoice == \"random-d\" #Choisit un ami ne connaissant pas la rumeur\r\n unawareFriends = [friend for friend in allFriends if friend.isUnaware(rumorType)]\r\n if unawareFriends:\r\n friends_chosen = [choice(unawareFriends)] #Choice : erreur si liste vide\r\n else:\r\n friends_chosen = []\r\n \r\n for friend in friends_chosen:\r\n try: \r\n try:\r\n #Si toldRumor est déjà dans le dict. de friend\r\n peopleToInform[friend][toldRumor].append(talker)\r\n except:\r\n #Si toldRumor n'est pas dans le dict. de friend\r\n peopleToInform[friend][toldRumor] = [talker]\r\n except:\r\n #Si friend n'est pas dans les clés de peopleToInform\r\n peopleToInform[friend] = {toldRumor:[talker]} \r\n \r\n ###TRANSMISSION DE LA RUMEUR###\r\n newInformed = 0 #Nombre de nouveaux informes a ce tour\r\n \r\n for listener in peopleToInform.keys():\r\n for told_rumor in peopleToInform[listener].keys():\r\n for talker in peopleToInform[listener][told_rumor]:\r\n \r\n if sim.configIsGeneral(): #Configuration : parametres generaux\r\n error_prob, modif_type, update_rule = sim.getGlobalConfig()\r\n else: #Parametres individuels\r\n error_prob, modif_type = talker.getConfig(listener)[:2]#De transmission (A:B)\r\n update_rule = listener.getConfig(talker)[2] #De reception (B:A)\r\n \r\n #Decide de la version racontee par l'émetteur\r\n heard_rumor = alter_rumor(told_rumor, sim, modif_type, error_prob, rumorType)\r\n \r\n #Decide de la version retenue par le récepteur\r\n if listener.isUnaware(rumorType): #Si c'est sa premiere rumeur il la garde\r\n newInformed += 1\r\n listener.setRumor(heard_rumor, rumorType)\r\n else: #Sinon, ca depend des parametres de la simulation\r\n old_rumor = listener.getRumor(rumorType)\r\n new_rumor=choose_rumor(old_rumor, heard_rumor, sim, update_rule, rumorType)\r\n listener.setRumor(new_rumor, rumorType)\r\n\r\n return newInformed\r\n\r\n\r\ndef alter_rumor(rumor, sim, modif_type, modif_prob, rumorType):\r\n \"\"\"Effectue le changement de la rumeur rumor en fonction de son type rumorType et des\r\n parametres de transmission modif_type et modif_prob, avec l'aide de la classe sim contenant\r\n les donnees sur les limites de tailles des differents types de rumeurs.\"\"\"\r\n newRumor = rumor #\"Copie\" de la rumeur à modifier, cas \"none\"\r\n \r\n if modif_type != \"none\":\r\n if random() < modif_prob: #Si le RNG decide qu'il faut modifier la version\r\n \r\n if rumorType == 0: #RUMOR IS 'BINARY'\r\n if modif_type==\"incremental\":\r\n newRumor = (rumor+choice([1, -1]))%(2**sim.binarySize)#Modulo val.max\r\n else:#modif_type=='bitflip'\r\n newRumor = rumor ^ (2**randrange(sim.binarySize))#xor avec puiss. de 2\r\n \r\n else: #RUMOR IS 'CLUEDO'\r\n modRumor = list(rumor) #Rumeur en modification\r\n if modif_type==\"incremental\":\r\n modRumor[0] += choice([-1, 1])\r\n nbValues = len(modRumor)\r\n for i in range(nbValues): #Va parcourir les 3 valeurs du cluedo\r\n if modRumor[i] >= sim.cluedoSize[i]: #Si on a dépassé dans une catég.\r\n modRumor[i] = 0 \r\n modRumor[(i+1)%nbValues] += 1\r\n elif modRumor[i] < 0: #Si on est sous zéro adns une catégorie\r\n modRumor[i] = sim.cluedoSize[i]-1\r\n modRumor[(i+1)%nbValues] -= 1\r\n else:#modif_type=='bitflip'\r\n indexChanged = randrange(len(modRumor))\r\n newValue = randrange(sim.cluedoSize[indexChanged])\r\n modRumor[indexChanged] = newValue\r\n newRumor = tuple(modRumor)\r\n \r\n return newRumor\r\n\r\n\r\ndef choose_rumor(oldRumor, hrdRumor, sim, update_rule, rumorType):\r\n \"\"\"Choisit la version de la rumeur a retenir en fonction de update_rule, du type de la rumeur\r\n rumorType, de la version de la rumeur connue precedemment et de la nouvelle version transmise,\r\n sim contenant les donnees sur les limites de tailles des differents types de rumeurs.\"\"\"\r\n newRumor = oldRumor #Cas 'stable' : nouvelle rumeur reste la même\r\n \r\n if update_rule=='rewrite':\r\n newRumor = hrdRumor #Idem pour rumeur binaire ou cluedo\r\n \r\n elif update_rule=='mixture':\r\n \r\n if rumorType == 0: #RUMOR IS 'BINARY'\r\n old_bits = bin(oldRumor)[2:].zfill(sim.binarySize) #Bits de old version\r\n hrd_bits = bin(hrdRumor)[2:].zfill(sim.binarySize) #Bits de heard version\r\n new_bits = [] #Bits de new version\r\n for i in range(sim.binarySize):\r\n if old_bits[i] == hrd_bits[i]: #Bits identiques\r\n new_bits.append(old_bits[i])\r\n else: \r\n if random() < sim.keepSelfBitsProb: #Bit diffèrent, garde ancien\r\n new_bits.append(old_bits[i])\r\n else: #Bits diffèrent, garde nouveau\r\n new_bits.append(hrd_bits[i])\r\n newRumor = int(''.join(new_bits), 2) #Assemble les bits\r\n \r\n else: #RUMOR IS 'CLUEDO'\r\n old_bits = list(oldRumor) #Bits de old version\r\n hrd_bits = list(hrdRumor) #Bits de heard version\r\n new_bits = [] #Bits de new version\r\n for i in range(len(old_bits)):\r\n if old_bits[i] == hrd_bits[i]: #Bits identiques\r\n new_bits.append(old_bits[i])\r\n else:\r\n if random() < sim.keepSelfBitsProb: #Bits diffèrent, garde ancien\r\n new_bits.append(old_bits[i])\r\n else:\r\n new_bits.append(hrd_bits[i]) #Bits diffèrent, garde nouveau\r\n newRumor = tuple(new_bits) #Assemble les bits\r\n \r\n return newRumor\r\n","sub_path":"rumorFunctions.py","file_name":"rumorFunctions.py","file_ext":"py","file_size_in_byte":14975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"579816937","text":"from __future__ import print_function\nfrom setuptools import setup, find_packages\nfrom fake_rpi.version import __version__ as VERSION\nfrom build_utils import BuildCommand\nfrom build_utils import PublishCommand\nfrom build_utils import BinaryDistribution\n\n\nPACKAGE_NAME = 'fake_rpi'\nBuildCommand.pkg = PACKAGE_NAME\nPublishCommand.pkg = PACKAGE_NAME\nPublishCommand.version = VERSION\n\n\nsetup(\n\tauthor='Kevin Walchko',\n\tauthor_email='walchko@users.noreply.github.com',\n\tname=PACKAGE_NAME,\n\tversion=VERSION,\n\tdescription='A bunch of fake interfaces for development when not using the RPi or unit testing',\n\tlong_description=open('readme.rst').read(),\n\turl='http://github.com/walchko/{}'.format(PACKAGE_NAME),\n\tclassifiers=[\n\t\t'Development Status :: 4 - Beta',\n\t\t'Intended Audience :: Developers',\n\t\t'License :: OSI Approved :: MIT License',\n\t\t'Operating System :: OS Independent',\n\t\t'Programming Language :: Python :: 2.7',\n\t\t'Programming Language :: Python :: 3.6',\n\t\t'Topic :: Software Development :: Libraries',\n\t\t'Topic :: Software Development :: Libraries :: Python Modules',\n\t\t'Topic :: Software Development :: Libraries :: Application Frameworks'\n\t],\n\tlicense='MIT',\n\tkeywords=['raspberry', 'pi', 'fake', 'fake_rpi', 'i2c', 'spi', 'gpio', 'serial'],\n\tpackages=find_packages('.'),\n\tinstall_requires=['build_utils', 'numpy'],\n\tcmdclass={\n\t\t'make': BuildCommand,\n\t\t'publish': PublishCommand\n\t}\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"310449457","text":"# Solution to Project Euler Problem #31: Coin sums\n# Copyright (c) MarcinSkrobczynski\n\nALL_COINS = [1, 2, 5, 10, 20, 50, 100, 200]\n\n\ndef main(n: int, coins: list) -> int:\n different_ways = [1] + [0] * n\n for coin in coins:\n for i in range(len(different_ways) - coin):\n different_ways[i + coin] += different_ways[i]\n return different_ways[-1]\n\n\ndef solution() -> int:\n return main(200, ALL_COINS)\n\n\nif __name__ == \"__main__\":\n print(f\"{main(5, ALL_COINS)} => {main(5, ALL_COINS) == 4}\")\n print(f\"{main(200, ALL_COINS)}\")\n","sub_path":"solutions/problem_0031.py","file_name":"problem_0031.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"186152056","text":"import asyncio\nimport json\nfrom typing import AsyncGenerator, Optional, Type\nfrom unittest.mock import Mock\n\nimport h2\nimport h11\nimport pytest\n\nfrom hypercorn.asyncio.h2 import H2Server\nfrom hypercorn.config import Config\nfrom hypercorn.typing import ASGIFramework\nfrom .helpers import MockTransport\nfrom ..helpers import ChunkedResponseFramework, EchoFramework, PushFramework\n\nBASIC_HEADERS = [(\":authority\", \"hypercorn\"), (\":scheme\", \"https\")]\nBASIC_DATA = \"index\"\nFLOW_WINDOW_SIZE = 1\n\n\nclass MockConnection:\n def __init__(\n self,\n event_loop: asyncio.AbstractEventLoop,\n *,\n config: Config = Config(),\n framework: Type[ASGIFramework] = EchoFramework,\n upgrade_request: Optional[h11.Request] = None,\n ) -> None:\n self.transport = MockTransport()\n self.server = H2Server( # type: ignore\n framework, event_loop, config, self.transport, upgrade_request=upgrade_request\n )\n self.connection = h2.connection.H2Connection()\n if upgrade_request is not None:\n self.connection.initiate_upgrade_connection()\n else:\n self.connection.initiate_connection()\n\n def send_request(self, headers: list, settings: dict) -> int:\n self.connection.update_settings(settings)\n self.server.data_received(self.connection.data_to_send())\n stream_id = self.connection.get_next_available_stream_id()\n self.connection.send_headers(stream_id, headers)\n self.server.data_received(self.connection.data_to_send())\n return stream_id\n\n async def send_data(self, stream_id: int, data: bytes) -> None:\n self.connection.send_data(stream_id, data)\n self.server.data_received(self.connection.data_to_send())\n await asyncio.sleep(0) # Yield to allow the server to process\n\n async def end_stream(self, stream_id: int) -> None:\n self.connection.end_stream(stream_id)\n self.server.data_received(self.connection.data_to_send())\n await asyncio.sleep(0) # Yield to allow the server to process\n\n def close(self) -> None:\n self.connection.close_connection()\n self.server.data_received(self.connection.data_to_send())\n\n async def get_events(self) -> AsyncGenerator[h2.events.Event, None]:\n while True:\n await self.transport.updated.wait()\n events = self.connection.receive_data(self.transport.data)\n self.transport.clear()\n for event in events:\n if isinstance(event, h2.events.ConnectionTerminated):\n self.transport.close()\n elif isinstance(event, h2.events.DataReceived):\n self.connection.acknowledge_received_data(\n event.flow_controlled_length, event.stream_id\n )\n self.server.data_received(self.connection.data_to_send())\n yield event\n if self.transport.closed.is_set():\n break\n\n\n@pytest.mark.asyncio\n@pytest.mark.parametrize(\n \"headers, body\",\n [\n (BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/\")], \"\"),\n (\n BASIC_HEADERS\n + [\n (\":method\", \"POST\"),\n (\":path\", \"/\"),\n (\"content-length\", str(len(BASIC_DATA.encode()))),\n ],\n BASIC_DATA,\n ),\n ],\n)\nasync def test_request(headers: list, body: str, event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop)\n stream_id = connection.send_request(headers, {})\n if body != \"\":\n await connection.send_data(stream_id, body.encode())\n await connection.end_stream(stream_id)\n response_data = b\"\"\n async for event in connection.get_events():\n if isinstance(event, h2.events.ResponseReceived):\n assert (b\":status\", b\"200\") in event.headers\n assert (b\"server\", b\"hypercorn-h2\") in event.headers\n assert b\"date\" in (header[0] for header in event.headers)\n elif isinstance(event, h2.events.DataReceived):\n response_data += event.data\n elif isinstance(event, h2.events.StreamEnded):\n connection.close()\n data = json.loads(response_data.decode())\n assert data[\"request_body\"] == body # type: ignore\n\n\n@pytest.mark.asyncio\nasync def test_protocol_error(event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop)\n connection.server.data_received(b\"broken nonsense\\r\\n\\r\\n\")\n assert connection.transport.closed.is_set() # H2 just closes on error\n\n\n@pytest.mark.asyncio\nasync def test_pipelining(event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop)\n streams = [\n connection.send_request(BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/1\")], {}),\n connection.send_request(BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/1\")], {}),\n ]\n for stream_id in streams:\n await connection.end_stream(stream_id)\n responses = 0\n async for event in connection.get_events():\n if isinstance(event, h2.events.ResponseReceived):\n responses += 1\n elif isinstance(event, h2.events.StreamEnded) and responses == 2:\n connection.close()\n assert responses == len(streams)\n\n\n@pytest.mark.asyncio\nasync def test_server_sends_chunked(event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop, framework=ChunkedResponseFramework)\n stream_id = connection.send_request(BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/\")], {})\n await connection.end_stream(stream_id)\n response_data = b\"\"\n async for event in connection.get_events():\n if isinstance(event, h2.events.DataReceived):\n response_data += event.data\n elif isinstance(event, h2.events.StreamEnded):\n connection.close()\n assert response_data == b\"chunked data\"\n\n\n@pytest.mark.asyncio\nasync def test_initial_keep_alive_timeout(event_loop: asyncio.AbstractEventLoop) -> None:\n config = Config()\n config.keep_alive_timeout = 0.01\n server = H2Server(EchoFramework, event_loop, config, Mock())\n await asyncio.sleep(2 * config.keep_alive_timeout)\n server.transport.close.assert_called() # type: ignore\n\n\n@pytest.mark.asyncio\nasync def test_post_response_keep_alive_timeout(event_loop: asyncio.AbstractEventLoop) -> None:\n config = Config()\n config.keep_alive_timeout = 0.01\n connection = MockConnection(event_loop, config=config)\n stream_id = connection.send_request(BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/1\")], {})\n connection.server.pause_writing()\n await connection.end_stream(stream_id)\n await asyncio.sleep(2 * config.keep_alive_timeout)\n assert not connection.transport.closed.is_set()\n connection.server.resume_writing()\n await asyncio.sleep(2 * config.keep_alive_timeout)\n assert connection.transport.closed.is_set()\n events = [event async for event in connection.get_events()]\n assert isinstance(events[-1], h2.events.ConnectionTerminated)\n\n\n@pytest.mark.asyncio\nasync def test_h2server_upgrade(event_loop: asyncio.AbstractEventLoop) -> None:\n upgrade_request = h11.Request(method=\"GET\", target=\"/\", headers=[(\"Host\", \"hypercorn\")])\n connection = MockConnection(event_loop, upgrade_request=upgrade_request)\n response_data = b\"\"\n async for event in connection.get_events():\n if isinstance(event, h2.events.ResponseReceived):\n assert (b\":status\", b\"200\") in event.headers\n assert (b\"server\", b\"hypercorn-h2\") in event.headers\n assert b\"date\" in (header[0] for header in event.headers)\n elif isinstance(event, h2.events.DataReceived):\n response_data += event.data\n elif isinstance(event, h2.events.StreamEnded):\n connection.close()\n\n\n@pytest.mark.asyncio\nasync def test_h2_flow_control(event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop)\n stream_id = connection.send_request(\n BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/\")],\n {h2.settings.SettingCodes.INITIAL_WINDOW_SIZE: FLOW_WINDOW_SIZE},\n )\n await connection.end_stream(stream_id)\n async for event in connection.get_events():\n if isinstance(event, h2.events.DataReceived):\n assert len(event.data) <= FLOW_WINDOW_SIZE\n elif isinstance(event, h2.events.StreamEnded):\n connection.close()\n\n\n@pytest.mark.asyncio\nasync def test_h2_push(event_loop: asyncio.AbstractEventLoop) -> None:\n connection = MockConnection(event_loop, framework=PushFramework)\n stream_id = connection.send_request(BASIC_HEADERS + [(\":method\", \"GET\"), (\":path\", \"/\")], {})\n await connection.end_stream(stream_id)\n push_received = False\n streams_received = 0\n async for event in connection.get_events():\n if isinstance(event, h2.events.PushedStreamReceived):\n assert (b\":path\", b\"/\") in event.headers\n assert (b\":method\", b\"GET\") in event.headers\n assert (b\":scheme\", b\"http\") in event.headers\n assert (b\":authority\", b\"hypercorn\") in event.headers\n push_received = True\n elif isinstance(event, h2.events.StreamEnded):\n streams_received += 1\n if streams_received == 2:\n connection.close()\n assert push_received\n","sub_path":"tests/asyncio/test_h2.py","file_name":"test_h2.py","file_ext":"py","file_size_in_byte":9390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"573303312","text":"#!/usr/bin/env python3\n# coding=utf-8\n\nimport datetime\nimport itertools\nimport json\nimport logging\nimport os\nimport re\nimport subprocess\nimport sys\nimport tempfile\nimport time\nfrom collections import defaultdict\nfrom pprint import pformat\nfrom typing import TextIO, Optional, Dict, Sequence\n\nimport click\nimport requests\n\nfrom lib.amazon import target_group_arn_for, get_autoscaling_group, get_releases, find_release, get_current_key, \\\n set_current_key, as_client, release_for, find_latest_release, get_all_current, remove_release, get_events_file, \\\n save_event_file, get_short_link, put_short_link, delete_short_link, list_short_links, delete_s3_links, \\\n get_autoscaling_groups_for, download_release_file, download_release_fileobj, log_new_build, list_all_build_logs, \\\n list_period_build_logs, get_ssm_param\nfrom lib.cdn import DeploymentJob\nfrom lib.env import Environment, Config\nfrom lib.instance import ConanInstance, AdminInstance, BuilderInstance, Instance, print_instances\nfrom lib.releases import Version\nfrom lib.ssh import run_remote_shell, exec_remote, exec_remote_all, exec_remote_to_stdout\n\nlogger = logging.getLogger('ce')\n\nRELEASE_FORMAT = '{: <5} {: <10} {: <10} {: <10} {: <14}'\nADS_FORMAT = '{: <5} {: <10} {: <20}'\nDECORATION_FORMAT = '{: <10} {: <15} {: <30} {: <50}'\n\n\n@click.group()\n@click.option(\"--env\", type=click.Choice([env.value for env in Environment]),\n default=Environment.STAGING.value, metavar='ENV',\n help='Select environment ENV')\n@click.option(\"--mosh/--no-mosh\", help='Use mosh to connect to hosts')\n@click.option(\"--debug/--no-debug\", help='Turn on debugging')\n@click.pass_context\ndef cli(ctx: click.Context, env: str, mosh: bool, debug: bool):\n ctx.obj = Config(env=Environment(env), use_mosh=mosh)\n if debug:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n logging.getLogger('boto3').setLevel(logging.WARNING)\n logging.getLogger('botocore').setLevel(logging.WARNING)\n\n\ndef pick_instance(cfg: Config):\n elb_instances = Instance.elb_instances(target_group_arn_for(cfg))\n if len(elb_instances) == 1:\n return elb_instances[0]\n while True:\n print_instances(elb_instances, number=True)\n inst = input('Which instance? ')\n try:\n return elb_instances[int(inst)]\n except (ValueError, IndexError):\n pass\n\n\ndef pick_instances(cfg: Config):\n return Instance.elb_instances(target_group_arn_for(cfg))\n\n\ndef sizeof_fmt(num, suffix='B'):\n for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:\n if abs(num) < 1024.0:\n return \"%3.1f%s%s\" % (num, unit, suffix)\n num /= 1024.0\n return \"%.1f%s%s\" % (num, 'Yi', suffix)\n\n\ndef describe_current_release(cfg: Config):\n current = get_current_key(cfg)\n if not current:\n return \"none\"\n r = release_for(get_releases(), current)\n if r:\n return str(r)\n else:\n \"non-standard release with s3 key '{}'\".format(current)\n\n\ndef wait_for_autoscale_state(instance, state):\n logger.info(\"Waiting for %s to reach autoscale lifecycle '%s'...\", instance, state)\n while True:\n autoscale = instance.describe_autoscale()\n if not autoscale:\n logger.error(\"Instance is not longer in an ASG: stopping\")\n return\n cur_state = autoscale['LifecycleState']\n logger.debug(\"State is %s\", cur_state)\n if cur_state == state:\n logger.info(\"...done\")\n return\n time.sleep(5)\n\n\ndef get_events(cfg: Config):\n events = json.loads(get_events_file(cfg))\n if 'ads' not in events:\n events['ads'] = []\n if 'decorations' not in events:\n events['decorations'] = []\n if 'motd' not in events:\n events['motd'] = ''\n return events\n\n\ndef save_events(cfg: Config, events):\n save_event_file(cfg, json.dumps(events))\n\n\ndef wait_for_elb_state(instance, state):\n logger.info(\"Waiting for %s to reach ELB state '%s'...\", instance, state)\n while True:\n instance.update()\n instance_state = instance.instance.state['Name']\n if instance_state != 'running':\n raise RuntimeError('Instance no longer running (state {})'.format(instance_state))\n logger.debug(\"State is %s\", instance.elb_health)\n if instance.elb_health == state:\n logger.info(\"...done\")\n return\n time.sleep(5)\n\n\ndef are_you_sure(name: str, cfg: Config) -> bool:\n while True:\n typed = input(\n f'Confirm operation: \"{name}\" in env {cfg.env.value}\\nType the name of the environment to proceed: ')\n if typed == cfg.env.value:\n return True\n\n\ndef confirm_branch(release):\n branch = release.branch\n while True:\n typed = input('Confirm build branch \"{}\"\\nType the name of the branch: '.format(branch))\n if typed == branch:\n return True\n\n\ndef confirm_action(description):\n typed = input('{}: [Y/N]\\n'.format(description))\n return typed.upper() == 'Y'\n\n\ndef is_everything_awesome(instance):\n try:\n response = exec_remote(instance, ['curl', '-s', '--max-time', '2', 'http://127.0.0.1/healthcheck'])\n return response.strip() == \"Everything is awesome\"\n except subprocess.CalledProcessError:\n return False\n\n\ndef wait_for_healthok(instance):\n logger.info(\"Waiting for instance to be Online %s\", instance)\n sys.stdout.write('Waiting')\n while not is_everything_awesome(instance):\n sys.stdout.write('.')\n # Flush stdout so tmux updates\n sys.stdout.flush()\n time.sleep(10)\n print(\"Ok, Everything is awesome!\")\n\n\ndef restart_one_instance(as_group_name: str, instance: Instance, modified_groups: Dict[str, int]):\n instance_id = instance.instance.instance_id\n logger.info(\"Enabling instance protection for %s\", instance)\n as_client.set_instance_protection(AutoScalingGroupName=as_group_name,\n InstanceIds=[instance_id],\n ProtectedFromScaleIn=True)\n as_group = get_autoscaling_group(as_group_name)\n adjustment_required = as_group['DesiredCapacity'] == as_group['MinSize']\n if adjustment_required:\n logger.info(\"Group '%s' needs to be adjusted to keep enough nodes\", as_group_name)\n modified_groups[as_group['AutoScalingGroupName']] = as_group['DesiredCapacity']\n logger.info(\"Putting %s into standby\", instance)\n as_client.enter_standby(\n InstanceIds=[instance_id],\n AutoScalingGroupName=as_group_name,\n ShouldDecrementDesiredCapacity=not adjustment_required)\n wait_for_autoscale_state(instance, 'Standby')\n logger.info(\"Restarting service on %s\", instance)\n restart_response = exec_remote(instance, ['sudo', 'systemctl', 'restart', 'compiler-explorer'])\n if restart_response:\n logger.warning(\"Restart gave some output: %s\", restart_response)\n wait_for_healthok(instance)\n logger.info(\"Moving %s out of standby\", instance)\n as_client.exit_standby(\n InstanceIds=[instance_id],\n AutoScalingGroupName=as_group_name)\n wait_for_autoscale_state(instance, 'InService')\n wait_for_elb_state(instance, 'healthy')\n logger.info(\"Disabling instance protection for %s\", instance)\n as_client.set_instance_protection(AutoScalingGroupName=as_group_name,\n InstanceIds=[instance_id],\n ProtectedFromScaleIn=False)\n logger.info(\"Instance restarted ok\")\n\n\n@cli.command()\n@click.pass_obj\ndef admin(cfg: Config):\n \"\"\"Log in to the administrative instance.\"\"\"\n run_remote_shell(cfg, AdminInstance.instance())\n\n\n@cli.group()\ndef conan():\n \"\"\"Conan instance management commands.\"\"\"\n\n\n@conan.command(name='login')\n@click.pass_obj\ndef conan_login(cfg: Config):\n \"\"\"Log in to the conan instance.\"\"\"\n instance = ConanInstance.instance()\n run_remote_shell(cfg, instance)\n\n\n@conan.command(name='exec')\n@click.argument('remote_cmd', required=True, nargs=-1)\ndef conan_exec(remote_cmd: Sequence[str]):\n \"\"\"Execute the REMOTE_CMD on the conan instance.\"\"\"\n instance = ConanInstance.instance()\n exec_remote_to_stdout(instance, remote_cmd)\n\n\n@conan.command(name='restart')\ndef conan_restart():\n \"\"\"Restart the conan instance.\"\"\"\n instance = ConanInstance.instance()\n exec_remote(instance, [\"sudo\", \"service\", \"ce-conan\", \"restart\"])\n\n\n@conan.command(name='reloadwww')\ndef conan_reloadwww():\n \"\"\"Reload the conan web.\"\"\"\n instance = ConanInstance.instance()\n exec_remote(instance, [\"sudo\", \"git\", \"-C\", \"/home/ubuntu/ceconan/conanproxy\", \"pull\"])\n\n\n@cli.group()\ndef builder():\n \"\"\"Builder machine manipulation commands.\"\"\"\n\n\n@builder.command(name='login')\n@click.pass_obj\ndef builder_login(cfg: Config):\n \"\"\"Log in to the builder machine.\"\"\"\n instance = BuilderInstance.instance()\n run_remote_shell(cfg, instance)\n\n\n@builder.command(name='exec')\n@click.argument('remote_cmd', required=True, nargs=-1)\ndef builder_exec(remote_cmd: Sequence[str]):\n \"\"\"Execute REMOTE_CMD on the builder instance.\"\"\"\n instance = BuilderInstance.instance()\n exec_remote_to_stdout(instance, remote_cmd)\n\n\n@builder.command(name='start')\ndef builder_start():\n \"\"\"Start the builder instance.\"\"\"\n instance = BuilderInstance.instance()\n if instance.status() == 'stopped':\n print(\"Starting builder instance...\")\n instance.start()\n for _ in range(60):\n if instance.status() == 'running':\n break\n time.sleep(1)\n else:\n raise RuntimeError(\"Unable to start instance, still in state: {}\".format(instance.status()))\n for _ in range(60):\n try:\n r = exec_remote(instance, [\"echo\", \"hello\"])\n if r.strip() == \"hello\":\n break\n except subprocess.CalledProcessError as e:\n print(\"Still waiting for SSH: got: {}\".format(e))\n time.sleep(1)\n else:\n raise RuntimeError(\"Unable to get SSH access\")\n res = exec_remote(instance,\n [\"bash\", \"-c\", \"cd infra && git pull && sudo ./setup-builder-startup.sh\"])\n print(res)\n print(\"Builder started OK\")\n\n\n@builder.command(name='stop')\ndef builder_stop():\n \"\"\"Stop the builder instance.\"\"\"\n BuilderInstance.instance().stop()\n\n\n@builder.command(name='status')\ndef builder_status():\n \"\"\"Get the builder status (running or otherwise).\"\"\"\n print(\"Builder status: {}\".format(BuilderInstance.instance().status()))\n\n\n@cli.group()\ndef instances():\n \"\"\"Instance management commands.\"\"\"\n\n\n@instances.command(name='exec_all')\n@click.pass_obj\n@click.argument('remote_cmd', required=True, nargs=-1)\ndef instances_exec_all(cfg: Config, remote_cmd: Sequence[str]):\n \"\"\"Execute REMOTE_CMD on all the instances.\"\"\"\n if not are_you_sure(f'exec command {remote_cmd} in all instances', cfg):\n return\n\n print(\"Running '{}' on all instances\".format(' '.join(remote_cmd)))\n exec_remote_all(pick_instances(cfg), remote_cmd)\n\n\n@instances.command(name='login')\n@click.pass_obj\ndef instances_login(cfg: Config):\n \"\"\"Log in to one of the instances.\"\"\"\n instance = pick_instance(cfg)\n run_remote_shell(cfg, instance)\n\n\n@instances.command(name='restart_one')\n@click.pass_obj\ndef instances_restart_one(cfg: Config):\n \"\"\"Restart one of the instances.\"\"\"\n instance = pick_instance(cfg)\n as_instance_status = instance.describe_autoscale()\n if not as_instance_status:\n logger.error(\"Failed restarting %s - was not in ASG\", instance)\n return\n as_group_name = as_instance_status['AutoScalingGroupName']\n modified_groups: Dict[str, int] = {}\n try:\n restart_one_instance(as_group_name, instance, modified_groups)\n except RuntimeError as e:\n logger.error(\"Failed restarting %s - skipping: %s\", instance, e)\n\n\n@instances.command(name='start')\n@click.pass_obj\ndef instances_start(cfg: Config):\n \"\"\"Start up the instances.\"\"\"\n print(\"Starting version %s\", describe_current_release(cfg))\n exec_remote_all(pick_instances(cfg), ['sudo', 'systemctl', 'start', 'compiler-explorer'])\n\n\n@instances.command(name='stop')\n@click.pass_obj\ndef instances_stop(cfg: Config):\n \"\"\"Stop the instances.\"\"\"\n if cfg.env == Environment.PROD:\n print('Operation aborted. This would bring down the site')\n print('If you know what you are doing, edit the code in bin/lib/ce.py, function instances_stop_cmd')\n elif are_you_sure('stop all instances', cfg):\n exec_remote_all(pick_instances(cfg), ['sudo', 'systemctl', 'stop', 'compiler-explorer'])\n\n\n@instances.command(name='restart')\n@click.option('--motd', type=str, default='Site is being updated',\n help='Set the message of the day used during update', show_default=True)\n@click.pass_obj\ndef instances_restart(cfg: Config, motd: str):\n \"\"\"Restart the instances, picking up new code.\"\"\"\n if not are_you_sure('restart all instances with version {}'.format(describe_current_release(cfg)), cfg):\n return\n # Store old motd\n begin_time = datetime.datetime.now()\n events = get_events(cfg)\n old_motd = events['motd']\n events['motd'] = old_motd if motd == '' else motd\n save_events(cfg, events)\n modified_groups: Dict[str, int] = {}\n failed = False\n to_restart = pick_instances(cfg)\n\n for index, instance in enumerate(to_restart):\n logger.info(\"Restarting %s (%d of %d)...\", instance, index + 1, len(to_restart))\n as_instance_status = instance.describe_autoscale()\n if not as_instance_status:\n logger.warning(\"Skipping %s as it is no longer in the ASG\", instance)\n continue\n as_group_name = as_instance_status['AutoScalingGroupName']\n if as_instance_status['LifecycleState'] != 'InService':\n logger.warning(\"Skipping %s as it is not InService (%s)\", instance, as_instance_status)\n continue\n\n try:\n restart_one_instance(as_group_name, instance, modified_groups)\n except RuntimeError as e:\n logger.error(\"Failed restarting %s - skipping: %s\", instance, e)\n failed = True\n # TODO, what here?\n\n for group, desired in iter(modified_groups.items()):\n logger.info(\"Putting desired instances for %s back to %d\", group, desired)\n as_client.update_auto_scaling_group(AutoScalingGroupName=group, DesiredCapacity=desired)\n # Events might have changed, re-fetch\n events = get_events(cfg)\n events['motd'] = old_motd\n save_events(cfg, events)\n end_time = datetime.datetime.now()\n delta_time = end_time - begin_time\n print(f'Instances restarted in {delta_time.total_seconds()} seconds')\n sys.exit(1 if failed else 0)\n\n\n@instances.command(name='status')\n@click.pass_obj\ndef instances_status(cfg: Config):\n \"\"\"Get the status of the instances.\"\"\"\n print_instances(Instance.elb_instances(target_group_arn_for(cfg)), number=False)\n\n\n@cli.group()\ndef builds():\n \"\"\"Build manipulation commands.\"\"\"\n\n\n@builds.command(name=\"current\")\n@click.pass_obj\ndef builds_current(cfg: Config):\n \"\"\"Print the current release.\"\"\"\n print(describe_current_release(cfg))\n\n\ndef old_deploy_staticfiles(branch, versionfile):\n print(\"Deploying static files\")\n downloadfile = versionfile\n filename = 'deploy.tar.xz'\n remotefile = branch + '/' + downloadfile\n download_release_file(remotefile[1:], filename)\n os.mkdir('deploy')\n subprocess.call(['tar', '-C', 'deploy', '-Jxf', filename])\n os.remove(filename)\n subprocess.call(['aws', 's3', 'sync', 'deploy/out/dist/dist', 's3://compiler-explorer/dist/cdn'])\n subprocess.call(['rm', '-Rf', 'deploy'])\n\n\ndef deploy_staticfiles(release) -> bool:\n print(\"Deploying static files to cdn\")\n cc = f'public, max-age={int(datetime.timedelta(days=365).total_seconds())}'\n\n with tempfile.NamedTemporaryFile(suffix=os.path.basename(release.static_key)) as f:\n download_release_fileobj(release.static_key, f)\n with DeploymentJob(f.name, 'ce-cdn.net', version=release.version, cache_control=cc) as job:\n return job.run()\n\n\n@builds.command(name='set_current')\n@click.pass_obj\n@click.option('--branch', help='if version == latest, branch to get latest version from')\n@click.option('--raw/--no-raw', help='Set a raw path for a version')\n@click.argument('version')\ndef builds_set_current(cfg: Config, branch: Optional[str], version: str, raw: bool):\n \"\"\"Set the current version to VERSION for this environment.\n\n If VERSION is \"latest\" then the latest version (optionally filtered by --branch), is set.\n \"\"\"\n to_set = None\n release = None\n if raw:\n to_set = version\n else:\n setting_latest = version == 'latest'\n release = find_latest_release(branch) if setting_latest else find_release(\n Version.from_string(version))\n if not release:\n print(\"Unable to find version \" + version)\n if setting_latest and branch != '':\n print('Branch {} has no available versions (Bad branch/No image yet built)'.format(branch))\n elif are_you_sure('change current version to {}'.format(release.key), cfg) and confirm_branch(release):\n print(f'Found release {release}')\n to_set = release.key\n if to_set is not None:\n log_new_build(cfg, to_set)\n if release and release.static_key:\n if not deploy_staticfiles(release):\n print(\"...aborted due to deployment failure!\")\n sys.exit(1)\n else:\n old_deploy_staticfiles(branch, to_set)\n set_current_key(cfg, to_set)\n if release:\n print(\"Marking as a release in sentry...\")\n token = get_ssm_param(\"/compiler-explorer/sentryAuthToken\")\n result = requests.post(\n f\"https://sentry.io/api/0/organizations/compiler-explorer/releases/{release.version}/deploys/\",\n data=dict(environment=cfg.env.value),\n headers=dict(Authorization=f'Bearer {token}'))\n if not result.ok:\n raise RuntimeError(f\"Failed to send to sentry: {result} {result.content.decode('utf-8')}\")\n print(\"...done\", json.loads(result.content.decode()))\n\n\n@builds.command(name=\"rm_old\")\n@click.option('--dry-run/--no-dry-run', help='dry run only')\n@click.argument('max_age', type=int)\ndef builds_rm_old(dry_run: bool, max_age: int):\n \"\"\"Remove all but the last MAX_AGE builds.\"\"\"\n current = get_all_current()\n max_builds: Dict[str, int] = defaultdict(int)\n for release in get_releases():\n max_builds[release.version.source] = max(release.version.number, max_builds[release.version.source])\n for release in get_releases():\n if release.key in current:\n print(\"Skipping {} as it is a current version\".format(release))\n else:\n age = max_builds[release.version.source] - release.version.number\n if age > max_age:\n if dry_run:\n print(\"Would remove build {}\".format(release))\n else:\n print(\"Removing build {}\".format(release))\n remove_release(release)\n else:\n print(\"Keeping build {}\".format(release))\n\n\n@builds.command(name='list')\n@click.pass_obj\n@click.option('-b', '--branch', type=str, help='show only BRANCH (may be specified more than once)',\n metavar='BRANCH', multiple=True)\ndef builds_list(cfg: Config, branch: Sequence[str]):\n \"\"\"List available builds.\n\n The --> indicates the build currently deployed in this environment.\"\"\"\n current = get_current_key(cfg)\n releases = get_releases()\n filter_branches = set(branch)\n print(RELEASE_FORMAT.format('Live', 'Branch', 'Version', 'Size', 'Hash'))\n for _, releases in itertools.groupby(releases, lambda r: r.branch):\n for release in releases:\n if len(filter_branches) == 0 or release.branch in filter_branches:\n print(\n RELEASE_FORMAT.format(\n ' -->' if release.key == current else '',\n release.branch, str(release.version), sizeof_fmt(release.size), str(release.hash))\n )\n\n\n@builds.command(name='history')\n@click.option('--from', 'from_time')\n@click.option('--until', 'until_time')\n@click.pass_obj\ndef builds_history(cfg: Config, from_time: Optional[str], until_time: Optional[str]):\n \"\"\"Show the history of current versions for this environment.\"\"\"\n if from_time is None and until_time is None:\n if confirm_action(\n 'Do you want list all builds for {}? It might be an expensive operation:'.format(cfg.env.value)):\n list_all_build_logs(cfg)\n else:\n list_period_build_logs(cfg, from_time, until_time)\n\n\n@cli.group()\ndef ads():\n \"\"\"Community advert manipulation features.\"\"\"\n\n\n@ads.command(name='list')\n@click.pass_obj\ndef ads_list(cfg: Config):\n \"\"\"List the existing community adverts.\"\"\"\n events = get_events(cfg)\n print(ADS_FORMAT.format('ID', 'Filters', 'HTML'))\n for ad in events['ads']:\n print(ADS_FORMAT.format(ad['id'], str(ad['filter']), ad['html']))\n\n\n@ads.command(name='add')\n@click.pass_obj\n@click.option(\"--filter\", 'lang_filter', help='Filter to these languages (default all)', multiple=True)\n@click.argument(\"html\")\ndef ads_add(cfg: Config, lang_filter: Sequence[str], html: str):\n \"\"\"Add a community advert with HTML.\"\"\"\n events = get_events(cfg)\n new_ad = {\n 'html': html,\n 'filter': lang_filter,\n 'id': max([x['id'] for x in events['ads']]) + 1 if len(events['ads']) > 0 else 0\n }\n if are_you_sure('add ad: {}'.format(ADS_FORMAT.format(new_ad['id'], str(new_ad['filter']), new_ad['html'])), cfg):\n events['ads'].append(new_ad)\n save_event_file(cfg, json.dumps(events))\n\n\n@ads.command(name='remove')\n@click.pass_obj\n@click.option('--force/--no-force', help='Force remove (no confirmation)')\n@click.argument('ad_id', type=int)\ndef ads_remove(cfg: Config, ad_id: int, force: bool):\n \"\"\"Remove community ad number AD_ID.\"\"\"\n events = get_events(cfg)\n for i, ad in enumerate(events['ads']):\n if ad['id'] == ad_id:\n if force or \\\n are_you_sure('remove ad: {}'.format(ADS_FORMAT.format(ad['id'], str(ad['filter']), ad['html'])),\n cfg):\n del events['ads'][i]\n save_event_file(cfg, json.dumps(events))\n break\n\n\n@ads.command(name='clear')\n@click.pass_obj\ndef ads_clear(cfg: Config):\n \"\"\"Clear all community ads.\"\"\"\n events = get_events(cfg)\n if are_you_sure('clear all ads (count: {})'.format(len(events['ads'])), cfg):\n events['ads'] = []\n save_event_file(cfg, json.dumps(events))\n\n\n@ads.command(name='edit')\n@click.option(\"--filter\", 'lang_filter', help='Change filters to these languages', multiple=True)\n@click.option(\"--html\", help='Change html to HTML')\n@click.argument('ad_id', type=int)\n@click.pass_obj\ndef ads_edit(cfg: Config, ad_id: int, html: str, lang_filter: Sequence[str]):\n \"\"\"Edit community ad AD_ID.\"\"\"\n events = get_events(cfg)\n for i, ad in enumerate(events['ads']):\n if ad['id'] == ad_id:\n new_ad = {\n 'id': ad['id'],\n 'filter': lang_filter or ad['filter'],\n 'html': html or ad['html']\n }\n print('{}\\n{}\\n{}'.format(ADS_FORMAT.format('Event', 'Filter(s)', 'HTML'),\n ADS_FORMAT.format('TO', str(new_ad['filter']), new_ad['html'])))\n if are_you_sure('edit ad id: {}'.format(ad['id']), cfg):\n events['ads'][i] = new_ad\n save_event_file(cfg, json.dumps(events))\n break\n\n\n@cli.group()\ndef decorations():\n \"\"\"Manage the decorations (ok, Easter Eggs).\"\"\"\n\n\n@decorations.command(name='list')\n@click.pass_obj\ndef decorations_list(cfg: Config):\n events = get_events(cfg)\n print(DECORATION_FORMAT.format('Name', 'Filters', 'Regex', 'Decoration'))\n for dec in events['decorations']:\n print(DECORATION_FORMAT.format(dec['name'], str(dec['filter']), dec['regex'], json.dumps(dec['decoration'])))\n\n\ndef check_dec_args(regex, decoration):\n try:\n re.compile(regex)\n except re.error as re_err:\n raise RuntimeError(f\"Unable to validate regex '{regex}' : {re_err}\") from re_err\n try:\n decoration = json.loads(decoration)\n except json.decoder.JSONDecodeError as json_err:\n raise RuntimeError(f\"Unable to parse decoration '{decoration}' : {json_err}\") from json_err\n return regex, decoration\n\n\n@decorations.command(name='add')\n@click.pass_obj\n@click.option('--filter', 'lang_filter', help='filter for this language', multiple=True)\n@click.argument('name')\n@click.argument('regex')\n@click.argument('decoration')\ndef decorations_add(cfg: Config, lang_filter: Sequence[str], name: str, regex: str, decoration: str):\n \"\"\"\n Add a decoration called NAME matching REGEX resulting in json DECORATION.\n \"\"\"\n events = get_events(cfg)\n if name in [d['name'] for d in events['decorations']]:\n raise RuntimeError(f'Duplicate decoration name {name}')\n regex, decoration = check_dec_args(regex, decoration)\n\n new_decoration = {\n 'name': name,\n 'filter': lang_filter,\n 'regex': regex,\n 'decoration': decoration\n }\n if are_you_sure('add decoration: {}'.format(\n DECORATION_FORMAT.format(new_decoration['name'], str(new_decoration['filter']), new_decoration['regex'],\n json.dumps(new_decoration['decoration']))), cfg):\n events['decorations'].append(new_decoration)\n save_event_file(cfg, json.dumps(events))\n\n\n@decorations.command(name='remove')\n@click.pass_obj\n@click.option(\"--force/--no-force\", help=\"force without confirmation\")\n@click.argument('name')\ndef decorations_remove(cfg: Config, name: str, force: bool):\n \"\"\"Remove a decoration.\"\"\"\n events = get_events(cfg)\n for i, dec in enumerate(events['decorations']):\n if dec['name'] == name:\n if force or \\\n are_you_sure('remove decoration: {}'.format(\n DECORATION_FORMAT.format(dec['name'], str(dec['filter']), dec['regex'],\n json.dumps(dec['decoration']))), cfg):\n del events['decorations'][i]\n save_event_file(cfg, json.dumps(events))\n break\n\n\n@decorations.command(name='clear')\n@click.pass_obj\ndef decorations_clear(cfg: Config):\n \"\"\"Clear all decorations.\"\"\"\n events = get_events(cfg)\n if are_you_sure('clear all decorations (count: {})'.format(len(events['decorations'])), cfg):\n events['decorations'] = []\n save_event_file(cfg, json.dumps(events))\n\n\n@decorations.command(name='edit')\n@click.pass_obj\n@click.option('--filter', 'lang_filter', help='filter for this language', multiple=True)\n@click.option('--regex', help='match REGEX')\n@click.option('--decoration', help='evaluate to DECORATION (json syntax)')\n@click.argument('name')\ndef decorations_edit(cfg: Config, lang_filter: Sequence[str], name: str, regex: str, decoration: str):\n \"\"\"Edit existing decoration NAME.\"\"\"\n events = get_events(cfg)\n\n for i, dec in enumerate(events['decorations']):\n if dec['name'] == name:\n regex, decoration = check_dec_args(regex or dec['regex'],\n decoration or json.dumps(dec['decoration']))\n new_dec = {\n 'name': dec['name'],\n 'filter': lang_filter or dec['filter'],\n 'regex': regex,\n 'decoration': decoration\n }\n print('{}\\n{}\\n{}'.format(DECORATION_FORMAT.format('Name', 'Filters', 'Regex', 'Decoration'),\n DECORATION_FORMAT.format('TO', str(new_dec['filter']), new_dec['regex'],\n json.dumps(new_dec['decoration']))))\n if are_you_sure('edit decoration: {}'.format(dec['name']), cfg):\n events['decoration'][i] = new_dec\n save_event_file(cfg, json.dumps(events))\n break\n\n\n@cli.group(name='motd')\ndef motd_group():\n \"\"\"Message of the day manipulation functions.\"\"\"\n\n\n@motd_group.command(name='show')\n@click.pass_obj\ndef motd_show(cfg: Config):\n \"\"\"Prints the message of the day.\"\"\"\n events = get_events(cfg)\n print('Current motd: \"{}\"'.format(events['motd']))\n\n\n@motd_group.command(name='update')\n@click.argument('message', type=str)\n@click.pass_obj\ndef motd_update(cfg: Config, message: str):\n \"\"\"Updates the message of the day to MESSAGE.\"\"\"\n events = get_events(cfg)\n if are_you_sure('update motd from: {} to: {}'.format(events['motd'], message), cfg):\n events['motd'] = message\n save_event_file(cfg, json.dumps(events))\n\n\n@motd_group.command(name='clear')\n@click.pass_obj\ndef motd_clear(cfg: Config):\n \"\"\"Clears the message of the day.\"\"\"\n events = get_events(cfg)\n if are_you_sure('clear current motd: {}'.format(events['motd']), cfg):\n events['motd'] = ''\n save_events(cfg, events)\n\n\n@cli.group(name='events')\ndef events_group():\n \"\"\"Low-level manipulation of ads and events.\"\"\"\n\n\n@events_group.command(name='to_raw')\n@click.pass_obj\ndef events_to_raw(cfg: Config):\n \"\"\"Dumps the events file as raw JSON.\"\"\"\n print(get_events_file(cfg))\n\n\n@events_group.command(name='from_raw')\n@click.pass_obj\ndef events_from_raw(cfg: Config):\n \"\"\"Reloads the events file as raw JSON from console input.\"\"\"\n raw = input()\n save_event_file(cfg, json.dumps(json.loads(raw)))\n\n\n@events_group.command(name='to_file')\n@click.argument(\"file\", type=click.File(mode='w'))\n@click.pass_obj\ndef events_to_file(cfg: Config, file: TextIO):\n \"\"\"Saves the raw events file as FILE.\"\"\"\n file.write(get_events_file(cfg))\n\n\n@events_group.command(name='from_file')\n@click.argument(\"file\", type=click.File(mode='r'))\n@click.pass_obj\ndef events_from_file(cfg: Config, file: TextIO):\n \"\"\"Reads FILE and replaces the events file with its contents.\"\"\"\n new_contents = json.loads(file.read())\n if are_you_sure(f'load events from file {file.name}', cfg):\n save_event_file(cfg, new_contents)\n\n\n@cli.group()\ndef link():\n \"\"\"Link manipulation commands.\"\"\"\n\n\n@link.command(name='name')\n@click.pass_obj\n@click.argument(\"link_from\")\n@click.argument(\"link_to\")\ndef links_name(cfg: Config, link_from: str, link_to: str):\n \"\"\"Give link LINK_FROM a new name LINK_TO.\"\"\"\n if len(link_from) < 6:\n raise RuntimeError('from length must be at least 6')\n if len(link_to) < 6:\n raise RuntimeError('to length must be at least 6')\n base_link = get_short_link(link_from)\n if not base_link:\n raise RuntimeError('Couldn\\'t find base link {}'.format(link_from))\n base_link['prefix']['S'] = link_to[0:6]\n base_link['unique_subhash']['S'] = link_to\n base_link['stats']['M']['clicks']['N'] = '0'\n base_link['creation_ip']['S'] = '0.0.0.0'\n # It's us, so we don't care about \"anonymizing\" the time\n base_link['creation_date']['S'] = datetime.datetime.utcnow().isoformat()\n title = input('Link title: ')\n author = input('Author(s): ')\n if len(author) == 0:\n # We explicitly ignore author = . in the site code\n author = '.'\n project = input('Project: ')\n description = input('Description: ')\n base_link['named_metadata'] = {'M': {\n 'title': {'S': title},\n 'author': {'S': author},\n 'project': {'S': project},\n 'description': {'S': description}\n }}\n print('New link: {}'.format(pformat(base_link)))\n if are_you_sure('create new link named {}'.format(link_to), cfg):\n put_short_link(base_link)\n\n\n@link.command(name='update')\n@click.pass_obj\n@click.argument(\"link_from\")\n@click.argument(\"link_to\")\ndef links_update(cfg: Config, link_from: str, link_to: str):\n \"\"\"Update a link; point LINK_FROM to existing LINK_TO.\"\"\"\n if len(link_from) < 6:\n raise RuntimeError('from length must be at least 6')\n if len(link_to) < 6:\n raise RuntimeError('to length must be at least 6')\n base_link = get_short_link(link_from)\n if not base_link:\n raise RuntimeError('Couldn\\'t find base link {}'.format(link_from))\n link_to_update = get_short_link(link_to)\n if not link_to_update:\n raise RuntimeError('Couldn\\'t find existing short link {}'.format(link_to))\n link_to_update['full_hash'] = base_link['full_hash']\n print('New link: {}'.format(pformat(link_to_update)))\n if are_you_sure('update link named {}'.format(link_to), cfg):\n put_short_link(link_to_update)\n\n\n@link.command(name='maintenance')\n@click.option(\"--dry-run/--no-dry-run\", help=\"dry run only\")\n@click.pass_obj\ndef links_maintenance(cfg: Config, dry_run: bool):\n s3links, dblinks = list_short_links()\n s3keys_set = set()\n dbkeys_set = set()\n dbhashes_set = set()\n s3dirty_set = set()\n dbdirty_set = set()\n for page in s3links:\n for state in page['Contents']:\n if len(state['Key'][6:]) > 1:\n s3keys_set.add(state['Key'][6:])\n for page in dblinks:\n for item in page['Items']:\n unique_subhash = item['unique_subhash']['S']\n full_hash = item['full_hash']['S']\n dbkeys_set.add((unique_subhash, full_hash))\n dbhashes_set.add(full_hash)\n for dbkey in dbkeys_set:\n if dbkey[1] not in s3keys_set:\n dbdirty_set.add(dbkey)\n for s3key in s3keys_set:\n if s3key not in dbhashes_set:\n s3dirty_set.add(s3key)\n\n if are_you_sure('delete {} db elements:\\n{}\\n'.format(len(dbdirty_set), dbdirty_set), cfg) and not dry_run:\n for item in dbdirty_set:\n print('Deleting {}'.format(item))\n delete_short_link(item)\n if are_you_sure('delete {} s3 elements:\\n{}\\n'.format(len(s3dirty_set), s3dirty_set), cfg) and not dry_run:\n delete_s3_links(s3dirty_set)\n\n\ndef add_required_sub_parsers(parser, dest):\n sub_parser = parser.add_subparsers(dest=dest)\n sub_parser.required = True # docs say I can pass required=True in add_subparsers but that seems to be a lie\n return sub_parser\n\n\n@cli.group()\ndef environment():\n \"\"\"Environment manipulation commands.\"\"\"\n\n\n@environment.command(name='status')\n@click.pass_obj\ndef environment_status(cfg: Config):\n \"\"\"Gets the status of an environment.\"\"\"\n for asg in get_autoscaling_groups_for(cfg):\n print(f\"Found ASG {asg['AutoScalingGroupName']} with desired instances {asg['DesiredCapacity']}\")\n\n\n@environment.command(name='start')\n@click.pass_obj\ndef environment_start(cfg: Config):\n \"\"\"Starts up an environment by ensure its ASGs have capacity.\"\"\"\n for asg in get_autoscaling_groups_for(cfg):\n group_name = asg['AutoScalingGroupName']\n if asg['MinSize'] > 0:\n print(f\"Skipping ASG {group_name} as it has a non-zero min size\")\n continue\n prev = asg['DesiredCapacity']\n if prev:\n print(f\"Skipping ASG {group_name} as it has non-zero desired capacity\")\n continue\n print(f\"Updating {group_name} to have desired capacity 1 (from {prev})\")\n as_client.update_auto_scaling_group(AutoScalingGroupName=group_name, DesiredCapacity=1)\n\n\n@environment.command(name='refresh')\n@click.option(\"--min-healthy-percent\", type=click.IntRange(min=0, max=100), metavar='PERCENT',\n help='While updating, ensure at least PERCENT are healthy', default=75, show_default=True)\n@click.pass_obj\ndef environment_refresh(cfg: Config, min_healthy_percent: int):\n \"\"\"Refreshes an environment.\n\n This replaces all the instances in the ASGs associated with an environment with\n new instances (with the latest code), while ensuring there are some left to handle\n the traffic while we update.\"\"\"\n # TODO motd like the restart\n for asg in get_autoscaling_groups_for(cfg):\n group_name = asg['AutoScalingGroupName']\n if asg['DesiredCapacity'] == 0:\n print(f\"Skipping ASG {group_name} as it has a zero size\")\n continue\n describe_state = as_client.describe_instance_refreshes(\n AutoScalingGroupName=group_name\n )\n existing_refreshes = [x for x in describe_state['InstanceRefreshes'] if\n x['Status'] in ('Pending', 'InProgress')]\n if existing_refreshes:\n refresh_id = existing_refreshes[0]['InstanceRefreshId']\n print(f\" Found existing refresh {refresh_id} for {group_name}\")\n else:\n if not are_you_sure(f'Refresh instances in {group_name} with version {describe_current_release(cfg)}',\n cfg):\n return\n print(\" Starting new refresh...\")\n refresh_result = as_client.start_instance_refresh(\n AutoScalingGroupName=group_name,\n Preferences=dict(MinHealthyPercentage=min_healthy_percent)\n )\n refresh_id = refresh_result['InstanceRefreshId']\n print(f\" id {refresh_id}\")\n\n last_log = \"\"\n while True:\n time.sleep(5)\n describe_state = as_client.describe_instance_refreshes(\n AutoScalingGroupName=group_name,\n InstanceRefreshIds=[refresh_id]\n )\n refresh = describe_state['InstanceRefreshes'][0]\n status = refresh['Status']\n if status == 'InProgress':\n log = f\" {status}, {refresh['PercentageComplete']}%, \" \\\n f\"{refresh['InstancesToUpdate']} to update. \" \\\n f\"{refresh.get('StatusReason', '')}\"\n else:\n log = f\" Status: {status}\"\n if log != last_log:\n print(log)\n last_log = log\n if status in ('Successful', 'Failed', 'Cancelled'):\n break\n\n\n@environment.command(name='stop')\n@click.pass_obj\ndef environment_stop(cfg: Config):\n \"\"\"Stops an environment.\"\"\"\n if cfg.env == Environment.PROD:\n print('Operation aborted. This would bring down the site')\n print('If you know what you are doing, edit the code in bin/lib/ce.py, function environment_stop_cmd')\n elif are_you_sure('stop environment', cfg):\n for asg in get_autoscaling_groups_for(cfg):\n group_name = asg['AutoScalingGroupName']\n if asg['MinSize'] > 0:\n print(f\"Skipping ASG {group_name} as it has a non-zero min size\")\n continue\n prev = asg['DesiredCapacity']\n if not prev:\n print(f\"Skipping ASG {group_name} as it already zero desired capacity\")\n continue\n print(f\"Updating {group_name} to have desired capacity 0 (from {prev})\")\n as_client.update_auto_scaling_group(AutoScalingGroupName=group_name, DesiredCapacity=0)\n\n\ndef main():\n try:\n cli(prog_name='ce') # pylint: disable=unexpected-keyword-arg,no-value-for-parameter\n except (KeyboardInterrupt, SystemExit):\n # print empty line so terminal prompt doesn't end up on the end of some\n # of our own program output\n print()\n","sub_path":"bin/lib/ce.py","file_name":"ce.py","file_ext":"py","file_size_in_byte":39730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"456374848","text":"# -*- coding: utf-8 -*- \n\nimport sys\nimport snake\n\nfrom panda3d.core import Point2\n\nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.task.Task import Task\n\nfrom settings import *\nfrom helpers import genLabelText, loadObject\nfrom collections import deque\n\nclass World( ShowBase ):\n def __init__ ( self ):\n ShowBase.__init__( self )\n\n self.disableMouse( )\n self.snake = snake.Snake( body=[ (-7, 1), (-8, 1), (-9, 1) ], dot=(-7, 1) )\n self.snake.gen_dot( )\n\n self.background = loadObject( \"background\", scale=9000, depth=200, transparency=False )\n self.gameboard = loadObject( \"background\", scale=39.5, depth=100, transparency=False )\n self.escape_text = genLabelText( \"ESC : Quit\", 0 )\n self.pause_text = genLabelText( \"SPACE: Pause\", 1)\n self.score = genLabelText( \"SCORE: %s\" % self.snake.get_score( ), 0, left=False )\n \n self.bricks = deque( )\n self.make_dot( )\n\n self.draw_snake( )\n self.accept( \"escape\", sys.exit )\n self.accept( \"enter\", self.restart )\n self.accept( \"arrow_up\", self.snake.turn, [ POS_Y ] )\n self.accept( \"arrow_down\", self.snake.turn, [ NEG_Y ] )\n self.accept( \"arrow_left\", self.snake.turn, [ NEG_X ] )\n self.accept( \"arrow_right\", self.snake.turn, [ POS_X ] )\n self.accept( \"space\", self.tooggle_pause )\n\n self.game_task = taskMgr.add( self.game_loop, \"GameLoop\" )\n self.game_task.last = 0\n self.period = 0.15\n self.pause = False\n\n def game_loop( self, task ):\n dt = task.time - task.last\n if not self.snake.alive: \n return task.done\n if self.pause:\n return task.cont\n elif dt >= self.period:\n task.last = task.time\n self.snake.move_forward( )\n self.snake.check_state( )\n self.update_snake( )\n self.update_dot( )\n self.update_score( )\n return task.cont\n else:\n return task.cont\n\n\n def draw_snake( self ):\n for point in self.snake.body:\n brick = loadObject( \"brick\", pos=Point2( point[ X ], point[ Y ] ) )\n self.bricks.append( brick )\n\n def update_snake( self ):\n try:\n for i in xrange( len( self.snake.body ) ):\n point = self.snake.body[ i ]\n brick = self.bricks[ i ]\n brick.setPos( point[ X ], SPRITE_POS, point[ Y ] )\n except IndexError:\n new_head = self.dot\n self.make_dot( )\n self.bricks.appendleft( new_head )\n\n def make_dot( self ):\n self.dot = loadObject( \"brick\", pos=Point2( self.snake.dot[ X ], self.snake.dot[ Y ] ) ) \n\n def update_dot( self ):\n x, y = self.dot.getX( ), self.dot.getZ( )\n if ( x, y ) != self.snake.dot:\n self.dot.setPos( self.snake.dot[ X ], SPRITE_POS, self.snake.dot[ Y ] )\n\n def update_score( self ):\n if self.score:\n self.score.removeNode( )\n self.score = genLabelText( \"Score: %s\" % self.snake.get_score( ), 0, left=False )\n\n def tooggle_pause( self ):\n if self.pause: self.pause = False\n else: self.pause = True\n\nw = World( )\nw.run( )\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"187364537","text":"import scipy.io.wavfile\nimport numpy as np\n\nfs = 44100 # Hz\ndur = 5 # sec\nfmin = 10 # Hz\nfmax = fs/4 # Hz\n\nt = np.arange(0,dur,1/fs,dtype=\"float32\")\nmodf = 0 #np.sin(t*4*2*np.pi)\nf = (t/np.max(t)*(fmax-fmin)+fmin)*(1+0.05*modf)\nx = np.sin(np.multiply(f,t)*2*np.pi)\n\nscipy.io.wavfile.write(\"sweep.wav\", fs, x)\n","sub_path":"makesweep.py","file_name":"makesweep.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60676970","text":"#Rock, Paper, Scissors\nimport random, os\n\ndef clear_screen():\n '''\n Clear the screen after a match is over.\n '''\n os.system('CLS')\n\ndef prompt_user(count=0):\n '''Welcome message and gather user input.\n We are going to track how many times the user has played already and prompt accordingly.\n '''\n if count > 0:\n play_again = input(\"\\n\\nWould you like to play again (Yes or No)? \")\n\n if play_again.lower() == 'yes' or play_again.lower() == 'y':\n \tuser_select = input(\"\\nRock, Paper, or Scissors? \")\n elif play_again.lower() == 'no' or play_again.lower() == 'n':\n return play_again\n else:\n play_again = 'invalid'\n return play_again\n\n else:\n print(\"\\n\\nWelcome to Rock, Paper, Scissors!\")\n print(\"-\"*33)\n print(\"Your opponent, a random generator!\\n\\n\")\n user_select = input(\"Rock, Paper, or Scissors? \")\n\n return user_select\n\n\ndef game_match():\n '''\n Here we will take the user's input, randomly generate an opponent selection, and compare the randomly generated opponent selction to a dict of 'user wins' scenarios.\n '''\n count = 0\n wins = 0\n losses = 0\n ties = 0\n user_input = prompt_user(count)\n\n while user_input.lower() != 'no' and user_input.lower() != 'n':\n rand_options = ['rock', 'paper', 'scissors']\n user_win_cases = {'rock': 'scissors',\n 'paper': 'rock',\n 'scissors': 'paper'}\n rand_input = random.choice(rand_options)\n\n try:\n \tuser_win_cases[user_input.lower()]\n except KeyError:\n \tprint(\"\\nPlease enter a valid input and try again.\")\n \tuser_input = prompt_user(count)\n \tcontinue\n\n if rand_input.lower() == user_win_cases[user_input.lower()]:\n print(\"\\n\\nUser wins!\")\n print(\"-\"*10)\n print()\n print(\"User Choice: \"+user_input.lower())\n print(\"Random Gen. Choice: \"+rand_input.lower())\n print(\"-\"*28)\n print()\n count += 1\n wins += 1\n print(\"Your Wins: \"+str(wins))\n print(\"Your Losses: \"+str(losses))\n print(\"Your Ties: \"+str(ties))\n elif user_input.lower() == rand_input.lower():\n print(\"\\n\\nTie!\")\n count += 1\n ties += 1\n print(\"Your Wins: \"+str(wins))\n print(\"Your Losses: \"+str(losses))\n print(\"Your Ties: \"+str(ties))\n else:\n print(\"\\n\\nUser loses!\")\n print(\"-\"*11)\n print()\n print(\"User Choice: \"+user_input.lower())\n print(\"Random Gen. Choice: \"+rand_input.lower())\n count += 1\n losses += 1\n print(\"Your Wins: \"+str(wins))\n print(\"Your Losses: \"+str(losses))\n print(\"Your Ties: \"+str(ties))\n\n user_input = prompt_user(count)\n\n clear_screen()\n\ngame_match()\n","sub_path":"plain_rps.py","file_name":"plain_rps.py","file_ext":"py","file_size_in_byte":2981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"114801436","text":"_base_ = './fastercnn_r50_fpn_crop.py'\nnorm_cfg = dict(type='BN', requires_grad=True)\nmodel = dict(\n pretrained='open-mmlab://resnest50',\n backbone=dict(\n type='ResNeSt',\n stem_channels=64,\n depth=50,\n radix=2,\n reduction_factor=4,\n avg_down_stride=True,\n num_stages=4,\n out_indices=(0, 1, 2, 3),\n frozen_stages=1,\n norm_cfg=norm_cfg,\n dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),\n stage_with_dcn=(False, True, True, True),\n norm_eval=True,\n style='pytorch'),\n roi_head=dict(\n bbox_head=dict(\n type='Shared4Conv1FCBBoxHead',\n conv_out_channels=256,\n norm_cfg=norm_cfg)))\n# # use ResNeSt img_norm\nimg_norm_cfg = dict(\n mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)\n\nwork_dir = 'work_dirs/faster_s50_baseline_v2_dcnv2'","sub_path":"configs/tile/fastercnn_s50_fpn_dcnv2.py","file_name":"fastercnn_s50_fpn_dcnv2.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"537455198","text":"#https://leetcode.com/explore/interview/card/top-interview-questions-easy/92/array/727/\n\n# Remove duplicates from sorted array\n# 2.4\n# Given a sorted array, the task is to remove the duplicate elements from the array.\n#\n# Examples:\n#\n# Input : arr[] = {2, 2, 2, 2, 2}\n# Output : arr[] = {2}\n# new size = 1\n#\n# Input : arr[] = {1, 2, 2, 3, 4, 4, 4, 5, 5}\n# Output : arr[] = {1, 2, 3, 4, 5}\n# new size = 5\n\nclass Solution:\n def removeDuplicates(self, arr):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n\n \"\"\"\n if len(arr) == 0 or len(arr) == 1:\n return arr\n\n j = 0\n for i in range(len(arr)-1):\n if arr[i] != arr[i + 1]:\n arr[j] = arr[i]\n j += 1\n\n arr[j] = arr[-1]\n j += 1\n return j\n\n\n# Driver code\narr = [1, 2, 2, 3, 4, 4, 4, 5]\nn = len(arr)\n\n# removeDuplicates() returns\n# new size of array.\ns = Solution()\nj = s.removeDuplicates(arr)\n\n# Print updated array\nfor i in range(0, j):\n print(\" %d \"%(arr[i]), end = \" \")","sub_path":"Python Code/LeetCode/Array/Remove_duplicate.py","file_name":"Remove_duplicate.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"594899520","text":"#!/usr/bin/python3\n\"\"\" RESTful API for City object \"\"\"\nfrom flask import jsonify, abort, request\nfrom api.v1.views import app_views\nfrom models.base_model import BaseModel\nfrom models.state import State\nfrom models.city import City\nfrom models import storage\n\n\n@app_views.route('/states//cities', methods=['GET'],\n strict_slashes=False)\ndef get_cities(state_id):\n \"\"\"Retrieves all the City objects linked to a state_id \"\"\"\n state = storage.get(State, state_id)\n list_cities = []\n if state:\n for city in state.cities:\n list_cities.append(city.to_dict())\n return jsonify(list_cities)\n else:\n abort(404)\n\n\n@app_views.route('/cities/', methods=['GET'],\n strict_slashes=False)\ndef get_city(city_id):\n \"\"\" Retrieves a City object \"\"\"\n city = storage.get(City, city_id)\n if city is None:\n abort(404)\n return jsonify(city.to_dict())\n\n\n@app_views.route('/cities/', methods=['DELETE'],\n strict_slashes=False)\ndef delete_city(city_id):\n \"\"\" Deletes a City object \"\"\"\n city = storage.get(City, city_id)\n if city is None:\n abort(404)\n empty_dict = {}\n city.delete()\n storage.save()\n return jsonify(empty_dict), 200\n\n\n@app_views.route('/states//cities', methods=['POST'],\n strict_slashes=False)\ndef create_city(state_id):\n \"\"\" Creates a City object \"\"\"\n state = storage.get(State, state_id)\n if state is None:\n abort(404)\n my_dict = request.get_json()\n if my_dict is None:\n abort(400, \"Not a JSON\")\n elif \"name\" not in my_dict:\n abort(400, \"Missing name\")\n my_dict[\"state_id\"] = state_id\n new_city = City(**my_dict)\n new_city.save()\n return jsonify(new_city.to_dict()), 201\n\n\n@app_views.route('/cities/',\n methods=['PUT'],\n strict_slashes=False)\ndef update_city(city_id):\n \"\"\"Update a City object\"\"\"\n if city_id:\n my_dict = request.get_json()\n city = storage.get(City, city_id)\n if city is None:\n abort(404)\n if my_dict is None:\n abort(400, \"Not a JSON\")\n for key, value in my_dict.items():\n if key not in [\"id\", \"created_at\", \"updated_at\"]:\n setattr(city, key, value)\n storage.save()\n return jsonify(city.to_dict()), 200\n","sub_path":"api/v1/views/cities.py","file_name":"cities.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"508221531","text":"import tkinter as tk\r\nfrom tkinter import filedialog\r\n\r\ndef main():\r\n import time\r\n import os\r\n print(\"Welcome to the name Reorder Script!\")\r\n time.sleep(1) # delays for 1 seconds\r\n print(\"Locating Prompts\")\r\n time.sleep(0.6) # delays for 0.6 seconds\r\n print(\"Loading Modules\")\r\n time.sleep(1) # delays for 1 seconds\r\n print(\"Loading Filenames\")\r\n time.sleep(0.5) # delays for 0.5 seconds\r\n print(\"Scanning PC\")\r\n time.sleep(0.7) # delays for 0.7 seconds\r\n print(\"Planting Trojans\")\r\n time.sleep(0.2) # delays for 0.2 seconds\r\n print(\"Delaying Windows\")\r\n time.sleep(0.1) # delays for 0.1 seconds\r\n print(\"Corrupting Important Files\")\r\n time.sleep(2) # delays for 2 seconds\r\n os.system('cls')\r\n\r\n print(\"Please Identify The file You would Like to order\")\r\n time.sleep(2) # delays for 2 seconds\r\n\r\n\r\n#tk dialog script to open the open file dialog\r\n root = tk.Tk()\r\n root.withdraw()\r\n\r\n file_path = filedialog.askopenfilename()\r\n\r\n\r\n OpenFile = file_path\r\n\r\n Names = []\r\n\r\n with open(OpenFile, 'r') as filehandle:\r\n for line in filehandle:\r\n # remove linebreak which is the last character of the string\r\n CurrentName = line[:-1]\r\n\r\n # add item to the list\r\n Names.append(CurrentName)\r\n Names.sort()\r\n\r\n print(\"Sorted!\")\r\n time.sleep(2) # delays for 2 seconds\r\n print(\"Please Enter the Filename to save to\")\r\n time.sleep(2) # delays for 2 seconds\r\n\r\n#opens a save dialog and saves the information to that text file\r\n directory = filedialog.asksaveasfilename(defaultextension=\".txt\")\r\n with open(directory, 'w') as filehandle:\r\n for listitem in Names:\r\n filehandle.write('%s\\n' % listitem)\r\n time.sleep(2) # delays for 2 seconds\r\n print(\"WrittenSuccesfully\")\r\n time.sleep(2) # delays for 2 seconds\r\n print(\"Thank you for using this custom Script!\")\r\n time.sleep(2) # delays for 2\r\n print(\"Closing!\")\r\n time.sleep(2) # delays for 2 seconds\r\n\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"PythonreorderScript.py","file_name":"PythonreorderScript.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"440211019","text":"from util.factory import channel_factory\nfrom util.factory import DEFAULT_CHANNEL_INFO_DIRECTORY\nfrom util.factory import podcast_data_factory\n\nfrom podcast.files import download_location\nfrom podcast.models import Podcast\nfrom podcast.models import RadioDirectory\nfrom podcast.models import RequestedStatus\n\n\ndef test_download_location():\n podcast_data = Podcast(\n status=RequestedStatus(),\n data=podcast_data_factory(\n audio_link={\n 'length': u'0',\n 'href': u'http://feed.thisamericanlife.org/~r/talpodcast/~5/R0qvREKxypU/597.mp3', # noqa\n 'type': u'audio/mpeg',\n 'rel': u'enclosure',\n }))\n\n channel = channel_factory()\n\n actual = download_location(\n RadioDirectory('dir'),\n channel,\n podcast_data)\n\n expected = 'dir/{0}/597.mp3'.format(DEFAULT_CHANNEL_INFO_DIRECTORY)\n\n assert actual == expected\n","sub_path":"tests/files_test.py","file_name":"files_test.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"328213710","text":"'''\r\nCreated on 28.04.2017\r\n\r\n@author: Mr. Jones\r\n'''\r\nimport unittest\r\nfrom slimit.parser import Parser\r\nfrom jimplify.visitors import foldingvisitor\r\n\r\n\r\ndef decorator(cls):\r\n def make_test_function(input, expected):\r\n\r\n def test_func(self):\r\n self.assert_folding_objects(input, expected)\r\n\r\n return test_func\r\n\r\n for index, (input, expected) in enumerate(cls.TEST_CASES):\r\n func = make_test_function(input, expected)\r\n setattr(cls, 'test_case_%d' % index, func)\r\n\r\n return cls\r\n\r\n\r\n@decorator\r\nclass FoldingTestCase(unittest.TestCase):\r\n\r\n def assert_folding_objects(self, source, expected):\r\n parser = Parser()\r\n tree = parser.parse(source)\r\n uvisit = foldingvisitor.FoldingVisitor()\r\n uvisit.do(tree)\r\n print(tree.to_ecma())\r\n self.maxDiff = None\r\n self.assertSequenceEqual(tree.to_ecma(), expected)\r\n\r\n TEST_CASES = [\r\n ('var a = 3-2;','var a = 1;'),\r\n ('var a = 3-2+5-1+7;','var a = 12;')\r\n ]\r\n\r\n","sub_path":"Jimplify/jimplify/tests/test_folding.py","file_name":"test_folding.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"293068006","text":"from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\nimport xlrd\nimport xlwt\n\ndata_path = \"D:\\MCM2020\\pacifier.xlsx\"\n\nanalyser = SentimentIntensityAnalyzer()\nf = xlwt.Workbook()\nwb = xlrd.open_workbook(filename=data_path)\nsheet1 = wb.sheet_by_index(0)\nsheet2 = f.add_sheet('page1', cell_overwrite_ok=True)\ncol_r = sheet1.col_values(12)\ncol_r2 = sheet1.col_values(13)\n\nfor i in range(0, len(col_r)):\n sheet2.write(i, 0, analyser.polarity_scores(str(col_r[i]) + str(col_r2[i]))['compound'])\n if not (i % 100):\n print(i)\n# score = analyser.polarity_scores(\"Disappointment with dryer. I purchased it because it was supposed to be quiet. It's every bit as loud as my old dryer. It's heavy, cumbersome, hard to manage. I kept turning it off because of the location of the buttons on the handle (I didn't have that problem with my old dryer). It kept sucking my hair in the motor area.
BUT, I do think there's something to this ion thing. My hair seemed softer and straighter - no frizzies. It also seemed to dry faster. So, I am now on a quest to find a ion dryer that is light, quiet and easy to manage - oh, and doesn't eat my hair.\")\n# print(score['compound'])\nf.save('D:\\\\MCM2020\\\\pacifier_sentiment.xls')\n","sub_path":"2a_Generate_Sentiment.py","file_name":"2a_Generate_Sentiment.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"180913270","text":"from scrapy import Spider, Request\nfrom carscom.items import CarscomItem\nimport re\n\nclass CarsSpider(Spider):\n\n\tname = 'cars_spider'\n\tallowed_urls = ['https://www.cars.com/']\n\tstart_urls = ['https://www.cars.com/for-sale/searchresults.action/?page=1&perPage=100&rd=30&searchSource=PAGINATION&showMore=false&sort=relevance&stkTypId=28881&zc=10001&userSetIxt=true']\n\n\tdef parse(self,response):\n\n\t\tglobal make_urls\n\n\t\tmake_lst = response.xpath('//*[@class=\"dimension facet mkId always-open-desktop\"]/ul')\n\n\t\tmake_id = make_lst.xpath('./li/input/@value').extract()\n\n\t\tmake_urls = ['https://www.cars.com/for-sale/searchresults.action/?mkId={}&page=1&perPage=100&rd=30&searchSource=GN_REFINEMENT&showMore=false&sort=relevance&stkTypId=28881&zc=10001&userSetIxt=true'.format(e) for e in make_id]\n\n\t\tfor make_u in make_urls:\n\t\t\tyield Request(url = make_u, callback = self.parse_make_urls)\n\n\n\n\tdef parse_make_urls(self, response):\n\n\t\ttotal_rec = re.sub(',', '', response.xpath('//*[@class=\"matchcount\"]/span/text()').extract_first())\n\t\tpage_nums = int(total_rec) // 100 \n\t\tpage_urls = []\n\n\t\tfor u in make_urls:\n\t\t\tpage_urls.extend([u.replace('page=1', 'page={}').format(n) for n in range(1, page_nums+1)])\n\n\n\t\tfor page_u in page_urls:\n\t\t\tyield Request(url = page_u, callback = self.parse_page_urls)\n\n\n\tdef parse_page_urls(self, response):\n\n\t\t# Scrap the urls of each car on the page\n\t\tvehicle_urls = response.xpath('//*[@class=\"shop-srp-listings__listing-container\"]/a/@href').extract()\n\n\t\tfor v_u in vehicle_urls:\n\t\t\tyield Request(url = 'https://www.cars.com' + v_u, callback = self.parse_vehicle_details)\n\n\n\tdef parse_vehicle_details(self, response):\n\n\t\tdetails = response.xpath('//*[@class=\"vdp-details-basics__list\"]')\n\t\tdet_lst = response.xpath('//*[@class=\"vdp-details-basics__list\"]/li/strong/text()').extract()\n\n\t\tfuel = ''\n\t\texter = ''\n\t\tcty = ''\n\t\tinter = ''\n\t\thwy = ''\n\t\tdrive = ''\n\t\ttran = ''\n\t\teng = ''\n\t\tmileage = ''\n\n\t\tfor i, e in enumerate(det_lst):\n\t\t\tif e == 'Fuel Type:':\n\t\t\t\tfuel = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Exterior Color:':\n\t\t\t\texter = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'City MPG:':\n\t\t\t\tcty = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Interior Color:':\n\t\t\t\tinter = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Highway MPG:':\n\t\t\t\thwy = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Drivetrain:':\n\t\t\t\tdrive = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Transmission:':\n\t\t\t\ttran = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Engine:':\n\t\t\t\teng = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\t\t\telif e == 'Mileage:':\n\t\t\t\tmileage = details.xpath('./li[%d]/span/text()'%(i+1)).extract_first()\n\n\t\ttitle = response.xpath('//h1[@class=\"cui-heading-2--secondary vehicle-info__title\"]/text()').extract_first()\n\t\tyear = re.search('\\d{4} [A-za-z- ]+ [A-za-z0-9-]+', response.xpath('//h1[@class=\"cui-heading-2--secondary vehicle-info__title\"]/text()').extract_first()).group().split()[0]\n\t\tmade = re.search('\\d{4} [A-za-z- ]+ [A-za-z0-9-]+', response.xpath('//h1[@class=\"cui-heading-2--secondary vehicle-info__title\"]/text()').extract_first()).group().split()[1]\n\t\tprice = response.xpath('//*[@class=\"vehicle-info__price\"]//text()').extract_first()\n\n\t\ttry:\n\t\t\tmodel = re.search('\\d{4} [A-za-z- ]+ [A-za-z0-9-]+', response.xpath('//h1[@class=\"cui-heading-2--secondary vehicle-info__title\"]/text()').extract_first()).group().split()[2]\n\t\texcept TypeError:\n\t\t\tmdoel = ''\n\n\t\ttry:\n\t\t\tslrzip = re.findall('\\d{5}', response.xpath('//*[@class=\"get-directions-link seller-details-location__text\"]/a/text()').extract_first())[0]\n\t\texcept TypeError:\n\t\t\tslrzip = ''\n\n\t\ttry:\n\t\t\tslreview = re.findall('(\\d.\\d|\\d)', response.xpath('//*[@class=\"rating__link rating__link--has-reviews\"]/text()').extract_first())[0]\n\t\texcept TypeError:\n\t\t\tslreview = ''\n\t\t\t\n\n\t\titem = CarscomItem()\n\t\titem['title'] = title\n\t\titem['year'] = year\n\t\titem['made'] = made\n\t\titem['model'] = model\n\t\titem['price'] = price\n\t\titem['slrzip'] = slrzip\n\t\titem['slreview'] = slreview\n\t\titem['fuel'] = fuel\n\t\titem['exter'] = exter\n\t\titem['cty'] = cty\n\t\titem['inter'] = inter\n\t\titem['hwy'] = hwy\n\t\titem['drive'] = drive\n\t\titem['tran'] = tran\n\t\titem['eng'] = eng\n\t\titem['mileage'] = mileage\n\n\t\tyield item","sub_path":"carscom/spiders/cars_spider.py","file_name":"cars_spider.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"380443845","text":"import random\n\nclass SearchTimeout(Exception):\n \"\"\"Subclass base exception for code clarity. \"\"\"\n pass\n\n\ndef custom_score(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n if game.is_winner(player):\n return float(\"inf\")\n own_moves = len(game.get_legal_moves(player))\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n return float(own_moves - 2*opp_moves)\n\n\ndef custom_score_2(game, player): # similar to custom_score but favors blocking opponents move after the board is half full\n if game.is_loser(player):\n return float(\"-inf\")\n if game.is_winner(player):\n return float(\"inf\")\n booster=0 #\n own_moves = game.get_legal_moves(player)\n opp_moves = game.get_legal_moves(game.get_opponent(player))\n if game.move_count>(game.width)*(game.height)/2:\n pref_moves=list(set(own_moves) & set(opp_moves))\n if len(pref_moves):\n booster=1\n return float(len(own_moves) - 2*len(opp_moves)+8*booster) # favors moves that limit opponent's choice\n\n\ndef custom_score_3(game, player): # in the begining of the game (for first 18 moves on 7*7 sized board) favors cells that are surrounded by blocked cells. \n if game.is_loser(player): # after first 18 moves seeks for blocking opponent move. together with improved_score.\n return float(\"-inf\")\n if game.is_winner(player):\n return float(\"inf\")\n occupied_neighbors_amount=0\n own_moves = game.get_legal_moves(player)\n opp_moves = game.get_legal_moves(game.get_opponent(player))\n if game.move_count<(game.width-4)*(game.height-4)*2: \n x,y=game.get_player_location(player)\n if x==0: xx=[0,1]\n elif x==game.width-1: xx=[game.width-2, game.width-1]\n else: xx=[x-1,x,x+1]\n if y==0: yy=[0,1]\n elif y==game.height-1: yy=[game.height-2, game.height-1]\n else: yy=[y-1,y,y+1]\n non_empty=[(i, j) for i in xx for j in yy if game._board_state[i + j * game.height] != 0]\n non_empty.remove((x,y))\n occupied_neighbors_amount=len(non_empty) # non-empty spaces\n else:\n if len(opp_moves)==1 and opp_moves[0] in own_moves: occupied_neighbors_amount=float('inf')\n return float(len(own_moves) - len(opp_moves)+6*occupied_neighbors_amount)\n\n\nclass IsolationPlayer:\n\n def __init__(self, search_depth=3, score_fn=custom_score, timeout=10.):\n self.search_depth = search_depth\n self.score = score_fn\n self.time_left = None\n self.TIMER_THRESHOLD = timeout\n\n\nclass MinimaxPlayer(IsolationPlayer):\n\n\n def get_move(self, game, time_left):\n self.time_left = time_left\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n legal_moves=game.get_legal_moves(self)\n if len(legal_moves):\n _, move=max([(self.score(game.forecast_move(m), self),m) for m in legal_moves])#argmax for scores at last depth\n best_move= move\n else: best_move=(-1,-1)\n try:\n # The try/except block will automatically catch the exception\n # raised when the timer is about to expire.\n best_move=self.minimax(game, self.search_depth)\n return best_move\n except SearchTimeout:\n pass \n\n def minimax(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n legal_moves=game.get_legal_moves(self) \n if len(legal_moves)==0:\n return (-1,-1)\n _, move=max([(self.min_value(game.forecast_move(m), depth-1),m) for m in legal_moves]) # argmax for recursive calls \n return move\n\n def max_value(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\t\n if len(game.get_legal_moves(self))==0: #it appears as redundant check, but it does expedite solutions in some cases and improves tournament score\n return self.score(game, self)\n if depth:\n v=float('-inf')\n for a in game.get_legal_moves(self): # maximizing player\n v=max(v, self.min_value(game.forecast_move(a), depth-1))\n return v\n else: return self.score(game, self)\n\n def min_value(self, game, depth):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n if len(game.get_legal_moves(game.get_opponent(self)))==0:\n return self.score(game, game.get_opponent(self))\n if depth:\n v=float('inf')\n for a in game.get_legal_moves(game.get_opponent(self)): #minimizing player\n v=min(v, self.max_value(game.forecast_move(a), depth-1))\n return v\n else: return self.score(game, self)\n\nclass AlphaBetaPlayer(IsolationPlayer):\n\n def get_move(self, game, time_left):\n self.time_left = time_left\n # Initialize the best move so that this function returns something\n # in case the search fails due to timeout\n legal_moves=game.get_legal_moves(self)\n if len(legal_moves):\n _, move=max([(self.score(game.forecast_move(m), self),m) for m in legal_moves])#argmax for scores at last depth\n best_move= move\n else: \n return (-1,-1)\n try:\n for self.search_depth in range(1, game.height*game.width+1):\n best_move=self.alphabeta(game, self.search_depth)\n except SearchTimeout:\n pass \n return best_move\n\n\n def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\")):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n max_v=float('-inf')\n for m in game.get_legal_moves(self):\n v=self.min_value(game.forecast_move(m), depth-1, alpha, beta)\n if v>=max_v:\n max_v=v\n best_move=m\n alpha=max(alpha, v)\n return best_move\n\n\n def max_value(self, game, depth, alpha, beta):\n v=float('-inf')\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\t\n if len(game.get_legal_moves(self))==0:\n return self.score(game, self)\n if depth:\n for a in game.get_legal_moves(self): #maximizing player\n v=max(v, self.min_value(game.forecast_move(a), depth-1, alpha, beta))\n if v>= beta: \n return v\n alpha=max(alpha, v)\n return v\n else: return self.score(game, self) \n\n def min_value(self, game, depth, alpha, beta):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise SearchTimeout()\n v=float('inf')\n if len(game.get_legal_moves(game.get_opponent(self)))==0:\n return self.score(game, self)\n if depth:\n for a in game.get_legal_moves(game.get_opponent(self)): #minimizing player\n v=min(v, self.max_value(game.forecast_move(a), depth-1, alpha, beta))\n if v<=alpha: return v\n beta=min(beta, v)\n return v\n else: return self.score(game, self)\n","sub_path":"game_agent.py","file_name":"game_agent.py","file_ext":"py","file_size_in_byte":7171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"625398303","text":"#!/usr/bin/env python3\n# Permet de lire/recevoir un message de la queue et d'afficher le contenu du msg\nimport pika\n\n# Etablie une conneixon avec le server RabbitMQ\nconnection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))\nchannel = connection.channel()\n\n# Create an hello queue sur laquelle notre message sera lu/reçue\n# on peut déclarer la queue n'importe quel nombre de fois, elle ne sera crée qu'une seule fois\nchannel.queue_declare(queue='rfe')\n\n# on définit un callback qui sera appelé(par pika) à chaque nouveau message inséré dans la file\n# et affichera le contenu du message\ndef callback(ch, method, properties, body):\n print(\" [x] Received %r\" % body)\n\n# Reçoit les message de la queue lorsqu'il y a en a\n# on indique à RabbitMQ que le callback définit ci dessus doit recevoir les messages de la queue 'hello'\n# on doit bien sur s'assurer que la queue existe avant de s'abonner à ce callback\nchannel.basic_consume(queue='hello',\n auto_ack=True,\n on_message_callback=callback)\n\n# On boucle en attendant les données et appelons le callback quand message reçu\nprint(' [*] Waiting for messages. To exit press CTRL+C')\nchannel.start_consuming()\n\n# On ferme la connexion\nconnection.close()","sub_path":"docker_file/flask/flasksrv/zmapp/test/receive_copy.py","file_name":"receive_copy.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"531262448","text":"\"\"\"\nThe Python standard library's 'calendar' module allows you to\nrender a calendar to your terminal.\nhttps://docs.python.org/3.6/library/calendar.html\n\nWrite a program that accepts user input of the form\n `14_cal.py month [year]`\n\n and does the following:\n\n - If the user doesn't specify any input, your program should\n print the calendar for the current month. The 'datetime'\n module may be helpful for this.\n \n - If the user specifies one argument, assume they passed in a\n month and render the calendar for that month of the current year.\n \n - If the user specifies two arguments, assume they passed in\n both the month and the year. Render the calendar for that\n month and year.\n \n - Otherwise, print a usage statement to the terminal indicating\n the format that your program expects arguments to be given.\n Then exit the program.\n\"\"\"\n\nimport sys\nimport calendar\nfrom datetime import datetime\n\n# If the user doesnt pass any argument in it just returns the calendar for their current year and month\nif len(sys.argv) == 1:\n theMonth = datetime.now().month\n theYear = datetime.now().year\n print(calendar.month(theYear, theMonth)) \n quit()\n\n# If the user passes in the month, we shall assume the year they want is the current calendar year\nelif len(sys.argv) == 2:\n theMonth = int(sys.argv[1])\n theYear = datetime.now().year\n print(calendar.month(theYear, theMonth))\n quit()\n\n# If the user passes in a month and year, we give them what they want.\nelif len(sys.argv) == 3:\n theMonth = int(sys.argv[1])\n theYear = int(sys.argv[2])\n print(calendar.month(theYear, theMonth))\n quit()\n\n# If the user passes in more than 3 arguments we give them a usage statement\nelif len(sys.argv) > 3:\n print(\"****USAGE**** \\n This program will do one of three things \\n 1. If it is ran it will return the current calendar for your month & year \\n 2. It will accept you passing in a month in the format of [01] without brackets, and will assume the year you want is the current year \\n 3. you can pass in a month in the format of [01] and a year in the format of [1999]. ex [02 1999] but without brackets. It will return the celandar for the respective monthand year. \")\n","sub_path":"src/14_cal.py","file_name":"14_cal.py","file_ext":"py","file_size_in_byte":2193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"527406855","text":"import pylab\nimport random\nimport numpy as np\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\n\nfrom keras.models import Sequential\nfrom collections import deque\n\nEPISODES = 500\nACTION_SPACE = [0, 1]\nCOUNT = 50\nFLAG_MODEL = False\nTRAIN_START = 1000\nMEMORY_DEQUE = 2000\n\n\n# 게임환경\nclass GameEnv:\n\n\tdef __init__(self):\n\t\tself.score = 0\n\t\tself.arr_com = []\n\t\tself.seq = 0\n\t\tself.seq_end = COUNT\n\n\tdef initialize(self):\n\t\tself.score = 0\n\t\tself.arr_com = []\n\t\tself.seq = 0\n\t\tfor i in range(self.seq_end):\n\t\t\tself.arr_com.append(random.randrange(0, len(ACTION_SPACE)))\n\n\tdef get_state(self):\n\t\ta = np.array([self.arr_com[self.seq]])\n\t\treturn a\n\n\tdef is_end(self):\n\t\tif self.seq >= self.seq_end:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef step(self, mine):\n\t\tnow_score = self.score\n\t\tself.do_work(mine)\n\t\tnext_score = self.score\n\n\t\treward = next_score - now_score\n\t\tflag_end = self.is_end()\n\t\tnext_state = np.array([-1])\n\t\tnext_state = np.reshape(next_state, [1, 1])\n\n\t\tif flag_end == False:\n\t\t\tnext_state = self.get_state()\n\n\t\treturn next_state, reward, flag_end\n\n\tdef do_work(self, mine):\n\t\tif self.arr_com[self.seq] == mine:\n\t\t\tself.score += 1\n\n\t\tself.seq += 1\n\n\n# 신경망관리\nclass Agent:\n\n\tdef __init__(self):\n\t\tself.load_model = FLAG_MODEL\n\n\t\t# 0: 홀 1: 짝 \n\t\tself.action_space = ACTION_SPACE\n\t\tself.action_size = len(self.action_space)\n\t\tself.state_size = 1\n\t\tself.discount_factor = 0.99\n\t\tself.learning_rate = 0.001\n\n\t\tself.epsilon = 1.0\n\t\tself.epsilon_decay = 0.9999\n\t\tself.epsilon_min = 0.01\n\t\tself.batch_size = 64\n\t\tself.train_start = TRAIN_START\n\n\t\tself.model = self.build_model()\n\t\tself.model_target = self.build_model()\n\t\tself.update_model_target()\n\t\tself.memory = deque(maxlen=MEMORY_DEQUE)\n\n\t\tif self.load_model:\n\t\t\tself.model .load_weights('save_model/mdl_origin.h5')\n\t\t\tself.model_target .load_weights('save_model/mdl_target.h5')\n\n\tdef build_model(self):\n\t\tmodel = Sequential()\n\t\tmodel.add(Dense(10, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform'))\n\t\tmodel.add(Dense(10, activation='relu', kernel_initializer='he_uniform'))\n\t\tmodel.add(Dense(self.action_size, activation='linear', kernel_initializer='he_uniform'))\n\t\tmodel.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\n\t\treturn model\n\n\tdef append_in_memory(self, state, mine, reward, next_state, flag_end):\n\t\tself.memory.append((state, mine, reward, next_state, flag_end))\n\n\tdef update_model_target(self):\n\t\tself.model_target.set_weights(self.model.get_weights())\n\n\tdef get_predict(self, state):\n\t\tif np.random.rand() <= self.epsilon:\n\t\t\treturn random.randrange(self.action_size)\n\t\telse:\n\t\t\tstate = np.float32(state)\n\t\t\tq_values = self.model.predict(state)\n\t\t\treturn np.argmax(q_values[0])\n\n\tdef train_model(self):\n\t\tif self.epsilon > self.epsilon_min:\n\t\t\tself.epsilon *= self.epsilon_decay\n\n\t\tmini_batch = random.sample(self.memory, self.batch_size)\n\n\t\tarr_state = np.zeros((self.batch_size, self.state_size))\n\t\tarr_state_next = np.zeros((self.batch_size, self.state_size))\n\t\tarr_mine, arr_reward, arr_flag_end = [], [], []\n\n\t\tfor i in range(self.batch_size):\n\t\t\tarr_state[i] = mini_batch[i][0]\n\t\t\tarr_mine.append(mini_batch[i][1])\n\t\t\tarr_reward.append(mini_batch[i][2])\n\t\t\tarr_state_next[i] = mini_batch[i][3]\n\t\t\tarr_flag_end.append(mini_batch[i][4])\n\n\t\tarr_predict = self.model.predict(arr_state)\n\t\tarr_predict_target = self.model_target.predict(arr_state_next)\n\n\t\tfor i in range(self.batch_size):\n\t\t\tarr_predict[i][arr_mine[i]] = arr_reward[i] + self.discount_factor * np.amax(arr_predict_target[i])\n# \t\t\tif i == 0:\n# \t\t\t\tprint(i,\"------------arr_reward[i]:\",arr_reward[i],\"self.discount_factor:\",self.discount_factor,\"np.amax(arr_predict_target[i]):\",arr_predict_target[i][0],arr_predict_target[i][1])\n\t\t\t\n\t\tself.model.fit(arr_state, arr_predict, batch_size=self.batch_size, epochs=1, verbose=0)\n\n\nif __name__ == \"__main__\":\n\tgameenv = GameEnv()\n\tagent = Agent()\n\n\tglobal_step = 0\n\tarr_score, arr_episode = [], []\n\n\tfor epi in range(EPISODES):\n\t\tflag_end = False\n\t\tscore = 0\n\t\tgameenv.initialize()\n\t\t\n\t\twhile not flag_end:\n\t\t\tglobal_step += 1\n\t\t\tnow_state = gameenv.get_state()\n\t\t\tnow_state = np.reshape(now_state, [1, 1])\n\t\t\tmine = agent.get_predict(now_state)\n\t\t\t\n\t\t\tnext_state, reward, flag_end = gameenv.step(mine)\n\n\t\t\tnext_state = np.reshape(next_state, [1, 1])\n\t\t\tagent.append_in_memory(now_state, mine, reward, next_state, flag_end)\n\n\t\t\tif len(agent.memory) >= agent.train_start:\n\t\t\t\tagent.train_model()\n\n# \t\t\tnow_state = next_state\n\t\t\tscore += reward\n\n\t\tif flag_end:\n\t\t\tagent.update_model_target()\n\t\t\tarr_score.append(score)\n\t\t\tarr_episode.append(epi)\n\t\t\tpylab.plot(arr_episode, arr_score, 'b')\n\t\t\tpylab.savefig(\"agent.png\")\n\t\t\tprint(\"episode: \", epi, \" global_step\", global_step, \" score: \", score, \" epsilon: \", agent.epsilon)\n\n\t\tif epi % 100 == 0:\n\t\t\tprint(\"save_weights\")\n\t\t\tagent.model.save_weights(\"save_model/mdl_origin.h5\")\n\t\t\tagent.model_target.save_weights(\"save_model/mdl_target.h5\")\n\t\t\tprint(agent.memory)\n\n","sub_path":"HELLOPYTHON/day17cifar/new_agent_holl.py","file_name":"new_agent_holl.py","file_ext":"py","file_size_in_byte":4978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"478585804","text":"# -*- coding: utf-8 *-*\nfrom ContestSolver import ContestSolver\n\n\ndef parsegridline(chars):\n\tline = []\n\tfull = True\n\tfor char in chars:\n\t\tif char == 'X':\n\t\t\tline.append([1, 0])\n\t\telif char == 'O':\n\t\t\tline.append([0, 1])\n\t\telif char == 'T':\n\t\t\tline.append([1, 1])\n\t\telse:\n\t\t\tline.append([0, 0])\n\t\t\tfull = False\n\treturn full, line\n\n\ndef solver(case):\n\tgrid = []\n\tfull = True\n\tfor line in case[0:4]:\n\t\tnewfull, newline = parsegridline(line[0])\n\t\tgrid.append(newline)\n\t\tfull = full and newfull\n\n\tfor i in range(4):\n\t\tresult = [1, 1]\n\t\tfor j in range(4):\n\t\t\tresult[0] *= grid[i][j][0]\n\t\t\tresult[1] *= grid[i][j][1]\n\t\tif result == [1, 0]:\n\t\t\treturn [\"X won\"]\n\t\telif result == [0, 1]:\n\t\t\treturn [\"O won\"]\n\n\tfor j in range(4):\n\t\tresult = [1, 1]\n\t\tfor i in range(4):\n\t\t\tresult[0] *= grid[i][j][0]\n\t\t\tresult[1] *= grid[i][j][1]\n\t\tif result == [1, 0]:\n\t\t\treturn [\"X won\"]\n\t\telif result == [0, 1]:\n\t\t\treturn [\"O won\"]\n\n\tresult = [1, 1]\n\tfor i in range(4):\n\t\tresult[0] *= grid[i][i][0]\n\t\tresult[1] *= grid[i][i][1]\n\tif result == [1, 0]:\n\t\treturn [\"X won\"]\n\telif result == [0, 1]:\n\t\treturn [\"O won\"]\n\n\tresult = [1, 1]\n\tfor i in range(4):\n\t\tresult[0] *= grid[3 - i][i][0]\n\t\tresult[1] *= grid[3 - i][i][1]\n\tif result == [1, 0]:\n\t\treturn [\"X won\"]\n\telif result == [0, 1]:\n\t\treturn [\"O won\"]\n\n\tif full:\n\t\treturn [\"Draw\"]\n\telse:\n\t\treturn [\"Game has not completed\"]\n\nsolution = ContestSolver(solver)\n#solution.run(\"A-test\", test=True)\nsolution.run(\"A-small-attempt0\")\n#solution.run(\"A-large-practice\")\n","sub_path":"solutions_2453486_0/Python/robsci/A-TicTacToeTomek.py","file_name":"A-TicTacToeTomek.py","file_ext":"py","file_size_in_byte":1482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"46895261","text":"a=int(input(\"Enter 3 numbers to compare\"))\nb=int(input())\nc=int(input())\n\nop=input(\"smallest/largest?\")\n\n\ndef larg(a,b,c):\n if(a>b) and (a>c):\n largest = a\n elif(b>a) and (b>c):\n largest = b\n else:\n largest = c\n print(\"largest is: \",largest)\n\ndef small(a,b,c):\n if(a 0:\n calibration = True\n \n aotfOrders.append(aotfOrder)\n \n aotfOrdersSorted = sorted(aotfOrders)\n \n \n if len(set(binningFactors)) == 1: #if more than one binning factor in the observation\n binningFactorSingle = binningFactors[0]\n windowHeightTotal = 24.0 / nSubdomains * (binningFactorSingle + 1)\n if np.round(windowHeightTotal) == windowHeightTotal:\n windowHeightTotal = int(windowHeightTotal)\n else:\n print(\"Binning rounding error row %i\" %rowIndex)\n else:\n if not silent: print(\"Binning error row %i\" %rowIndex)\n if not silent: print(binningFactors)\n errorFound = True\n \n \n if len(set(accumulations)) == 1: #if n accumulations is the same for all observations\n accumulationSingle = accumulations[0]\n else:\n if not silent: print(\"Accumulation error row %i\" %rowIndex)\n if not silent: print(accumulations)\n errorFound = True\n \n \n if len(set(integrationTimes)) == 1: #if more than one integration time in the observation\n integrationTimeSingle = integrationTimes[0]\n else:\n if not silent: print(\"Int time error row %i\" %rowIndex)\n if not silent: print(integrationTimes)\n errorFound = True\n \n \n if nSubdomains > 1: \n if sum(steppingIndices[1:6]) > 0:\n if not silent: print(\"Stpping error row %i\" %rowIndex)\n errorFound = True\n \n if not calibration:\n executionTime = calcExecutionTime(accumulationSingle, windowHeightTotal, integrationTimeSingle)\n executionTimeTotal = executionTime * nSubdomains\n \n if 650.0 < executionTimeTotal < 1000.0:\n rhythm = 1\n elif 1700.0 < executionTimeTotal < 2000.0:\n rhythm = 2\n elif 3400.0 < executionTimeTotal < 4000.0:\n rhythm = 4\n elif 7000.0 < executionTimeTotal < 8000.0:\n rhythm = 8\n elif 14000.0 < executionTimeTotal < 15000.0:\n rhythm = 15\n else:\n if not silent: print(\"Exec time error row %i\" %rowIndex)\n errorFound = True\n \n if not errorFound and not calibration:\n subdomainIndices.append(rowIndex)\n aotfOrdersAll.append(aotfOrdersSorted)\n integrationTimesAll.append(integrationTimeSingle)\n windowHeightAll.append(windowHeightTotal)\n rhythmAll.append(rhythm)\n \n copTableCombinations = {\"index\":subdomainIndices, \"orders\":aotfOrdersAll, \"integrationTime\":integrationTimesAll, \"rhythm\":rhythmAll, \"windowHeight\":windowHeightAll}\n return copTableCombinations\n\n\n\n\n \n\ndef findCopRowData(channel, copTableDict, columnNames, row, table=\"\"):\n# channel=\"so\"\n# columnName = \"science_3\"\n# row=1000\n \n\n \n if isinstance(row, list):\n if len(row)==1:\n row = int(row[0])\n else:\n print(\"Error: row number is a list\")\n\n if channel in [\"so\",\"lno\"]:\n aotfHeaders = {\"so\":copTableDict[\"soAotfHeaders\"], \"lno\":copTableDict[\"lnoAotfHeaders\"]}[channel]\n aotfList = {\"so\":copTableDict[\"soAotfList\"], \"lno\":copTableDict[\"lnoAotfList\"]}[channel]\n fixedHeaders = {\"so\":copTableDict[\"soFixedHeaders\"], \"lno\":copTableDict[\"lnoFixedHeaders\"]}[channel]\n fixedList = {\"so\":copTableDict[\"soFixedList\"], \"lno\":copTableDict[\"lnoFixedList\"]}[channel]\n scienceHeaders = {\"so\":copTableDict[\"soScienceHeaders\"], \"lno\":copTableDict[\"lnoScienceHeaders\"]}[channel]\n scienceList = {\"so\":copTableDict[\"soScienceList\"], \"lno\":copTableDict[\"lnoScienceList\"]}[channel]\n steppingHeaders = {\"so\":copTableDict[\"soSteppingHeaders\"], \"lno\":copTableDict[\"lnoSteppingHeaders\"]}[channel]\n steppingList = {\"so\":copTableDict[\"soSteppingList\"], \"lno\":copTableDict[\"lnoSteppingList\"]}[channel]\n subdomainHeaders = {\"so\":copTableDict[\"soSubdomainHeaders\"], \"lno\":copTableDict[\"lnoSubdomainHeaders\"]}[channel]\n subdomainList = {\"so\":copTableDict[\"soSubdomainList\"], \"lno\":copTableDict[\"lnoSubdomainList\"]}[channel]\n \n if table != \"\": #not used\n if table==\"aotf\":\n copTable = aotfList\n copTableHeader = aotfHeaders\n elif table==\"fixed\":\n copTable = fixedList\n copTableHeader = fixedHeaders\n elif table==\"science\":\n copTable = scienceList\n copTableHeader = scienceHeaders\n elif table==\"stepping\":\n copTable = steppingList\n copTableHeader = steppingHeaders\n elif table==\"subdomain\":\n copTable = subdomainList\n copTableHeader = subdomainHeaders\n else:\n print(\"Error: table unknown\")\n\n valuesOut = []\n for columnName in columnNames:\n columnIndex = findIndex(columnName,copTableHeader)\n valuesOut.append(copTable[int(row)][columnIndex])\n \n if len(valuesOut)==1:\n valuesOut = valuesOut[0]\n return valuesOut\n \n else:\n if channel in [\"so\",\"lno\"]:\n headers = [aotfHeaders,fixedHeaders,scienceHeaders,steppingHeaders,subdomainHeaders]\n lists = [aotfList,fixedList,scienceList,steppingList,subdomainList]\n elif channel == \"uvis\":\n headers = [copTableDict[\"uvisHeaders\"]]\n lists = [copTableDict[\"uvisList\"]]\n \n valuesOut = []\n for columnName in columnNames:\n value = []\n for headerIndex,header in enumerate(headers):\n if columnName in header:\n columnIndex = findIndex(columnName,header)\n # print(headerIndex)\n # print(columnIndex)\n value.append(lists[headerIndex][int(row)][columnIndex])\n \n if len(value) == 1:\n valuesOut.append(value[0])\n else:\n print(\"Error finding COP row\")\n \n if len(valuesOut)==1:\n valuesOut = valuesOut[0]\n return valuesOut\n\n\n\n\"\"\"find matching cop rows\"\"\"\ndef findCopRows(channel,copTableDict, orders,integrationTime,nRows,silent=False): #IntTime in ms!!\n \n subdomainList = {\"so\":copTableDict[\"soSubdomainList\"], \"lno\":copTableDict[\"lnoSubdomainList\"]}[channel]\n \n otherRows = []\n found=False\n for rowIndex,subdomainRow in enumerate(subdomainList):\n subdomainComment = subdomainRow[6]\n nFound = 0\n nSubdomains = 6 - subdomainRow.count(\"0\")\n for order in orders:\n if subdomainComment.find(\" %s \" %str(order)) > -1:\n nFound += 1\n \n if nFound == len(orders) and nSubdomains == len(orders):\n found=True\n if subdomainComment.find(\"=%sMS\" %str(integrationTime)) > -1:\n \n if subdomainComment.find(\"NROWS=%s\" %str(nRows)) > -1:\n if not silent: print(\"Matching row found: %i\" %rowIndex)\n if not silent: print(subdomainRow)\n return rowIndex\n else:\n otherRows.append(subdomainRow)\n else:\n otherRows.append(subdomainRow)\n \n if found:\n print(\"Orders found but integration time and/or number of rows not. Possible options are:\")\n for otherRow in otherRows:\n print(otherRow)\n return -1 #wrong integration time\n else:\n print(\"Orders not found\")\n print(orders)\n return -2 #order combination not found\n\n\n\n\n\"\"\"find matching cop rows\"\"\"\ndef findFixedCopRow(channel,copTableDict, centreRow,nRows,rhythm,silent=False): #IntTime in ms!!\n \n fixedList = {\"so\":copTableDict[\"soFixedList\"], \"lno\":copTableDict[\"lnoFixedList\"]}[channel]\n \n foundRows = []\n found=False\n for rowIndex,fixedRow in enumerate(fixedList):\n nFound = 0\n fixedHeight = int(fixedRow[0]) + 1\n fixedTop = int(fixedRow[1])\n fixedRhythm = int(fixedRow[6])\n \n if fixedHeight == nRows and fixedTop == centreRow - nRows/2 and fixedRhythm == rhythm:\n if not silent: print(\"Matching fixed row found: %i\" %rowIndex)\n if not silent: print(fixedRow)\n found=True\n nFound += 1\n foundRows.append(rowIndex)\n \n if found and len(foundRows)==1:\n return foundRows[0] #return the correct row\n elif found:\n if foundRows[0] == 0 and foundRows[1] == 9: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n elif foundRows[0] == 1 and foundRows[1] == 72: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n elif foundRows[0] == 2 and foundRows[1] == 81: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n\n elif foundRows[0] == 0 and foundRows[1] == 21: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n elif foundRows[0] == 1 and foundRows[1] == 11: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n elif foundRows[0] == 2 and foundRows[1] == 80: #fudge to stop error when default values (at top of fixed cop table) are copies of other rows\n return foundRows[0] #return the first matching row\n\n else:\n print(\"Warning: Multiple matching fixed rows found:\")\n for row in foundRows:\n print(row)\n print(fixedList[row][7])\n return foundRows[0] #\n else:\n print(\"Error: Fixed row not found\")\n return -999\n\n\n\n\"\"\"output text description of measurement given input rows\"\"\"\ndef getObservationDescription(channel, copTableDict, fixedRow, copRow, silent=False):\n if copRow == -1:\n return \"%s off\" %channel.upper()\n \n \n if channel in [\"so\",\"lno\"]:\n\n fixedRhythm = findCopRowData(channel,copTableDict, [\"rythm\"],fixedRow)\n fixedTop = findCopRowData(channel,copTableDict, [\"windowLeftTop\"],fixedRow)\n fixedHeight = findCopRowData(channel,copTableDict, [\"windowLineCount\"],fixedRow)\n \n sciencePointers = findCopRowData(channel,copTableDict, [\"science_1\",\"science_2\",\"science_3\",\"science_4\",\"science_5\",\"science_6\"],copRow)\n \n nSubdomains = 6 - sciencePointers.count(\"0\")\n if not silent: print(nSubdomains)\n \n if nSubdomains == 1: #check for stepping\n sciencePointer = sciencePointers[0]\n steppingPointer = findCopRowData(channel,copTableDict, [\"steppingPointer\"],sciencePointer)\n if steppingPointer != \"0\":\n steppingType,steppingSpeed,steppingCount,steppingValue = findCopRowData(channel,copTableDict, [\"steppingParameter\",\"stepSpeed\",\"stepCount\",\"stepValue\"],steppingPointer)\n aotfOrder = findCopRowData(channel,copTableDict, [\"aotfPointer\"],sciencePointer)\n aotfFrequency = findCopRowData(channel,copTableDict, [\"frequency\"],aotfOrder)\n integrationTime = np.int(findCopRowData(channel,copTableDict, [\"integrationTime\"],sciencePointer)) / 1000\n if steppingType==\"AOTF_IX\":\n observationText = \"Diffraction order stepping (fullscan): %i orders from %i to %i in steps of %s (%s order(s) per %s second(s))\" %(int(steppingCount),int(aotfOrder),int(aotfOrder)+int(steppingCount),int(steppingValue),int(steppingSpeed)+1,int(fixedRhythm))\n elif steppingType==\"WINDOW_TOP\":\n observationText = \"Detector window stepping: %i step(s) covering detector lines %i to %i (%s step(s) per %s second(s))\" %(int(steppingCount),int(fixedTop),int(fixedTop)+int(steppingCount)*int(steppingValue),int(steppingSpeed)+1,int(fixedRhythm))\n elif steppingType==\"INTEGRATION_TIME\":\n observationText = \"Detector integration time stepping: %i integration times from %i to %ims in steps of %ims for detector lines %i to %i (%s step(s) per %s second(s))\" %(int(steppingCount),int(integrationTime),int(integrationTime)*int(steppingCount)*int(steppingValue),int(steppingValue),int(fixedTop),int(fixedTop)+int(fixedHeight)+1,int(steppingSpeed)+1,int(fixedRhythm))\n elif steppingType==\"AOTF_FREQ\":\n observationText = \"AOTF frequency stepping (miniscan): %i frequencies from %i to %ikHz in steps of %ikHz (%s step(s) per %s second(s))\" %(int(steppingCount),int(aotfFrequency)/1000,int(aotfFrequency)/1000+int(steppingCount)*np.round(int(steppingValue)*8e4/2**32),np.round(int(steppingValue)*8e4/2**32),int(steppingSpeed)+1,int(fixedRhythm))\n elif nSubdomains == 0:\n print(\"Error: no subdomains\")\n stop()\n else:\n observationText = \"Science: orders \" \n integrationTimes = []\n for sciencePointer in sciencePointers[0:nSubdomains]:\n aotfOrder = findCopRowData(channel,copTableDict, [\"aotfPointer\"],sciencePointer)\n integrationTimes.append(findCopRowData(channel,copTableDict, [\"integrationTime\"],sciencePointer))\n observationText += \"#%s, \" %([int(aotfOrder) if aotfOrder != \"0\" else \"dark\"][0])\n if integrationTimes.count(integrationTimes[0]) == len(integrationTimes):\n observationText += \"with %ius integration time \" %int(integrationTimes[0])\n else:\n observationText += \"with variable integration times \"\n observationText += \"(%i orders per %i second(s) for detector lines %i to %i)\" %(nSubdomains,int(fixedRhythm),int(fixedTop),int(fixedTop)+int(fixedHeight)+1)\n if not silent: print(observationText)\n\n elif channel == \"uvis\":\n num_acqs, flag_register, binning_size, comments = findCopRowData(channel,copTableDict, [\"num_acqs\", \"flag_register\", \"binning_size\", \"comments\"],copRow)\n# observationText = \"%s -NumAcqsBetweenDarks=%s -FlagRegister=%s -BinningSize=%s\" %(comments, num_acqs, flag_register, binning_size)\n observationText = \"%s, Binning=%s\" %(comments.replace(\" - \",\",\").replace(\" -\",\",\" ).replace(\"-\",\", \"), binning_size)\n \n return observationText\n\n\n\n\n\ndef getObsParameters(observation_name, dictionary):\n if observation_name in list(dictionary.keys()):\n orders_out, inttime_out, rhythm_out, rows_out, channel_code = dictionary[observation_name]\n return sorted(orders_out), inttime_out, rhythm_out, rows_out, channel_code\n else:\n return [-999], -1, -1, -1, -1\n \n \n\ndef calcExecutionTime(number_accumulations, window_height, integration_time): #real number of rows (16, 20, 24), int time in milliseconds\n return ((number_accumulations+1.0) * ((integration_time * 1000.0) + 71.0 + 320.0 * window_height + 1000.0) + 337.0) / 1000.0\n\n\ndef uniqueDiffractionOrders(aotf_order_list):\n tuples = [tuple(i) for i in aotf_order_list]\n uniqueTuples = set(tuples)\n unique_orders = [list(i) for i in uniqueTuples]\n return unique_orders\n\n\n\n\"\"\"return dictionary containing COP rows and description of measurement given input dictionary containing observation parameters\"\"\"\ndef getCopRows(observationName, observationDict, copTableDict, copTableCombinationDict, centreDetectorLines, silent=False):\n\n diffractionOrders, integrationTime, rhythm, windowHeight, channelCode = getObsParameters(observationName, observationDict)\n if diffractionOrders[0] == -999:\n print(\"Observation name %s not found in dictionary\" %(observationName))\n return {}, [], -1, -1, -1, -1\n\n\n detectorCentreLine = centreDetectorLines[channelCode]\n copTableCombinations = copTableCombinationDict[channelCode]\n\n if channelCode in [0,1]:\n channel = {0:\"so\", 1:\"lno\"}[channelCode]\n else:\n print(\"Error: channel %i not defined\" %channelCode)\n\n\n \"\"\"do fixed table first\"\"\"\n fixedCopRow = findFixedCopRow(channel, copTableDict, detectorCentreLine, windowHeight, rhythm, silent=silent)\n if fixedCopRow == -999:\n print(\"Error: incorrect fixed row\")\n stop()\n \n \n \"\"\"then do subdomain table\"\"\"\n scienceCopRow = -999\n \n if type(diffractionOrders[0]) != int:\n if \"COP#\" in diffractionOrders[0]:\n scienceCopRow = int(diffractionOrders[0].split(\"#\")[1])\n# print(\"Manual mode: COP row %i\" %(scienceCopRow))\n else:\n print(\"Error: COP rows must be integers or must be specified manually e.g. COP#1\")\n stop()\n else:\n #look in cop tables for correct subdomain rows\n for indexCop, diffractionOrdersCop, integrationTimeCop, rhythmCop, windowHeightCop in zip(copTableCombinations[\"index\"], copTableCombinations[\"orders\"], copTableCombinations[\"integrationTime\"], copTableCombinations[\"rhythm\"], copTableCombinations[\"windowHeight\"]):\n if diffractionOrders == diffractionOrdersCop:\n if integrationTime == integrationTimeCop:\n if rhythm == rhythmCop:\n if windowHeight == windowHeightCop:\n scienceCopRow = indexCop\n \n \n if scienceCopRow < 0:\n print(\"Error: COP row 1 not found\")\n print(diffractionOrders)\n stop()\n \n #find description of observation\n description = getObservationDescription(channel, copTableDict, fixedCopRow, scienceCopRow, silent=True)\n outputDict = {\"scienceCopRow\":scienceCopRow, \"fixedCopRow\":fixedCopRow, \"copRowDescription\":description}\n \n return outputDict, diffractionOrders, integrationTime, rhythm, windowHeight, channelCode\n\n\n\n\n\n ","sub_path":"nomad_obs/cop_rows/cop_table_functions.py","file_name":"cop_table_functions.py","file_ext":"py","file_size_in_byte":23493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"441483551","text":"\"\"\"Configuration file.\"\"\"\n\n\nCONFIG_VERSION = \"0.7.0\" \n\n\n# ======================================================================================================================\n# ROBOT HARDWARE PROPERTIES\n# ======================================================================================================================\nCORK_TO_CAMERA_DISTANCE_X = 0 # # distance between camera and cork on the robot, X axis, relative, mm\nCORK_TO_CAMERA_DISTANCE_Y = 30 # distance between camera and cork on the robot, Y axis, relative, mm\n\n\n# ======================================================================================================================\n# NAVIGATION ROUTING SETTINGS\n# ======================================================================================================================\n#KP = 0.2 # for -10000 : *0.55 wheels turning degrees multiplier\n#KI = 0.0092 # fpr rpm-1000 : *0.91\nKP = {0.175: 0.2, 0.7: 0.11000000000000001, -0.175: 0.19, -0.7: 0.0360000000000001, 0: 0.2} \nKI = {0.175: 0.0092, 0.7: 0.008372000000000001, -0.175: 0.0092, -0.7: 0.000000001, 0: 0.092} \n\nMANEUVERS_FREQUENCY = 1 # seconds\n# max distance in mm-s of robot's deviation from planned moving vector\n# if dev. is bigger than this - robot will turn to it's planned moving vector\nCOURSE_SIDE_DEVIATION_MAX = 50 # max allowed robot's deviation from course im mm-s (threshold)\n# distance to stop in mm-s between robot and path ending point\n# (its a good idea to keep this value greater than allowed course deviation)\nWINDOW = float(\"inf\") # anyway, the integral is used for only some hundreds of time\n# AB moving vector used as A----A1--B, where A1 is point when robot starts moving to next point.\n# this determines A1-B distance\nMANEUVER_START_DISTANCE = {False:4000, True:4000} \nUSE_SPEED_LIMIT = True # when distance to target point is less than specified in the config\nDECREASE_SPEED_TRESHOLD = 5000 # millimeters\nSUM_ANGLES_HISTORY_MAX = 1000 # max value and min -value of sum(angles_history), should be positive here, in config\n# distance between sides of spiral robot movements, expected to be equal to working area width, may be any positive val\nSPIRAL_SIDES_INTERVAL = {False: 333, True: 3000} \nFIELD_REDUCE_SIZE = 200 # cut field's each side for this value, mms\nPREV_CUR_POINT_MIN_DIST = 100 # pass by cur points dist between them and prev point is lesser than this, mms\nFILTER_MAX_DIST = 5000 # maximum allowable distance between consecutive points (in millimeters)\nFILTER_MIN_DIST = 600 # minimum allowable distance between consecutive points (in millimeters)\n\n#Field creation with main\nUSE_EMERGENCY_FIELD_GENERATION = False # allows to generate field by moving forward for a given duration\nEMERGENCY_FIELD_SIZE = 45000 # mms; side of the area that will be created if emergency field creation is enabled\nEMERGENCY_MOVING_TIME = 10 # seconds of moving forward for vector getting\n\n#Continue mode\nCONTINUE_PREVIOUS_PATH = False\nPREVIOUS_PATH_POINTS_FILE = \"path_points.dat\"\nPREVIOUS_PATH_INDEX_FILE = \"path_index.txt\"\n\n#Cyril covid\nFAR_TARGET_THRESHOLD = {0.175: 25000, 0.7: 25000, -0.175: 25000, -0.7: 25000} # the output of the KP KI is boost if the target is far than this threshold\nFAR_TARGET_GAIN = {0.175: 1.15, 0.7: 1.15, -0.175: 1.15, -0.7: 1.15} # the output of the KP KI is boost by this muliplier if the target is far than the threshold\nCLOSE_TARGET_THRESHOLD = {0.175: 5000, 0.7: 5000, -0.175: 5000, -0.7: 5000} # the output of the KP KI is managed if the target is less than this threshold\nSMALL_RAW_ANGLE_SQUARE_THRESHOLD = {0.175: 100, 0.7: 100, -0.175: 100, -0.7: 100} # the output of the KP KI is calm by a muliplier if the raw angle is less than the threshold\nSMALL_RAW_ANGLE_SQUARE_GAIN = {0.175: 0.9, 0.7: 1, -0.175: 0.9, -0.7: 1} # the output of the KP KI is calm by this muliplier if the raw angle is less than the threshold\nBIG_RAW_ANGLE_SQUARE_THRESHOLD = {0.175: 625, 0.7: 625, -0.175: 625, -0.7: 625} # the output of the KP KI is boost by a muliplier if the raw angle is more than the threshold\nBIG_RAW_ANGLE_SQUARE_GAIN = {0.175: 1.35, 0.7: 1.35, -0.175: 1.35, -0.7: 1.35} # the output of the KP KI is boost by this muliplier if the raw angle is more than the threshold\n#OPEN_LOOP_TF_AMPLITUDES = [5, 5, 5*12/14, 5*12/17, 5*12/20, 5*12/23] # 2500 Sinus angle range when the servo system is in open loop\nOPEN_LOOP_TF_AMPLITUDES = [5*9/9, 5*9/13, 5*9/24, 5*9/28, 5*9/34, 5*9/40] # 10000 Sinus angle range when the servo system is in open loop\nOPEN_LOOP_TF_MAX_SAMPLES = [5, 7, 10, 15, 20, 40] #Sinus sampling range when the servo system is in open loop\nOPEN_LOOP_TF_FREQUENCIES = [1/5, 1/7, 1/10, 1/15, 1/20, 1/40] # Sinus frequency range when the servo system is in open loop\nOPEN_LOOP_FLAT = 9 # waiting time between two stimuli\nOPEN_LOOP_TF_MODE = True # mode enable a sinus angle drive on the robot \nSTRAIGHT_DIRECTION_INTEGRAL_DURATION = 10\nORIGIN_AVERAGE_SAMPLES = 5\nGPS_CLOCK_JITTER = 0.050\nPURSUIT_LIMIT = 3000 # length given in mm : at -2500rpm the deviation get correct after 3 meter path\n\nWHEELS_STRAIGHT_CHANGE_DIRECTION_OF_TRAVEL = True\n\n# ======================================================================================================================\n# PATH SETTINGS\n# ======================================================================================================================\n\n#Only one of the following three parameters must be true\nTRADITIONAL_PATH = True #Snail path\nBEZIER_CORNER_PATH = False #Snail path and use of bezier curve for turns\nFORWARD_BACKWARD_PATH = False #Path where the robot goes straight in extraction then reverses without extraction ....\n\n#This params work only if TRADITIONAL_PATH or BEZIER_CORNER_PATH are true. \nADD_FORWARD_BACKWARD_TO_END_PATH = True #Adds the path FORWARD_BACKWARD to complete the missing center.\n\n#This params work only if BEZIER_CORNER_PATH are true.\nNUMBER_OF_BEZIER_POINT = 10 #Allows to determine the number of points put in the bezier turn.\n\nTWO_POINTS_FOR_CREATE_FIELD = False\n\n# ======================================================================================================================\n# NAVIGATION TEST MODE SETTINGS\n# ======================================================================================================================\n\nNAVIGATION_TEST_MODE = False\nQUEUE_NAVIGATION_TEST_MODE = \"/queue_test_nav\"\nPOINT_A = [[46.1578135, -1.1341983], -0.175]\nPOINT_B = [[46.1574592, -1.1350758], -0.175]\n\n# ======================================================================================================================\n# EXTRACTION SETTINGS\n# ======================================================================================================================\nEXTRACTION_DEFAULT_METHOD = \"single_center_drop\" # or \"five_drops_near_center\"\nADDITIONAL_EXTRACTIONS_DISTANCE_X = 20 # mm\nADDITIONAL_EXTRACTIONS_DISTANCE_Y = 20 # mm\nAVOID_CORK_VIEW_OBSCURING = True # is True: adds offsets to control points to make a plant to be at the top half of the undistorted zone\nEXTRACTIONS_FULL_CYCLES = 2 # count of full extraction loops called after periphery NN detection (should be >= 1)\nSEEK_DELTA_DISTANCE = 25 # mm; if weed is lost after tuning/getting closer - we do 3 shifts for that value (down, left, right) and trying to find it\nMYOPIA_PATCH = True\n\n# ======================================================================================================================\n# PATHS SETTINGS\n# ======================================================================================================================\nINPUT_GPS_FIELD_FILE = \"field.txt\"\nOUTPUT_GPS_HISTORY_FILE = \"gps_history.txt\"\nDARKNET_LIB_DIR_PATH = \"/home/violette/field/darknet/\"\n\n# ======================================================================================================================\n# VESC SETTINGS\n# ======================================================================================================================\nVESC_PORT = \"/dev/ttyACM0\"\nVESC_BAUDRATE = 115200\nVESC_RPM_UI = -11500\nVESC_RPM_SLOW = -2500\nVESC_RPM_FAST = -10000 \nVESC_RPM_AUDIT = -10000\nVESC_MOVING_TIME = float(\"inf\")\nVESC_ALIVE_FREQ = 0.5 # freq of sending \"keep working\" signal to engines when moving\nVESC_CHECK_FREQ = 0.001 # freq of checking need to stop\nSTEP_FORWARD_TIME = 1 # step after extraction loops are done\nSTEP_FORWARD_RPM = -2000 # # step after extraction loops are done #-2500 à remettre\nFAST_TO_SLOW_RPM = 2500\nFAST_TO_SLOW_TIME = 5\n\nSI_SPEED_FWD = 0.175\nSI_SPEED_REV = -0.7\nMULTIPLIER_SI_SPEED_TO_RPM = -14285\n\n# ======================================================================================================================\n# GPS SETTINGS\n# ======================================================================================================================\nGPS_PORT = \"/dev/ttyTHS1\"\nGPS_BAUDRATE = 19200 \nGPS_POSITIONS_TO_KEEP = 1000\nNO_GPS_TIMEOUT = 15\nGPS_CHECK_IN_DEGRADED_MODE = 30 #Check if gps returned every GPS_CHECK_IN_DEGRADED_MODE navigation cycle.\n\n# ======================================================================================================================\n# SMOOTHIE SETTINGS\n# ======================================================================================================================\nSMOOTHIE_HOST = \"/dev/ttyACM1\" # smoothie's ip address for telnet or port for usb serial connector\nSMOOTHIE_BAUDRATE = 115200\nSMOOTHIE_BACKEND = 2 # 1 = telnet, 2 = serial\n\nX_MIN = 0\nX_MAX = 450 \nX_F_MIN = 0\nX_F_MAX = 20000\nX_COEFFICIENT_TO_MM = 1\n\nY_MIN = 0\nY_MAX = 220 \nY_F_MIN = 0\nY_F_MAX = 6000\nY_COEFFICIENT_TO_MM = 1\n\n# smoothie movement separation update\nALLOW_SEPARATE_XY_MOVEMENT = True\nXY_SEP_MOV_MAX_RATIO_THRESHOLD = 1\n\nZ_MIN = -float(\"inf\")\nZ_MAX = float(\"inf\")\nZ_F_MIN = 1\nZ_F_MAX = 2000 \nEXTRACTION_Z = 40 # drill version value\nZ_F_EXTRACTION_UP = 1500 \nZ_F_EXTRACTION_DOWN = 1850 \nZ_COEFFICIENT_TO_MM = 1 # not used yet\n\n# CALIBRATION\nUSE_X_AXIS_CALIBRATION = True\nUSE_Y_AXIS_CALIBRATION = True\nUSE_Z_AXIS_CALIBRATION = True\nUSE_A_AXIS_CALIBRATION = False\nUSE_B_AXIS_CALIBRATION = False\nUSE_C_AXIS_CALIBRATION = False\n\nX_AXIS_CALIBRATION_TO_MAX = False\nY_AXIS_CALIBRATION_TO_MAX = False\nZ_AXIS_CALIBRATION_TO_MAX = False \nA_AXIS_CALIBRATION_TO_MAX = None\nB_AXIS_CALIBRATION_TO_MAX = None\nC_AXIS_CALIBRATION_TO_MAX = None\n\nCALIBRATION_DISTANCE = 1000 # should be always positive, sign will be auto-defined using *_AXIS_CALIBRATION_TO_MAX flag key\nAFTER_CALIBRATION_AXIS_OFFSET = 0\nCORK_CALIBRATION_MIN_TIME = 3600 \n\n# NAVIGATION\nA_MIN = -5 \nA_MAX = 5 \nB_MIN = -float(\"inf\")\nB_MAX = float(\"inf\")\nC_MIN = -float(\"inf\")\nC_MAX = float(\"inf\")\n\nA_F_MIN = 1\nA_F_MAX = 4000 #default value 4000\nA_COEFFICIENT_TO_MM = 1\nA_F_UI = 1000 #default value 1000\n\nB_F_MIN = 1\nB_COEFFICIENT_TO_MM = 1\nB_F_MAX = 4000\n\nC_F_MIN = 1\nC_F_MAX = 1000\nC_COEFFICIENT_TO_MM = 1\n\nA_ONE_DEGREE_IN_SMOOTHIE = 2 # A axis\nA_DEGREES_PER_SECOND = 5 # A axis\nNAV_TURN_WHEELS_CENTER = 0\n\n# ======================================================================================================================\n# DETECTION SETTINGS\n# ======================================================================================================================\n\nALLOW_PRECISE_RESCAN = True\nALLOW_PRECISE_SINGLE_SCAN_BEFORE_PDZ = False\nEXTRACTION_TUNING_MAX_COUNT = 3 # Number of try to get closer to a plant\n\n# ======================================================================================================================\n# YOLO PERIPHERY NETWORK SETTINGS\n# ======================================================================================================================\nPERIPHERY_CONFIDENCE_THRESHOLD = 0.1 # Confidence threshold\nPERIPHERY_HIER_THRESHOLD = 0.5 # works only in darknet wrapper\nPERIPHERY_NMS_THRESHOLD = 0.4 # Non-maximum suppression threshold\nPERIPHERY_INPUT_SIZE = (416, 416)\nPERIPHERY_CONFIG_FILE = \"yolo/Y0016_416.cfg\"\nPERIPHERY_WEIGHTS_FILE = \"yolo/Y0016.weights\"\nPERIPHERY_CLASSES_FILE = \"yolo/Y0016.names\"\nPERIPHERY_DNN_BACKEND = 5 # cv.dnn: DNN_BACKEND_CUDA = 5; DNN_BACKEND_OPENCV = 3\nPERIPHERY_DNN_TARGET = 6 # cv.dnn: DNN_TARGET_CUDA = 6; DNN_TARGET_CUDA_FP16 = 7; DNN_TARGET_CPU = 0\nPERIPHERY_WRAPPER = 1 # 1 = darknet, 2 = opencv from darknet\nPERIPHERY_DATA_FILE = \"yolo/Y0016.data\" \n\n# ======================================================================================================================\n# YOLO PRECISE NETWORK SETTINGS\n# ======================================================================================================================\nPRECISE_CONFIDENCE_THRESHOLD = 0.1 # Confidence threshold\nPRECISE_HIER_THRESHOLD = 0.5 # works only in darknet wrapper\nPRECISE_NMS_THRESHOLD = 0.4 # Non-maximum suppression threshold\nPRECISE_INPUT_SIZE = (832, 832)\nPRECISE_CONFIG_FILE = \"yolo/Y0016_832.cfg\"\nPRECISE_WEIGHTS_FILE = \"yolo/Y0016.weights\"\nPRECISE_CLASSES_FILE = \"yolo/Y0016.names\"\nPRECISE_DATA_FILE = \"yolo/Y0016.data\" \nPRECISE_DNN_BACKEND = 5 # cv.dnn: DNN_BACKEND_CUDA = 5; DNN_BACKEND_OPENCV = 3\nPRECISE_DNN_TARGET = 6 # cv.dnn: DNN_TARGET_CUDA = 6; DNN_TARGET_CUDA_FP16 = 7; DNN_TARGET_CPU = 0\nPRECISE_WRAPPER = 1 # 1 = darknet, 2 = opencv from darknet\n\n\n# ======================================================================================================================\n# CAMERA SETTINGS\n# ======================================================================================================================\nCAMERA_W = 3280\nCAMERA_H = 2464\nAPPLY_IMAGE_CROPPING = True\nCROP_W_FROM = 498 \nCROP_W_TO = 2498 \nCROP_H_FROM = 160 \nCROP_H_TO = 1660 \nCAMERA_FRAMERATE = 8\nCAMERA_FLIP_METHOD = 0\nSCENE_CENTER_X = 1000 # 1576 for uncropped\nSCENE_CENTER_Y = 980 # 1104 for uncropped\nONE_MM_IN_PX = 5.2\nISP_DIGITAL_GAIN_RANGE_FROM = 4\nISP_DIGITAL_GAIN_RANGE_TO = 4\nGAIN_RANGE_FROM = 4\nGAIN_RANGE_TO = 4\nEXPOSURE_TIME_RANGE_FROM = 660000\nEXPOSURE_TIME_RANGE_TO = 660000\nAE_LOCK = True\nCV_APPLY_ROTATION = False\nCV_ROTATE_CODE = 2\nAPPLY_THREAD_BUFF_CLEANING = True\nBUFF_CLEANING_DELAY = 0 # seconds of waiting before frame reading; should be positive or zero; set to 0 if thread cleaning is used\n#WORKING_ZONE_POLY_POINTS = [[1000, 1145], [230, 1080], [245, 755], [340, 400], [640, 315], [1000, 270], [1360, 315], [1660, 400], [1755, 755], [1770, 1080]]\nUNDISTORTED_ZONE_RADIUS = 300\nDELAY_BEFORE_2ND_SCAN = 0.3 # delay in seconds after robot stop and before second scan (M=1)\nVIEW_ZONE_POLY_POINTS = [[387, 618], [439, 510], [556, 433], [670, 375], [808, 319], [982, 285], [1143, 279], [1293, 294], [1501, 339], [1635, 395], [1766, 473], [1816, 550], [1867, 637], [1881, 675], [1919, 795], [1942, 926], [1959, 1066], [1964, 1217], [1957, 1321], [1949, 1393], [1874, 1425], [1802, 1457], [1692, 1498], [1555, 1537], [1410, 1567], [1219, 1589], [1081, 1590], [944, 1590], [804, 1575], [679, 1552], [569, 1525], [423, 1475], [330, 1431], [277, 1399], [273, 1289], [279, 1131], [297, 976], [343, 780]]\n#WORKING_ZONE_POLY_POINTS = [[1000, 1145], [230, 1080], [245, 755], [340, 400], [640, 315], [1000, 270], [1360, 315], [1660, 400], [1755, 755], [1770, 1080]] \nWORKING_ZONE_POLY_POINTS = [[1000, 1050], [30, 1080], [45, 755], [40, 300], [640, 115], [1000, 70], [1360, 115], [1860, 300], [1955, 755], [1970, 1080]]\n# ======================================================================================================================\n# APP SETTINGS\n# ======================================================================================================================\nSAVE_DEBUG_IMAGES = False\nDEBUG_IMAGES_PATH = \"debug_images/\"\nALLOW_GATHERING = False\n\nROBOT_SN = \"SN003\" \nUI_LANGUAGE = \"en\"\nSLIDER_CREATE_FIELD_MIN = 15\nSLIDER_CREATE_FIELD_MAX = 150\nSLIDER_CREATE_FIELD_DEFAULT_VALUE = 25\nSLIDER_CREATE_FIELD_STEP = 1\n\nFRAME_SHOW = True\nSHARED_MEMORY_NAME_DETECTED_FRAME = \"/detected_frame\"\n\nAUDIT_MODE = False\nAUDIT_DIVIDER = 6\nAUDIT_OUTPUT_FILE = \"audit.txt\"\n\nSLOW_FAST_MODE = False\nSLOW_MODE_MIN_TIME = 3 # seconds\n\nVERBOSE = False\nLOG_ROOT_DIR = \"logs/\"\nSTATISTICS_OUTPUT_FILE = \"statistics.txt\"\nDATACOLLECTOR_SAVE_FILE = \"datacollection_save.dat\"\nDATA_GATHERING_DIR = \"gathered_data/\"\nLAST_ANGLE_WHEELS_FILE = \"last_angle_wheels.txt\"\nFILES_TO_KEEP_COUNT = 600\n\nQUEUE_NAME_UI_MAIN = \"/queue_ui_main\"\nQUEUE_NAME_UI_NOTIFICATION = \"/queue_ui_notification\"\n\nCONTINUOUS_INFORMATION_SENDING = True\nALIVE_SENDING_TIMEOUT = 1\n\n# ======================================================================================================================\n# DETECTION MANAGER SETTINGS\n# ======================================================================================================================\n\nCAMERA_POSITIONS = [(220, 0)] # smoothie global coordinates to take photos from for forming plants list. Format is (x, y)\nPDZ_DISTANCES = [{\"top\": 1000, \"bot\": 1000, \"left\": 1000, \"right\": 1000}] # precice detection zone sizes. At each camera position scan only plants inside this zone is added to extraction list\n#CAMERA_POSITIONS = [(150, 0), (300,0)] # smoothie global coordinates to take photos from for forming plants list. Format is (x, y)\n#PDZ_DISTANCES = [{\"top\": 750, \"bot\": 750, \"left\": 1000, \"right\": 350}, {\"top\": 750, \"bot\": 750, \"left\": 450, \"right\": 1000}] # precice detection zone sizes. At each camera position scan only plants inside this zone is added to extraction list\n# values are px count from scene center to. Format is {\"top\": int, \"bot\": int, \"left\": int, \"right\": int}\n\n\n# ======================================================================================================================\n# EXTRACTION MANAGER SETTINGS\n# ======================================================================================================================\n\nEXTRACTION_PATTERNS_OFFSET_MM = 20\nEXTRACTION_MAP_CELL_SIZE_MM = 10\nEXTRACTION_TRIES_PER_PLANT = 3 # defines how many times robot will try to extract plant or plants group in undist. zone\n# should be >= 1; expected value that equal to extraction strategies count so each of them can be applied\n\nAVOID_CORK_VIEW_OBSCURING_DIST_X = 10 # mm; offset size to \"remove\" corkscrew tube from camera-plant view\n# mm; offset size to \"remove\" corkscrew tube from camera-plant view, should be negative to move cork down and free view\nAVOID_CORK_VIEW_OBSCURING_DIST_Y = -10\n\nALLOW_DELTA_SEEKING = True # will try to move over supposed plant position if plant wasn't detected during approaching\n# False: seek over coordinates updated by cork obscuring patch; True: seek over original coordinates\nDELTA_SEEKING_IGNORE_OBSCURING = False\n\nDEBUG_MATRIX_FILE = False\nFILTER_EXTRACTED_PLANTS = True\nFILTER_EXT_PLANTS_TRIGGER_DIST = 25 # px; trigger distance for previous option key\n\n# ======================================================================================================================\n# PREDICTION SETTINGS\n# ======================================================================================================================\nZONE_THRESHOLD_DEGREE = [(436,5),(697,7),(796,17),(849,15),(953,6)]\n\n# ======================================================================================================================\n# NTRIP CLIENT SETTINGS\n# ======================================================================================================================\nNTRIP = False \nNTRIP_RESTART_TIMEOUT = 60\nNTRIP_USER = \"centipede\"\nNTRIP_PASSWORD = \"centipede\"\nNTRIP_CASTER = \"caster.centipede.fr\"\nNTRIP_PORT = 2101\nNTRIP_MOUNTPOINT = \"LIENSS\"\n\nNTRIP_OUTPUT_PORT = \"/dev/ttyACM1\"\nNTRIP_OUTPUT_BAUDRATE = 38400\n\nLEARN_GO_STRAIGHT = True\nLEARN_GO_STRAIGHT_UI = False\nMIN_PERPENDICULAR_GO_STRAIGHT = 100 # in mm\nVALUES_LEARN_GO_STRAIGHT = 40\nLEARN_GO_STRAIGHT_FILE = \"learn_go_straight.txt\"","sub_path":"config/config_v17_defaults.py","file_name":"config_v17_defaults.py","file_ext":"py","file_size_in_byte":19787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"318982104","text":"# Labs/Exceptions/DateBook.py\n#\n\nimport datetime\nimport time\n\nclass DateBook:\n\n def __init__(self, monthDate, events):\n self.monthDate = monthDate\n self.events = events\n self.__viewStartDate = self.__getFirstViewDate()\n self.__viewEndDate = self.__getLastViewDate()\n\n @property\n def events(self):\n return self.__events\n\n @events.setter\n def events(self, value):\n self.__events = value\n\n @events.deleter\n def events(self):\n del self.__events\n\n @property\n def monthDate(self):\n return self.__monthDate\n\n @monthDate.setter\n def monthDate(self, value):\n self.__monthDate = value\n\n @monthDate.deleter\n def monthDate(self):\n del self.__monthDate\n\n def __getFirstViewDate(self):\n\n # Determine the first date to display. Get the first day of the month, and then move backward\n # to the Sunday.\n\n monthStartDate = datetime.date(self.monthDate.year, self.monthDate.month, 1)\n return monthStartDate - datetime.timedelta(monthStartDate.isoweekday() % 7)\n\n def __getLastViewDate(self):\n\n # Find the last day of the month to display by getting the first day of the next month and backing up one day.\n\n month = self.monthDate.month % 12 + 1;\n if (month == 1):\n year = self.monthDate.year + 1\n else:\n year = self.monthDate.year\n\n monthEndDate = datetime.date(year, month, 1) - datetime.timedelta(1)\n\n # Get the last date to display.\n\n return monthEndDate + datetime.timedelta(6 - (monthEndDate.isoweekday() % 7))\n\n def renderCalendarView(self):\n\n # Add all of the days in the view to a list.\n\n currentDate = self.__viewStartDate\n today = datetime.date.today()\n oneDay = datetime.timedelta(1)\n\n print(\"\\033[0;37m\")\n\n while (currentDate <= self.__viewEndDate):\n for d in range(0, 7):\n if currentDate.month == self.monthDate.month and currentDate.day == 1:\n print(\"\\033[0m\", end=\"\")\n if currentDate == today:\n print(\"\\033[1m\", end=\"\")\n print(\"{:>2d}\".format(currentDate.day), end=\"\")\n if currentDate == today:\n print(\"\\033[0m\", end=\"\")\n if self.events.get(currentDate) != None and self.events.get(currentDate).date == currentDate:\n print(\"* \", end=\"\")\n else:\n print(\" \", end=\"\")\n currentDate = currentDate + oneDay\n if currentDate.month != self.monthDate.month and currentDate.day == 1:\n print(\"\\033[0;37m\", end=\"\")\n print(\"\")\n print(\"\\033[0m\")\n","sub_path":"Student_Files/Python/Labs/Exceptions/DateBook.py","file_name":"DateBook.py","file_ext":"py","file_size_in_byte":2749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"404406818","text":"import Node\r\ndef selectionSort(list):\r\n '''selection sort algorithm\r\n\r\n selection sort algorithm for linked list with swaping pointer values\r\n '''\r\n currentpre=list.headNode() #node before current node\r\n current=currentpre.getNext() #current pionter of linked list\r\n\r\n #outer loop for selection sort\r\n while current!=None:\r\n minimum=current #selecting first node as minimum values node\r\n nextprev=current; #previous node for nextinner pointer\r\n beforesort=nextprev #previous not for the pointer with minimum value\r\n nextinner=current.getNext() #node for next inner loop of selection sort\r\n\r\n #inner loop of selection sort\r\n while nextinner!=None:\r\n\r\n if minimum.getData()>nextinner.getData(): #finding minimum value pointer\r\n beforesort=nextprev #setting values of pointer to minimum value pointer\r\n minimum=nextinner\r\n nextprev=nextinner\r\n nextinner=nextinner.getNext()\r\n\r\n\r\n #after the inner loop completion of cycle we will have all values in correnponding pointer\r\n #there are there condition of minimum value pointer\r\n #(i) current pointer is minimum value pointer in this case will do no swap\r\n #(ii) pointer pointed to minimum value pointer in this case if will be executed\r\n #(iii) pointer is some where else in the linked list in this case elif will be executed\r\n\r\n #case ii\r\n #pionter are next to each other in this we did not beforesort pointer because that is current pointer\r\n # and we did not next next pointer of current node here is code\r\n if current.getNext()==minimum :\r\n minimumNext=minimum.getNext() #pointer to next node pointed my minimum node\r\n currentpre.setNext(minimum) #nex pointer of pointer before the current node is to minimum node\r\n minimum.setNext(current) #set minimum next pointer of minimum node to current\r\n current.setNext(minimumNext) #set next pointer of current to next pointer pointed my my minimum pointer\r\n current=minimum #set current to minimum\r\n\r\n #case iii\r\n #in this case we need all four pointer we have created for swapping\r\n #i.e i) pre-pointer of current node ii)nex pointer of current node\r\n # iii)pre-pointer of sorted node iv) pointer pointed my sorted node\r\n #here is code for swapping\r\n elif current.getNext()!=minimum and current!=minimum:\r\n currentNext=current.getNext() #pointer pointed my the current node\r\n minimumNext=minimum.getNext() #pointer to next node pointed my minimum node\r\n currentpre.setNext(minimum) #set pre-current to minimum pointer\r\n minimum.setNext(currentNext) #set next pointer of minimum node to currentNext pointer\r\n beforesort.setNext(current) #setting pre pointer of sorted node to current pointer\r\n current.setNext(minimumNext) #current pointer next pointer set to next of minimumNext\r\n current=minimum #set current to minimum\r\n currentpre=current #set pre-current to current\r\n current=current.getNext() #set current to current ->next\r\n list.show()","sub_path":"AlgorithmsInPython/sorting algorithm with linked list/linkedSelectionSort.py","file_name":"linkedSelectionSort.py","file_ext":"py","file_size_in_byte":3466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"262305929","text":"from futuquant import *\nimport pandas as pd\n#import TA-Lib\nimport talib\nimport sys\nfrom pymongo import MongoClient\nimport datetime\nimport math\nimport time\nimport re\nfrom datetime import date,timedelta\n# -*- coding: utf-8 -*-\n\nd_value_250_up=8\nd_value_250_down=-25 #250均线<-25\nd_value_1000=-10 #1000日均线<10\n\nyear_time=18*12*30*24*60*60\nmonth_time=30*24*60*60\nstock_time_close_250 = dict()\nstock_time_close_1000 = dict()\nstock_month_tick_count_250 = dict()\nstock_month_tick_count_1000 = dict()\n\nyear_stock_time_close_250 = dict()\nyear_stock_time_close_1000 = dict()\nyear_stock_tick_count_250 = dict()\nyear_stock_tick_count_1000 = dict()\n\n\nnew_year_price_ma250_rate = dict()\nnew_year_price_ma1000_rate = dict()\n\nnew_price_and_ma250_price = dict()\nnew_price_and_ma1000_price = dict()\n\ndef addzero(n): \n ''''' \n add 0 before 0-9 \n return 01-09 \n ''' \n nabs = abs(int(n)) \n if(nabs<10): \n return \"0\"+str(nabs) \n else: \n return nabs \n\ndef getyearandmonth(year,mon,n=0):\n global year_index\n #print(\"n:\",n)\n #now = datetime.now() \n #nextDay = now + timedelta(days = n*30)\n thisyear = int(year) \n thismon = int(mon) \n totalmon = thismon+n \n j = totalmon%12\n if(j==0):\n j=12\n year_index = year_index -1\n #print(\"year_index:\",year_index,\" j:\",j,\" n:\",n)\n j = addzero(j)\n #print(\"thisyear:\",thisyear,\" j:\",j)\n return (str(year_index),str(j))\n\ndef getseason(year,month,season=0):\n season_list = []\n #print \"before month:\",month\n if month%3 != 0:\n month = month - month%3\n print(\"after month:\",month)\n for cur in range(3*(season),3*(season+1)):\n cur_year,cur_month = getyearandmonth(year,month,cur+1)\n season_list.append(str(cur_year)+\"-\"+str(cur_month ))\n return season_list\n\ndef getyear(year,n_year=0):\n return str(year+n_year)\n\n\ndef print_guila_month_find_lowest_price_of_each_stock(stock_close_div,stock_close_div_begin_end,type):\n print(\"print_month_find_lowest_price_of_each_stock enter type:\",type)\n #for key,value in month_stock_close_div_begin_end.items():\n # print(\"month_stock_close_div_begin_end value:\",value,\" key:\",key)\n head = \"\"\n if type == 1:\n head = \"month_tick\"\n elif type == 2:\n head = \"year_tick\"\n\n for key,value in stock_close_div.items(): \n #print(\"print_month_find_lowest_price_of_each_stock value:\",value,\" key:\",key)\n sorted_dict = sorted(value.items(), key=lambda value:value[1])\n #print(\"print_month_find_lowest_price_of_each_stock sorted_dict:\",sorted_dict)\n print(\" ---------------------------------------------------------------time:\",key)\n current_div_count_best= 0\n current_div_count_better = 0\n current_div_count_worst = 0\n current_div_count_impossble = 0\n for key_name_rate_dict in sorted_dict:\n name = key_name_rate_dict[0]\n rate_info = key_name_rate_dict[1]\n if int(rate_info) > 7:\n current_div_count_best = current_div_count_best +1\n elif int(rate_info) >0 and int(rate_info) <7:\n current_div_count_better = current_div_count_better +1\n elif int(rate_info) <0 and int(rate_info) >-8:\n current_div_count_worst = current_div_count_worst +1\n elif int(rate_info) <= -8:\n current_div_count_impossble = current_div_count_impossble +1\n price = stock_close_div_begin_end[key][name]\n print(\"\",head,\" distribute current_div_count_best [7,~]:\",current_div_count_best,\" time:\",key)\n print(\"\",head,\" distribute current_div_count_better[0,7]:\",current_div_count_better,\" time:\",key)\n print(\"\",head,\" distribute current_div_count_worst:[-7,0]\",current_div_count_worst,\" time:\",key)\n print(\"\",head,\" distribute current_div_count_impossble[~,-7]:\",current_div_count_impossble,\" time:\",key)\n print(\"\",head,\" distribute-----------------------------\")\n\ndef print_month_find_lowest_price_of_each_stock(stock_close_div,stock_close_div_begin_end,type):\n print(\"print_month_find_lowest_price_of_each_stock enter type:\",type)\n #for key,value in month_stock_close_div_begin_end.items():\n # print(\"month_stock_close_div_begin_end value:\",value,\" key:\",key)\n head = \"\"\n if type == 1:\n head = \"month_tick\"\n elif type == 2:\n head = \"year_tick\"\n\n for key,value in stock_close_div.items(): \n #print(\"print_month_find_lowest_price_of_each_stock value:\",value,\" key:\",key)\n sorted_dict = sorted(value.items(), key=lambda value:value[1])\n #print(\"print_month_find_lowest_price_of_each_stock sorted_dict:\",sorted_dict)\n print(\" ------------------------------------------------------------------------------------------------------time:\",key)\n current_div_count_best= 0\n current_div_count_better = 0\n current_div_count_worst = 0\n current_div_count_impossble = 0\n count_num=0\n for key_name_rate_dict in sorted_dict:\n name = key_name_rate_dict[0]\n rate_info = key_name_rate_dict[1]\n rate_info = round(rate_info,2)\n if int(rate_info) > 7:\n current_div_count_best = current_div_count_best +1\n elif int(rate_info) >0 and int(rate_info) <7:\n current_div_count_better = current_div_count_better +1\n elif int(rate_info) <0 and int(rate_info) >-8:\n current_div_count_worst = current_div_count_worst +1\n elif int(rate_info) <= -8:\n current_div_count_impossble = current_div_count_impossble +1\n price = stock_close_div_begin_end[key][name]\n count_num = count_num +1\n print(\"\",head,\" tick_time:\",key,\" count:\",count_num,\" rate[begin-end]:\",rate_info,\" stock_name:\",name,\" price\",price)\n\nmonth_stock_close_div = dict()\nmonth_stock_close_div_begin_end = dict()\nyear_stock_close_div = dict()\nyear_stock_close_div_begin_end = dict()\n#month_stock_ranged_close_div = dict()\ndef month_find_lowest_price_of_each_stock(conn,code_stock_id,code_stock_name,start_time,end_time,cur_index,tick_time,stock_close_div,stock_close_div_begin_end,type):\n nowTime=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n head = \"code\"\n #print(\"cur_index:\",cur_index,\"---head:\",head,\" stock_info_each:\",stock_info_each,\" nowTime:\",nowTime,\" tick_time:\",tick_time)\n #code_stock_id = stock_info_each['code']#\"HK.00700\"\n #code_id = 'HK.00700'\n code_stock_id = code_stock_id.replace(\".\", \"\")\n #print(\"code_stock_id:\",code_stock_id)\n\n db = conn.stock_list\n time_value = re.compile(tick_time) #month\n #time_value = {\"$in\": [re.compile(\"2018-06\"), re.compile(\"2018-05\")]} #season\n #time_value = re.compile('2018') #year #{'$regex':tick_time}\n month_count = db[code_stock_id].find({\"time_key\":time_value},{\"time_key\":1,\"close\":1}).count()\n #print(\"month_count:\",month_count)\n if int(month_count) > 0 :\n stock_each = db[code_stock_id].find({\"time_key\":time_value},{\"time_key\":1,\"close\":1}).sort(\"time_key\")\n stock_each_list = list(stock_each[:])\n month_begin = stock_each_list[0][\"close\"]\n month_begin = round(month_begin,2)\n month_end = stock_each_list[month_count-1][\"close\"]\n month_end = round(month_end,2)\n month_div = 0\n if month_begin == month_begin and month_begin != 0:\n month_div = (month_end-month_begin)*100/month_begin\n #print(\"month_begin:\",month_begin,\" month_end:\",month_end,\" cacl div:\",month_div,\" code_stock_name:\",code_stock_name,\" time_value:\",time_value )\n #print(\"---------stock_each_list:\",stock_each_list)\n stock_each_ranged = db[code_stock_id].find({\"time_key\":time_value},{\"time_key\":1,\"close\":1}).sort(\"close\")\n stock_each_ranged_list = list(stock_each_ranged[:])\n month_begin_ranged = stock_each_ranged_list[0][\"close\"]\n month_end_ranged = stock_each_ranged_list[month_count-1][\"close\"]\n month_div_ranged =(month_end_ranged-month_begin_ranged)*100/month_begin_ranged\n #print(\"month_begin_ranged:\",month_begin_ranged,\" month_end_ranged:\",month_end_ranged,\" month_div_ranged:\", month_div_ranged)\n #print(\"---------stock_each_ranged_list:\",stock_each_ranged_list)\n #stock_name = code_stock_name#stock_info_each[\"stock_name\"]\n\n if tick_time in stock_close_div:\n if code_stock_name in stock_close_div[tick_time]:\n stock_close_div[tick_time].update({code_stock_name: month_div})\n else:\n stock_close_div[tick_time][code_stock_name] = month_div\n else:\n stock_close_div.update({tick_time:{code_stock_name: month_div}})\n\n start_price_end_price = \"month_start:\"+str(month_begin)+\" month_end:\"+str(month_end)\n if type==2:\n start_price_end_price = \"year_start:\"+str(month_begin)+\" year_end:\"+str(month_end)\n\n if tick_time in stock_close_div_begin_end:\n if code_stock_name in stock_close_div_begin_end[tick_time]:\n stock_close_div_begin_end[tick_time].update({code_stock_name: start_price_end_price})\n else:\n stock_close_div_begin_end[tick_time][code_stock_name] = start_price_end_price\n else:\n stock_close_div_begin_end.update({tick_time:{code_stock_name: start_price_end_price}})\n ##month_stock_ranged_close_div.update({stock_name:{tick_time: month_div_ranged}})\n '''\n else :\n if type ==1:\n print(\"not exist month_count:\",month_count,\" code:\",code_stock_id,\" tick_time:\",tick_time)\n elif type ==2:\n print(\"not exist year_count:\",month_count,\" code:\",code_stock_id,\" tick_time:\",tick_time)\n '''\n\n#db.stock_list.createIndex({\"time_key\":-1,\"close\":-1})\n\n\nif __name__ == '__main__':\n global year_index\n start_time= '1998-01-01'\n #end_time='2018-06-12'\n year_tick = 15\n month_tick = 12*year_tick\n if len(sys.argv) != 2:\n print(\"exit argv:\",sys.argv)\n sys.exit()\n if len(sys.argv) == 2:\n year = datetime.datetime.now().year\n month = datetime.datetime.now().month\n day = datetime.datetime.now().day\n end_time= str(year)+'-'+str(month)+'-'+str(day)\n start_time = str(year-year_tick)+'-'+str(month)+'-'+str(day)\n print(\"start_time:\",start_time,\" end_time:\",end_time)\n #quote_ctx = OpenQuoteContext(host='127.0.0.1', port=11111)\n #get stock mongdb\n conn = MongoClient('127.0.0.1', 27017)\n db = conn.market\n db_zhishu_table_name = sys.argv[1]\n print(\"######db_zhishu_table_name:\",db_zhishu_table_name)\n stock_info_list = db[db_zhishu_table_name].find()\n #print(\"stock_info_list:\",stock_info_list)\n #-----------------month\n stock_list_data_pd = pd.DataFrame(list(stock_info_list))\n cur_index = 1\n for index,stock_info_each in stock_list_data_pd.iterrows():\n stock_name = stock_info_each[\"stock_name\"]\n code_stock_id = stock_info_each[\"code\"]\n print(\"***************************************************************cur_index:\",cur_index)\n print(\"stock_name:\",stock_name,\" code_stock_id:\",code_stock_id)\n #print (\"--month-----------month_tick:\",month_tick)\n year_index = year\n for cur_tick_time in range(0,month_tick):\n cur_year,cur_month = getyearandmonth(year,month,-cur_tick_time)\n tick_time = cur_year+\"-\"+cur_month\n #print(\"===========tick_time:\",tick_time)\n month_find_lowest_price_of_each_stock(conn,code_stock_id,stock_name,start_time,end_time,cur_index,tick_time,month_stock_close_div,month_stock_close_div_begin_end,1)\n #print (\"--year-----------year_tick:\",year_tick)\n year_index = year\n for cur_tick_time in range(0,year_tick):\n cur_year = getyear(year,-cur_tick_time)\n #year_index = year_index +1\n month_find_lowest_price_of_each_stock(conn,code_stock_id,stock_name,start_time,end_time,cur_index,cur_year,year_stock_close_div,year_stock_close_div_begin_end,2)\n cur_index = cur_index + 1\n\n #最近几年的没有年初到年末涨跌幅\n print_guila_month_find_lowest_price_of_each_stock(year_stock_close_div,year_stock_close_div_begin_end,2)\n print_guila_month_find_lowest_price_of_each_stock(month_stock_close_div,month_stock_close_div_begin_end,1)\n\n #最近几月的没有月初到月末涨跌幅\n print_month_find_lowest_price_of_each_stock(year_stock_close_div,year_stock_close_div_begin_end,2)\n print_month_find_lowest_price_of_each_stock(month_stock_close_div,month_stock_close_div_begin_end,1)\n\n\n print(\"-------cacl_end------\\n\")\n #quote_ctx.close()\n sys.exit()\n\n","sub_path":"src/year_spread/source/year_drop_range_spread.py","file_name":"year_drop_range_spread.py","file_ext":"py","file_size_in_byte":13256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"326633153","text":"import urllib.request,urllib.parse\nfrom bs4 import BeautifulSoup\n\nclass Header:\n mHeader = {\n \"User-Agent\" : \"Mozilla/5.0 (Windows NT 10.0; Win64; x64)\"\n }\n\nclass naver(Header):\n #sample https://sarch.naver.com/search.naver?&where=news&ie=utf8&query=%EB%B9%84%EB%A6%AC\n mUrl = \"http://search.naver.com/search.naver?\"\n mConn = None\n def httpConnet(self,word):\n params = {\n #\"sm\":\"tab_hty.top\",t\n \"where\":\"news\",\n \"ie\": \"utf8\",\n \"query\": word\n }\n params = urllib.parse.urlencode(params)\n try:\n Request = urllib.request.urlopen(self.mUrl+params)\n except:\n print(\"programe error\")\n exit(-1)\n return Request.read().decode(\"utf-8\")\n \n\n def getContext(self,str):\n soup = BeautifulSoup(str,\"html.parser\")\n dir = []\n for li in soup.find('ul', {'class':'type01'}).findAll('li'):\n link = li.find('dt')\n if link == None:\n continue\n link = link.find(\"a\")\n \n dir.append([link.get('href'),link.get(\"title\").replace(',',\" \")])\n return dir\n \n \n def getdata(self,word):\n data = self.httpConnet(word)\n dir = self.getContext(data)\n with open(word+\".csv\",'w') as f:\n for dir in dir:\n f.write(dir[0]+','+dir[1]+'\\n')\n\n \nclass News:\n object =None\n def __init__(self):\n pass\n\n def run(self):\n try:\n word = input(\"input : \")\n object = naver()\n object.getdata(word)\n except:\n print(\"rinechran@gmail.com\")\n exit(0)\n\n\ndef main():\n news = News()\n news.run()\n pass\n\nif __name__==\"__main__\":\n main()","sub_path":"naver_news.py","file_name":"naver_news.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"194904146","text":"from django.shortcuts import render, redirect\nfrom django.conf import settings\n\nfrom downtime.models import Period\n\ntry:\n from django.utils.deprecation import MiddlewareMixin\nexcept ImportError:\n MiddlewareMixin = object\n\n\nclass DowntimeMiddleware(MiddlewareMixin):\n def process_request(self, request):\n exempt_exact_urls = getattr(settings,\n 'DOWNTIME_EXEMPT_EXACT_URLS', None)\n if exempt_exact_urls:\n for url in exempt_exact_urls:\n if request.path == url:\n return None\n\n exempt_paths = getattr(settings, 'DOWNTIME_EXEMPT_PATHS', ('/admin',))\n for path in exempt_paths:\n if request.path.startswith(path):\n return None\n\n objects = Period.objects.is_down()\n\n if objects.count():\n # we are down.\n url_redirect = getattr(settings, 'DOWNTIME_URL_REDIRECT', None)\n if url_redirect:\n return redirect(url_redirect)\n else:\n return render(request, \"downtime/downtime.html\", status=503)\n","sub_path":"downtime/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57021015","text":"\"\"\"FIXME: INCOMPLETE MODULE FOR MINECRAFT UTILITY REWRITE; DO NOT USE YET\n\nERROR CODES:\n 0: Cmnd success: User added or approved, or request sent\n-1: Benign error: Duplicate request which has been approved\n-2: Benign error: Duplicate request or approval\n-3:\n-4:\n-5:\n-6:\n-7: Malign error: Failed to access dbName or WhitelistFile\n-8: Malign error: User supplied invalid name\n-9: Malign error: Incomplete function (fault of developer)\n\"\"\"\n\nfrom collections import OrderedDict\nimport json\nfrom pathlib import Path\nfrom typing import Any, Dict, List, NewType, Type, Union\nfrom uuid import UUID\n\nimport requests\n\nfrom .embeds import minecraft_card, minecraft_suspension\nfrom ..exceptions import WhitelistError\nfrom ..grasslands import Peacock\n\nlog = Peacock()\n\n\ntype_entry_db: Type = NewType(\"DB Entry\", Dict[str, Union[bool, int, List[str], str]])\ntype_db: Type = NewType(\"Database\", List[type_entry_db])\n\n\n# The default profile for a new player being added to the database\n# (Do not use this for other stuff)\nPLAYERDEFAULT: type_entry_db = OrderedDict(\n [\n (\"name\", \"PLAYERNAME\"),\n (\"uuid\", \"00000000-0000-0000-0000-000000000000\"),\n (\"altname\", []),\n (\"discord\", 000000000000000000),\n (\"approved\", []),\n (\"submitted\", \"1970-01-01_00:00\"),\n (\"suspended\", 000),\n (\"operator\", 0),\n (\"notes\", []),\n ]\n)\n\n\ndef break_uid(uuid: str) -> str:\n \"\"\"Given an undashed UUID, break it into five fields.\"\"\"\n f = [hex(c)[2:] for c in UUID(uuid).fields]\n return \"-\".join(f[:3] + [f[3] + f[4], f[5]])\n\n\n# TODO: Implement\n# if ( # Yield each Entry if looking for something specific.\n# (pending is None or pending is bool(entry.get(\"approved\", [])))\n# and (\n# suspended is None\n# or (\n# suspended is True\n# and entry.get(\"suspended\", 0) not in (0, False, None)\n# )\n# or suspended is entry.get(\"suspended\", False)\n# )\n# ) or\n\n\ndef find(data: type_db, *params: str) -> List[type_entry_db]:\n out = []\n for entry in data:\n for p in params:\n if ( # Yield each Entry if any parameter...\n # ...Matches the Discord UUID.\n p == str(entry.get(\"discord\"))\n # ...Matches the Mojang UUID.\n or p.lower().replace(\"-\", \"\") == entry[\"uuid\"].lower().replace(\"-\", \"\")\n # ...Can be found in the Username History.\n or p.lower() in map(str.lower, entry[\"altname\"])\n ):\n out.append(entry)\n break\n return out\n\n\ndef id_from_name(uname_raw: str):\n \"\"\"Given a Minecraft Username, get its UUID.\"\"\"\n uname_low: str = uname_raw.lower()\n response = requests.get(\n \"https://api.mojang.com/users/profiles/minecraft/{}\".format(uname_low)\n )\n log.f(\"WLME_RESP\", str(response))\n if response.status_code == 200:\n return {\"code\": response.status_code, \"udat\": response.json()}\n else:\n return {\"code\": response.status_code}\n\n\nclass Interface:\n def __init__(self, client):\n self.client = client\n self.config = client.config\n self.ctx = None\n\n def __enter__(self) -> type_db:\n if self.ctx is not None:\n raise RuntimeError(\"Attempting to lock locked Whitelist.\")\n else:\n data: type_db = self.db_read()\n self.ctx = data\n return data\n\n def __exit__(self, exc_type, exc_value, traceback):\n if self.ctx is None:\n raise RuntimeError(\"Attempting to unlock unlocked Whitelist.\")\n else:\n self.db_write(self.ctx)\n self.ctx = None\n\n def cget(self, prop: str, default: Any = None) -> Any:\n \"\"\"Retrieve a value from the Configuration. Merely a shorthand.\"\"\"\n v = self.config.get(prop, default)\n return v\n\n @property\n def path_db(self) -> Path:\n return Path(self.cget(\"minecraftDB\", \"playerdb.json\"))\n\n @property\n def path_wl(self) -> Path:\n return Path(self.cget(\"minecraftWL\", \"whitelist.json\"))\n\n @property\n def path_op(self) -> Path:\n return Path(self.cget(\"minecraftOP\", \"ops.json\"))\n\n def db_read(self) -> type_db:\n try:\n with self.path_db.open(\"r\") as file_db:\n data: type_db = json.load(file_db, object_pairs_hook=OrderedDict)\n\n except OSError as e:\n # File does not exist: Pointless to continue.\n log.err(\"OSError on DB read: \" + str(e))\n raise WhitelistError(\"Cannot read PlayerDB file.\") from e\n\n return data\n\n def db_write(self, data):\n try:\n with self.path_db.open(\"w\") as file_db:\n # Save all the things.\n json.dump(data, file_db, indent=2)\n\n except OSError as e:\n # Cannot write file: Well this was all rather pointless.\n log.err(\"OSError on DB save: \" + str(e))\n raise WhitelistError(\"Cannot write PlayerDB file.\") from e\n\n def whitelist_rebuild(self, refreshall=False, refreshnet=False) -> int:\n \"\"\"Export the local database into the whitelist file itself. If Mojang\n ever changes the format of the server whitelist file, this is the\n function that will need to be updated.\n \"\"\"\n try:\n # Stage 0: Load the full database as ordered dicts, and the\n # whitelist as dicts.\n with self.path_db.open(\"r\") as file_db, self.path_wl.open(\"r\") as file_wl:\n data = json.load(file_db, object_pairs_hook=OrderedDict)\n if self.cget(\"minecraftStrictWL\", False):\n data_wl = []\n else:\n data_wl = json.load(file_wl)\n except OSError:\n # File does not exist: Pointless to continue.\n return 0\n data_op = [] # Op list is always strict.\n\n if refreshall:\n # Rebuild Index\n data_db = []\n # Stage 1: Make new DB.\n for applicant in data:\n # Stage 2: Find entries in old DB, import their stuff.\n entry_new = PLAYERDEFAULT.copy()\n entry_new.update(applicant)\n\n if refreshnet:\n # Stage 3, optional: Rebuild username history.\n name_history = requests.get(\n \"https://api.mojang.com/user/profiles/{}/names\".format(\n applicant[\"uuid\"].replace(\"-\", \"\")\n )\n )\n\n if name_history.status_code == 200:\n # Spy on their dark and shadowy past.\n entry_new.update(altname=[])\n for name in name_history.json():\n entry_new[\"altname\"].append(name[\"name\"])\n # Ensure the name is up to date.\n entry_new[\"name\"] = name[\"name\"]\n\n data_db.append(entry_new)\n with self.path_db.open(\"w\") as file_db:\n json.dump(data_db, file_db, indent=2)\n data = data_db\n\n for applicant in data:\n # Check everyone who has applied.\n app = next(\n (item for item in data_wl if item[\"uuid\"] == applicant[\"uuid\"]), False\n )\n # Is the applicant already whitelisted?\n if (\n app == False\n and len(applicant[\"approved\"]) > 0\n and not applicant[\"suspended\"]\n ):\n # Applicant is not whitelisted AND is approved AND is not\n # suspended, add them.\n data_wl.append({\"uuid\": applicant[\"uuid\"], \"name\": applicant[\"name\"]})\n elif app != False and applicant[\"suspended\"] and app in data_wl:\n # BadPersonAlert, remove them.\n data_wl.remove(app)\n\n # Is the applicant supposed to be an op?\n level = applicant.get(\"operator\", 0)\n applicant[\"operator\"] = level\n if level > 0:\n data_op.append(\n {\n \"uuid\": applicant[\"uuid\"],\n \"name\": applicant[\"name\"],\n \"level\": level,\n \"bypassesPlayerLimit\": False,\n }\n )\n\n log.f(\"wl+\", \"Refreshing Whitelist\")\n with self.path_op.open(\"w\") as file_op:\n json.dump(data_op, file_op, indent=2)\n with self.path_wl.open(\"w\") as file_wl:\n json.dump(data_wl, file_wl, indent=2)\n return 1\n\n\nclass Minecraft(object):\n def __init__(self, client):\n self.client = client\n self.config = client.config\n self.interface = Interface(client)\n\n def add_entry(self, entry: type_entry_db):\n raise NotImplementedError\n\n def get_entry(self, ident: str) -> List[type_entry_db]:\n raise NotImplementedError\n\n def edit(self, ident: str):\n \"\"\"Yield a search for Entries. Then pause with the Interface open, and\n wait until the Generator is resumed. Immediately after resume, the\n Interface will close, writing changes to the file.\n \"\"\"\n with self.interface as file:\n target = find(file, ident)\n yield target\n","sub_path":"petal/util/minecraft.py","file_name":"minecraft.py","file_ext":"py","file_size_in_byte":9328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"631638045","text":"# -*- coding: utf-8 -*-\n\n\n\"\"\"\nA Python interface for the Klout API.\n\nUse of PyKlout requires a Klout API key.\nYou can register and get a key at\n\n \n\n\nhttps://github.com/marcelcaraciolo/PyKlout\n\n\n\"\"\"\n\n\n__author__ = 'Marcel Caraciolo'\n__version__ = '0.1'\n\n\nimport urllib\nimport httplib\nimport json\nimport urllib2\n\nERROR_STATUS = {\n # \"200: \"OK: Success\", IS A GOOD STATUS\n 202: \"Accepted: Your request was accepted and the user was queued for processing.\",\n 401: \"Not Authorized: either you need to provide authentication credentials, or the credentials provided aren't valid.\",\n 403: \"Bad Request: your request is invalid, This is the status code returned if you've exceeded the rate limit or if you are over QPS.\",\n 404: \"Not Found: either you're requesting an invalid URI or the resource in question doesn't exist (ex: no such user in our system).\",\n 500: \"Internal Server Error: we did something wrong.\",\n 502: \"Bad Gateway: returned if Klout is down or being upgraded.\",\n 503: \"Service Unavailable: the Klout servers are up, but are overloaded with requests. Try again later.\",\n}\n\n\nclass KloutError(Exception):\n def __init__(self, code, msg):\n super(KloutError, self).__init__()\n self.code = code\n self.msg = msg\n\n def __str__(self):\n return repr(self)\n\n def __repr__(self):\n return '%i: %s' % (self.code, self.msg)\n\n\nclass Klout(object):\n '''\n Klout API Handler\n\n Parameters\n ----------\n api_key : string the Klout API Key.\n\n '''\n API_URL = 'api.klout.com'\n\n def __init__(self, api_key):\n self._api_key = api_key\n\n def _remove_empty_params(self, params):\n '''\n Remove all unused parameters\n\n Parameters\n ----------\n params: dict object\n A set of parameters key,value\n\n Returns\n --------\n The set of parameters as dict without empty parameters\n '''\n ret = {}\n for key in params:\n if not params[key] == None:\n ret[key] = params[key]\n\n return ret\n\n def make_api_call(self, url, query={}, body={}):\n '''\n Make the API Call to Klout\n\n Parameters\n ----------\n url: the url to call\n query: The GET parameters\n body: The POST parameters\n '''\n\n query = self._remove_empty_params(query)\n\n if 'key' not in query:\n query['key'] = self._api_key\n\n body = self._remove_empty_params(body)\n query_str = urllib.urlencode(query)\n body_str = urllib.urlencode(body)\n\n if len(query) > 0:\n if url.find('?') == -1:\n url = url + '?' + query_str\n else:\n url = url + '&' + query_str\n\n try:\n conn = httplib.HTTPConnection(self.API_URL)\n if body_str:\n conn.request('POST', url, body_str)\n else:\n conn.request('GET', url)\n resp = conn.getresponse()\n data = resp.read()\n data = json.loads(data)\n except httplib.HTTPException as err:\n msg = err.read() or ERROR_STATUS.get(err.code, err.message)\n raise KloutError(err.code, msg)\n except ValueError:\n if not data:\n msg = 'Empty response from Klout.'\n raise KloutError(200, msg)\n else:\n msg = 'Invalid response: %s' % data\n raise KloutError(503, msg)\n else:\n status = data.get(\"status\")\n if status in ERROR_STATUS:\n msg = ERROR_STATUS.get(status, \"Unknow Error\")\n raise KloutError(status, msg)\n\n if data.get('body', None):\n status = data.get(\"status\")\n msg = data['body']['error']\n raise KloutError(status, msg)\n\n return data\n\n def score(self, users):\n \"\"\"\n This method allows you to retrieve a Klout score\n\n Parameters\n ----------\n users: The usernames from whom fetching the scores\n\n Returns\n -------\n A list of tuples in the form [('user1', score1), ('user2', score2)...]\n Names are returned as unicode strings and scores as floats\n\n \"\"\"\n url = '/1/klout.json'\n\n if not users:\n raise KloutError(200, 'No topics returned.')\n\n if isinstance(users, (list, tuple)):\n users = ','.join(users)\n\n query = {'users': users}\n\n data = self.make_api_call(url, query)\n\n return [(r['twitter_screen_name'], r['kscore']) for r in data['users']]\n\n def users_show(self, users):\n \"\"\"\n This method allows you to retrieve the user objects\n\n Parameters\n ----------\n users: The usernames from whom fetching the scores\n\n Returns\n -------\n A dictionary with the returned data.\n\n \"\"\"\n url = '/1/users/show.json'\n\n if not users:\n raise KloutError(200, 'No Users')\n\n if isinstance(users, (list, tuple)):\n users = ','.join(users)\n\n query = {'users': users}\n\n data = self.make_api_call(url, query)\n\n return data['users']\n\n def users_topics(self, users):\n \"\"\"\n This method allows you to retrieve the top 3 topic objects\n\n Parameters\n ----------\n users: The usernames from whom fetching the top topics\n\n Returns\n -------\n A list of dicts in the form [{'username':['topic1', 'topic2', ..]}]\n usernames and topics are returned as unicode strings\n\n \"\"\"\n url = '/1/users/topics.json'\n\n if not users:\n raise KloutError(200, 'No Users')\n\n if isinstance(users, (list, tuple)):\n users = ','.join(users)\n\n query = {'users': users}\n\n data = self.make_api_call(url, query)\n\n data = data['users']\n\n return [{user['twitter_screen_name']:[topic for topic in user['topics']]} for user in data]\n\n def users_influenced_by(self, users):\n \"\"\"\n This method allows you to retrieve up to 5 user score pairs\n for users that are influenced by the given influencer\n\n Parameters\n ----------\n users: The usernames from it will fetch the influenced usernames\n\n Returns\n -------\n A list of dicts in the form [{'username':[('username',kscore), ('username2','kscore2'),...]}]\n usernames are returned as unicode strings and kscores as floats.\n\n \"\"\"\n url = '/1/soi/influenced_by.json'\n\n if not users:\n raise KloutError(0, 'No Users')\n\n if isinstance(users, (list, tuple)):\n users = ','.join(users)\n\n query = {'users': users}\n\n data = self.make_api_call(url, query)\n\n data = data['users']\n\n return [{user['twitter_screen_name']:[(influencer['twitter_screen_name'], influencer['kscore']) \\\n for influencer in user['influencers']]} for user in data]\n\n def users_influencer_of(self, users):\n \"\"\"\n This method allows you to retrieve up to 5 user score pairs\n for users that are influencers of the given user.\n\n Parameters\n ----------\n users: The usernames from it will fetch the influenced usernames\n\n Returns\n -------\n A list of dicts in the form [{'username':[('username',kscore), ('username2','kscore2'),...]}]\n usernames are returned as unicode strings and kscores as floats.\n \"\"\"\n url = self.API_URL + '/1/soi/influencer_of.json'\n\n if not users:\n raise KloutError(0, 'No Users')\n\n if isinstance(users, (list, tuple)):\n users = ','.join(users)\n\n query = {'users': users}\n\n data = self.make_api_call(url, query)\n\n data = data['users']\n\n return [{user['twitter_screen_name']:[(influencer['twitter_screen_name'], influencer['kscore']) \\\n for influencer in user['influencees']]} for user in data]\n","sub_path":"pyklout.py","file_name":"pyklout.py","file_ext":"py","file_size_in_byte":8053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"443490910","text":"# -*- coding: utf-8 -*-\nimport urllib.request\nfrom bottle import request, response\nfrom bottle import get, put, post, delete\n\n@get('/external/find/company/')\ndef find_empresa_webservice(cnpj):\n\tres = urllib.request.urlopen('http://www.receitaws.com.br/v1/cnpj/'+ cnpj)\n\tinfo = res.info()\n\tdata = res.read()\n\tres.close()\n\treturn data","sub_path":"controller/business/external.py","file_name":"external.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"470346923","text":"import requests\nimport json\nimport csv\n\nBigParserAccountEmail = \"neilpatelbigparser@gmail.com\"\nBigParserAccountPassword = \"HackTJ2017\"\nFileIDFromGrid = \"58d73444478af706507ad13c\"\n\nurl = \"https://www.bigparser.com/APIServices/api/common/login\"\ndata = {\n \"emailId\": BigParserAccountEmail,\n \"password\": BigParserAccountPassword,\n \"loggedIn\": True\n}\ndata_json = json.dumps(data)\nheaders = {'Content-type': 'application/json'}\nauthId = requests.post(url, data=data_json,headers=headers).json()['authId']\nprint(authId)\nurl = \"https://www.bigparser.com/connectors-api/api/apps/file/googleDrive/false\"\ndata = {\n\t\"fileIDs\" : [FileIDFromGrid]\n}\ndata_json = json.dumps(data)\nprint(data_json)\nheaders = {'Content-type': 'application/json', 'authId':authId}\n\nresponse = requests.put(url, data=data_json, headers=headers).json()\ntry:\n\turl = \"https://www.bigparser.com/connectors-api/api/apps/file/googleDrive/\" + response['requestId'] + \"/status\"\n\theaders = {'authId':authId}\n\tresponse = requests.get(url, headers=headers).json()\n\tprint(response)\nexcept KeyError:\n\tprint(\"Your Grid is already synced up to the most recent version of your Google Sheet\")\n\n\nurl2 = \"https://www.bigparser.com/APIServices/api/grid/headers?gridId=58d73446478af70572adf982\" \ntry:\n\theaders = {'authId':authId}\n\tresponse = requests.get(url2, headers=headers).json()\n\tprint(response)\nexcept KeyError:\n\tprint(\"Didn't work\")\n\nurl3 = \"https://www.bigparser.com/APIServices/api/query/table\"\ntry:\n\tdata2 = {\n\t\t\"gridId\": \"58d73446478af70572adf982\",\n\t\t\"rowCount\": 2042,\n\t\t\"tags\": [\n\t\t\t{\n\t\t\t\t\"columnName\": \"lat\"\n\t\t\t}\n\t\t]\n\t}\n\tdata2_json = json.dumps(data2)\n\theaders = {'authId':authId, 'Content-type': 'application/json'}\n\tresponse = requests.post(url3, headers=headers, data=data2_json).json()\nexcept KeyError:\n\tprint(\"Didn't work\")\n","sub_path":"Server/get_lat_long_data/sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"207658835","text":"pirate_ship = [int(el) for el in input().split(\">\")]\nwarship_ship = [int(el) for el in input().split(\">\")]\n\nmax_health = int(input())\n\n\nwhile True:\n line = input()\n if line == \"Retire\":\n break\n command = line.split()[0]\n\n if command == \"Fire\":\n index = int(line.split()[1])\n damage = int(line.split()[2])\n if index in range(len(warship_ship)):\n warship_ship[index] -= damage\n if warship_ship[index] <= 0:\n print(\"You won! The enemy ship has sunken.\")\n exit(0)\n\n elif command == \"Defend\":\n start_index = int(line.split()[1])\n end_index = int(line.split()[2])\n damage = int(line.split()[3])\n\n for sections in range(start_index, end_index+1):\n pirate_ship[sections] -= damage\n if pirate_ship[sections] <= 0:\n print(\"You lost! The pirate ship has sunken.\")\n exit(0)\n\n elif command == \"Repair\":\n index = int(line.split()[1])\n health = int(line.split()[2])\n if index in range(len(pirate_ship)):\n if pirate_ship[index] + health <= max_health:\n pirate_ship[index] += health\n else:\n pirate_ship[index] = max_health\n\n elif command == \"Status\":\n counter = 0\n lowest = int(max_health * 0.2)\n for section in pirate_ship:\n if section < lowest:\n counter += 1\n print(f\"{counter} sections need repair.\")\n\nprint(f\"Pirate ship status: {sum(pirate_ship)}\")\nprint(f\"Warship status: {sum(warship_ship)}\")\n\n","sub_path":"python_fundamentals_september 2020/mid_exam/exam/03. war_ships.py","file_name":"03. war_ships.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"30441494","text":"import os\n\ndef checkFolders():\n\tcwd = os.getcwd()\n\tfor subJob in os.listdir(os.path.join(cwd, 'subJobs')):\n\t\tos.chdir(os.path.join(cwd, 'subJobs', subJob))\t\n\t\tprint(subJob + ': '),\n\t\t#cwd = os.getcwd()\n\t\tif os.path.isfile('plop.stdout') and not os.path.islink('4KUZ_localsamp.maegz'):\n\t\t\tprint('done')\n\t\telse:\n\t\t\tprint('')\n\t\tos.chdir(cwd)\n\ndef main():\n\tcheckFolders()\t\n\nif __name__ == '__main__':\n\tmain()\n","sub_path":"bkscripts/check_progress.py","file_name":"check_progress.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"653282246","text":"import os.path\nimport shutil\n\nIMAGE_DIR = \"../../caltech101\"\nSUBSET_DIR = \"caltech10\"\n\nTARGET_CATEGORY = [\"accordion\", \"bonsai\", \"cougar_face\", \"dalmatian\", \"dollar_bill\",\n \"euphonium\", \"hedgehog\", \"grand_piano\", \"Motorbikes\", \"yin_yang\", ]\n\nTOP = 50\n\nif not os.path.exists(SUBSET_DIR):\n os.mkdir(SUBSET_DIR)\n\nfor file in os.listdir(IMAGE_DIR):\n try:\n cat = file.split(\"-\")[0]\n num = int(file.split(\"_\")[1][0:4])\n except:\n continue\n\n if cat in TARGET_CATEGORY and num <= TOP:\n source_image = \"%s/%s\" %(IMAGE_DIR, file)\n dest_image = \"%s/%s\" % (SUBSET_DIR, file)\n shutil.copyfile(source_image, dest_image)\n\n","sub_path":"create_subset.py","file_name":"create_subset.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"335171588","text":"# -*- coding: utf-8 -*-\nimport os\nimport shutil\nimport json\n\nfrom pygltflib import GLTF2\nimport subprocess\n\nfrom django.conf import settings\nfrom django.core.management.base import BaseCommand\nfrom furniture.models import Model3D, Product\nfrom django.db.models import Q\nfrom render.utils import execute_wait\n\nextensions = {'blend', 'glb', 'gltf', 'obj', 'x3d', 'fbx'}\n\nclass Command(BaseCommand):\n help = f\"read {extensions} and makes pair glb/blend and save it in tmp folder\"\n\n def add_arguments(self, parser):\n parser.add_argument('model_id', nargs=1, type=int, default=False)\n\n def handle(self, *args, **options):\n \"\"\"\n функция\n - копирует файл модели во временную папку\n - запускает blender со скриптом импорта модели\n \"\"\"\n try:\n model = Model3D.objects.get(pk=options['model_id'][0])\n upload_dir = os.path.join(settings.MEDIA_ROOT, 'tmp', 'furniture', 'test')\n os.makedirs(upload_dir, exist_ok=True)\n dest = os.path.join(upload_dir, f\"{model.name}.blend\")\n if not os.path.exists(model.blender_file):\n print(\"BLEND NOT EXISTS\")\n return\n # print('dest', dest)\n shutil.copy(model.blender_file, dest)\n cmd = [os.getenv('BLENDER'), dest, '--background', '-noaudio', '--python', 'blender/blender-import_model.py']\n for s in execute_wait(cmd):\n if 'ERROR:' in s or 'Exception' in s:\n # if 'WARNING:' in s or 'ERROR:' in s or 'Exception' in s or settings.DEBUG:\n self.stdout.write(f\"[blender] {s}\")\n with open(os.path.join(upload_dir, f\"{model.name}.json\"), \"r\") as f:\n info = json.loads(f.read())['meshes']\n _meshes = {m.name: {} for m in model.mesh_set.all()}\n for m in model.mesh_set.all():\n _meshes[m.name][m.material] = 4\n for mesh_info in info:\n meshname = mesh_info['name']\n if meshname not in _meshes:\n _meshes[meshname] = {}\n # \"materials\": [],\n # \"primitives\": [\n # {\n # \"material\": null,\n # \"faces\": 1,\n # \"uv_area\": 1.0,\n # \"gm_area\": 4.0,\n # \"density\": 0.5\n # }\n if not len(mesh_info['materials']):\n if None not in _meshes[meshname]:\n _meshes[meshname][None] = 0\n _meshes[meshname][mat] += 2\n else:\n for mat in mesh_info['materials']:\n if mat not in _meshes[meshname]:\n _meshes[meshname][mat] = 0\n _meshes[meshname][mat] += 2\n print('**_meshes**', _meshes)\n meshes = model.mesh_set.all()\n mesh_ids = [mesh.id for mesh in meshes]\n immutable = []\n hidden = [] # 0 2\n ok = [] # 4 2 *\n errors = []\n for mesh_info in info:\n # print(\">>\", mesh_info['name'], \", primitives:\", len(mesh_info['primitives']))\n pr = meshes.filter(name=mesh_info['name'])\n if not len(pr): # нет такого меша в базе\n # print(f\"{mesh_info['name']} not in db\")\n if len(mesh_info['materials']):\n immutable.append(mesh_info['name'])\n # print(\"immutable in all products\")\n else:\n hidden.append(mesh_info['name'])\n # print(\"hidden\")\n elif len(pr) == 1:\n # update DB\n m = pr[0]\n if len(mesh_info['materials']) == 1:\n if m.material:\n if m.material != mesh_info['materials'][0]:\n errors.append(f\"material {m.material} != {mesh_info['materials'][0]}\")\n else:\n m.material = mesh_info['materials'][0]\n if len(mesh_info['primitives']) == 1:\n prim = mesh_info['primitives'][0]\n if not m.area:\n m.area = prim['gm_area']\n m.uv_area = prim['uv_area']\n m.polygons = prim['faces']\n # print(\"SAVE NEW DATA\", mesh_info['name'], pr[0].area, pr[0].area, m.polygons)\n mesh_ids.remove(pr[0].id)\n ok.append(mesh_info['name'])\n else:\n errors.append(f\"в базе меш с таким именем один, а в модели {len(mesh_info['primitives'])}\")\n #print(\"ERROR\", errors[-1])\n else: # имеем дело с примитивами - смотрим на материалы\n for prim in mesh_info['primitives']:\n pr = pr.filter(material=prim['material'])\n if not len(pr):\n print(\"primitive ERROR, not in db\", prim['material'])\n immutable.append(f\"primitive {prim['material']} on {mesh_info['name']}\")\n else:\n # print(\"in db\", pr[0])\n ok.append(f\"primitive {prim['material']} on {mesh_info['name']}\")\n mesh_ids.remove(pr[0].id)\n if len(mesh_ids):\n errors.append(f\"there are meshes in the database that are not in the file model: {[(i, meshes.get(pk=i).name) for i in mesh_ids]}\")\n #print(\"ERROR - not existed meshes in db\", [(i, meshes.get(pk=i).name) for i in mesh_ids])\n self.stdout.write(f\"ok: {ok}, immutable: {immutable}, hidden: {hidden}\")\n if len(errors):\n self.stdout.write(f\"ERRORS: {errors}\")\n # self.stdout.write(\"management command completed\")\n\n except Exception as e:\n self.stdout.write(f\"!Exception: {repr(e)}\")\n raise\n finally:\n self.stdout.flush()\n","sub_path":"rendering/furniture/management/commands/analize_model.py","file_name":"analize_model.py","file_ext":"py","file_size_in_byte":6413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"1632032","text":"import dash\nimport dash_core_components as dcc\nimport dash_bootstrap_components as dbc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\nfrom dash.exceptions import PreventUpdate\nimport dash_table\nimport plotly.express as px\nfrom plotly.subplots import make_subplots\nimport pandas as pd\nimport mysql.connector\nfrom datetime import datetime,timedelta\nimport classes\nimport ranEngDashboardStyles as styles\nimport ran_functions\n\napp = dash.Dash(__name__, title='RAN Dashboard', external_stylesheets=[dbc.themes.BOOTSTRAP])\nserver = app.server\n\n# DB Connection Parameters\ndbPara = classes.dbCredentials()\n# FTP Connection Parameters\nftpLogin = classes.ranFtpCredentials()\n# Data\nranController = classes.ranControllers()\nnetworkAlarmFilePath = \"/configuration_files/NBI_FM/{}/\".format(str(datetime.now().strftime('%Y%m%d')))\ntopWorstFilePath = \"/BSC/top_worst_report/\"\nzeroTrafficFilePath = \"/BSC/zero_traffic/\"\nneOosLineChartDf = pd.DataFrame(data={'time':[], 'counter':[]})\n\n# Styles\ntabStyles = styles.headerStyles()\nnetworkOverviewStyles = styles.networkOverviewTab()\nengDashboardStyles = styles.engDashboardTab()\ngraphSyles = styles.graphStyles()\ndataTableStyles = styles.topWorstTab()\nnetworkCheckStyles = styles.networkCheckTab()\ngraphColors = styles.NetworkWideGraphColors()\ngraphInsightStyles = styles.graphInsightTab()\ntxCheckStyles = styles.txCheckTab()\ngraphTitleFontSize = 14\n\napp.layout = html.Div(children=[\n # Header & tabbed menu\n html.Div(\n style = tabStyles.headerFlexContainer,\n children = [\n html.H4(\n id = 'dashboardTitle',\n children = 'RAN Dashboard',\n style = tabStyles.dashboardTitle\n ),\n dcc.Tabs(\n id = 'tabsContainer',\n value = 'Network Overview',\n children = [\n dcc.Tab(\n label = 'Network Overview', \n value = 'Network Overview', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Engineering Dashboard', \n value = 'Engineering Dashboard', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Top Worst Report', \n value = 'Top Worst Report', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Network Check', \n value = 'Network Check', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Graph Insight', \n value = 'Graph Insight', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Tx Status', \n value = 'Tx Status', \n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n )\n ]\n )\n ]\n ),\n # Network Overview Tab\n html.Div(\n id = 'networkOverviewGridContainer',\n style = networkOverviewStyles.networkOverviewGridContainerStyle,\n children = [\n # BSC Dropdown\n html.Div(\n id = 'bscDropdownGridElement',\n style = networkOverviewStyles.bscDropdownGridElement,\n children = [\n dbc.DropdownMenu(\n children = [\n dcc.Checklist(\n id = 'bscDropdown',\n options = [\n {'label':'BSC_01_RRA', 'value':'BSC_01_RRA'},\n {'label':'BSC_02_STGO', 'value':'BSC_02_STGO'},\n {'label':'BSC_03_VM', 'value':'BSC_03_VM'},\n {'label':'BSC_04_VM', 'value':'BSC_04_VM'},\n {'label':'BSC_05_RRA', 'value':'BSC_05_RRA'},\n {'label':'BSC_06_STGO', 'value':'BSC_06_STGO'},\n {'label':'N/A', 'value':'N/A'}\n ],\n value = ['BSC_01_RRA', 'BSC_02_STGO', 'BSC_03_VM', 'BSC_04_VM', 'BSC_05_RRA', 'BSC_06_STGO'],\n labelStyle = {'display': 'block'}\n )\n ],\n label = 'BSC',\n color = 'primary'\n )\n ]\n ),\n # Logic Gate #1\n html.Div(\n id = 'gateOneDropdownGridElement',\n style = networkOverviewStyles.gateOneDropdownGridElement,\n children = [\n dcc.Dropdown(\n id = 'gateOneDropdown',\n options = [\n {'label':'AND', 'value':'AND'},\n {'label':'OR', 'value':'OR'}\n ],\n value = 'OR',\n clearable=False\n )\n ]\n ),\n # RNC Dropdown\n html.Div(\n id = 'rncDropdownGridElement',\n style = networkOverviewStyles.rncDropdownGridElement,\n children = [\n dbc.DropdownMenu(\n children = [\n dcc.Checklist(\n id = 'rncDropdown',\n options = [\n {'label':'RNC_01_RRA', 'value':'RNC_01_RRA'},\n {'label':'RNC_02_STGO', 'value':'RNC_02_STGO'},\n {'label':'RNC_03_VM', 'value':'RNC_03_VM'},\n {'label':'RNC_04_VM', 'value':'RNC_04_VM'},\n {'label':'RNC_05_RRA', 'value':'RNC_05_RRA'},\n {'label':'RNC_06_STGO', 'value':'RNC_06_STGO'},\n {'label':'RNC_07_VM', 'value':'RNC_07_VM'},\n {'label':'N/A', 'value':'N/A'}\n ],\n value = ['RNC_01_RRA', 'RNC_02_STGO', 'RNC_03_VM', 'RNC_04_VM', 'RNC_05_RRA', 'RNC_06_STGO', 'RNC_07_VM'],\n labelStyle = {'display': 'block'}\n )\n ],\n label = 'RNC',\n color = 'danger'\n )\n ]\n ),\n # Logic Gate #2\n html.Div(\n id = 'gateTwoDropdownGridElement',\n style = networkOverviewStyles.gateTwoDropdownGridElement,\n children = [\n dcc.Dropdown(\n id = 'gateTwoDropdown',\n options = [\n {'label':'AND', 'value':'AND'},\n {'label':'OR', 'value':'OR'}\n ],\n value = 'OR',\n clearable=False\n )\n ]\n ),\n # LTE RAT Dropdown\n html.Div(\n id = 'lteDropdownGridElement',\n style = networkOverviewStyles.lteDropdownGridElement,\n children = [\n dbc.DropdownMenu(\n children = [\n dcc.Checklist(\n id = 'lteDropdown',\n options = [\n {'label':'L1900', 'value':'L1900'},\n {'label':'AWS', 'value':'AWS'},\n {'label':'L850', 'value':'L850'},\n {'label':'L900', 'value':'L900'},\n {'label':'WTTX', 'value':'WTTX'},\n {'label':'N/A', 'value':'N/A'}\n ],\n value = ['L1900', 'AWS', 'L850', 'L900', 'WTTX'],\n labelStyle = {'display': 'block'}\n )\n ],\n label = 'LTE Bands',\n color = 'success',\n style = {'width':'100%'}\n )\n ]\n ),\n html.Div(\n id = 'mapGridElement',\n style = networkOverviewStyles.mapGridElement,\n children = [\n dcc.Graph(\n id = 'networkMap'\n )\n ]\n ),\n html.Div(\n id = 'gsmDistributionGraph',\n style = networkOverviewStyles.gsmDistGraphElement,\n children = [\n dcc.Graph(\n id = 'gsmPieChart'\n )\n ]\n ),\n html.Div(\n id = 'umtsDistributionGraph',\n style = networkOverviewStyles.umtsDistGraphElement,\n children = [\n dcc.Graph(\n id = 'umtsPieChart'\n )\n ]\n ),\n html.Div(\n id = 'lteDistributionGraph',\n style = networkOverviewStyles.lteDistGraphElement,\n children = [\n dcc.Graph(\n id = 'ltePieChart'\n )\n ]\n ),\n html.Div(\n id = 'neOosOverviewGraph',\n style = networkOverviewStyles.neOosOverviewGraphElement,\n children = [\n dcc.Graph(\n id = 'neOosOverviewChart'\n )\n ]\n )\n ]\n ),\n # Engineering Dashboard Tab\n html.Div(\n id = 'graphGridContainer',\n style = engDashboardStyles.graphGridContainerStyle,\n children = [\n html.Div(\n id = 'dataTypeDropdownGridElement',\n style = engDashboardStyles.dataTypeDropdownGridElement,\n children = [\n dcc.Dropdown(\n id = 'dataTypeDropdown',\n options = [\n {'label':'CS Call Setup Success Rate', 'value':'CS Call Setup Success Rate'}, \n {'label':'PS Call Setup Success Rate', 'value':'PS Call Setup Success Rate'}, \n {'label':'CS Drop Call Rate', 'value':'CS Drop Call Rate'}, \n {'label':'PS Drop Call Rate', 'value':'PS Drop Call Rate'}, \n {'label':'Assignment Success Rate', 'value':'Assignment Success Rate'}, \n {'label':'Location Update Success Rate', 'value':'Location Update Success Rate'}\n ],\n value = 'PS Drop Call Rate',\n style = {\n 'width': '100%', \n 'font-size': str(graphTitleFontSize) + 'px', \n 'text-align': 'center'\n }\n )\n ]\n ),\n html.Div(\n id = 'timeFrameDropdownGridElement',\n style = engDashboardStyles.timeFrameDropdownGridElement,\n children = [\n dcc.Dropdown(\n id='timeFrameDropdown',\n options=[\n {'label':'1 Day', 'value':'1'}, \n {'label':'3 Days', 'value':'3'}, \n {'label':'7 Days', 'value':'7'}, \n {'label':'30 Days', 'value':'30'}\n ],\n # value var is the default value for the drop down.\n value='1',\n style={\n 'width': '100%', \n 'font-size': str(graphTitleFontSize) + 'px', \n 'text-align': 'center'\n }\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n id = 'bscGraphContainer',\n style = engDashboardStyles.bscGraphContainer,\n children = [\n 'BSC Graph',\n dcc.Graph(\n id = 'bscGraph'\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n id = 'rncGraphContainer',\n style = engDashboardStyles.rncGraphContainer,\n children = [\n 'RNC Graph',\n dcc.Graph(\n id = 'rncGraph'\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n id = 'trxGraphContainer',\n style = engDashboardStyles.trxGraphContainer,\n children = [\n 'TRX Utilization',\n dcc.Graph(\n id = 'trxUsageGraph'\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n id = 'neOosGraphContainer',\n style = engDashboardStyles.neOosGraphContainer,\n children = [\n 'NE OOS',\n dcc.Graph(\n id = 'neOosGraph'\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n id = 'neOosLineChartContainer',\n style = engDashboardStyles.neOosLineChartContainer,\n children = [\n 'NE OOS',\n dcc.Graph(\n id = 'neOosLineChart'\n )\n ]\n ),\n html.Div(\n className = 'gridElement',\n style = engDashboardStyles.neOosListContainer,\n children = [\n html.H3('Top NE OOS'),\n dash_table.DataTable(\n id = 'neOosListDataTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell,\n sort_action = 'native'\n )\n ]\n )\n ]\n ),\n # Hidden datatable to store graph values\n html.Div(\n className = 'hiddenElement',\n style = {'display':'none'},\n children = [\n dash_table.DataTable(\n id = 'hiddenNeOosLineChartDatatable',\n columns = [{'name': i, 'id': i} for i in neOosLineChartDf.columns],\n data = neOosLineChartDf.to_dict('records')\n )\n ]\n ),\n # Top Worst Reports Tab\n html.Div(\n id = 'outerTopWorstReportFlexContainer',\n style = dataTableStyles.outerTopWorstReportFlexContainer,\n children = [\n # Inner Tab Container\n dcc.Tabs(\n id = 'innerTopWorstTabContainer',\n value = 'Daily Report',\n style = dataTableStyles.innerTopWorstTabContainer,\n children = [\n dcc.Tab(\n label = 'Daily Report',\n value = 'Daily Report',\n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Zero Traffic',\n value = 'Zero Traffic',\n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n ),\n dcc.Tab(\n label = 'Records',\n value = 'Records',\n style = tabStyles.tabStyle,\n selected_style = tabStyles.tabSelectedStyle\n )\n ]\n ),\n # Daily Top Worst Reports\n html.Div(\n id = 'datatableGridContainer', \n style = dataTableStyles.datatableGridContainer,\n children = [\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst LTE eRAB SR'),\n dash_table.DataTable(\n id = 'topWorst4GeRabSrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst LTE DCR'),\n dash_table.DataTable(\n id='topWorst4GDcrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst 3G PS CSSR'),\n dash_table.DataTable(\n id = 'topWorst3GPsCssrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst 3G CS CSSR'),\n dash_table.DataTable(\n id='topWorst3GCsCssrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst 3G PS DCR'),\n dash_table.DataTable(\n id='topWorst3GPsDcrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst 3G CS DCR'),\n dash_table.DataTable(\n id='topWorst3GCsDcrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst GSM CSSR'),\n dash_table.DataTable(\n id='topWorst2GSpeechCssrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('Top Worst GSM DCR'),\n dash_table.DataTable(\n id='topWorst2GSpeechDcrTable',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n )\n ]\n ),\n # Zero Traffic Daily Reports\n html.Div(\n id = 'zeroTrafficGridContainer',\n style = dataTableStyles.zeroTrafficGridContainer,\n children = [\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('LTE DL Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic4GDl',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('LTE UL Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic4GUl',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('UMTS Voice Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic3GVoice',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('UMTS HSDPA Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic3GHsdpa',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('UMTS HSUPA Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic3GHsupa',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n ),\n html.Div(\n className = 'datatableGridElement',\n children = [\n html.H3('GSM Zero Traffic'),\n dash_table.DataTable(\n id = 'zeroTraffic2G',\n style_header = dataTableStyles.style_header,\n style_cell = dataTableStyles.style_cell\n )\n ]\n )\n ]\n ),\n # Top Reports Records\n html.Div(\n id = 'topReportRecordGridContainer',\n style = dataTableStyles.topWorstRecordGridContainer,\n children = [\n html.Div(\n children = [\n html.H3('LTE eRAB SR Records'),\n html.Button('Add Entry', id = 'topWorst4GeRabSrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst4GeRabSrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst4GeRabSrRecordTableSubmit', n_clicks = 0),\n html.H3('LTE DCR Records'),\n html.Button('Add Entry', id = 'topWorst4GDcrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst4GDcrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst4GDcrRecordTableSubmit', n_clicks = 0),\n html.H3('3G PS CSSR Records'),\n html.Button('Add Entry', id = 'topWorst3GPsCssrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst3GPsCssrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst3GPsCssrRecordTableSubmit', n_clicks = 0),\n html.H3('3G CS CSSR Records'),\n html.Button('Add Entry', id = 'topWorst3GCsCssrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst3GCsCssrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst3GCsCssrRecordTableSubmit', n_clicks = 0),\n html.H3('3G PS DCR Records'),\n html.Button('Add Entry', id = 'topWorst3GPsDcrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst3GPsDcrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst3GPsDcrRecordTableSubmit', n_clicks = 0),\n html.H3('3G CS DCR Records'),\n html.Button('Add Entry', id = 'topWorst3GCsDcrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst3GCsDcrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst3GCsDcrRecordTableSubmit', n_clicks = 0),\n html.H3('GSM CSSR Records'),\n html.Button('Add Entry', id = 'topWorst2GSpeechCssrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst2GSpeechCssrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst2GSpeechCssrRecordTableSubmit', n_clicks = 0),\n html.H3('GSM DCR Records'),\n html.Button('Add Entry', id = 'topWorst2GSpeechDcrRecordTableClicks', n_clicks = 0),\n dash_table.DataTable(\n id = 'topWorst2GSpeechDcrRecordTable',\n style_header = dataTableStyles.style_header,\n columns = [{'name': '', 'id': ''}],\n include_headers_on_copy_paste = True,\n editable = True,\n row_deletable = True\n ),\n html.Button('Submit', id = 'topWorst2GSpeechDcrRecordTableSubmit', n_clicks = 0),\n ]\n )\n ]\n )\n ]\n ),\n # Network Check Tab\n html.Div(\n id = 'networkCheckGridContainer',\n style = networkCheckStyles.networkCheckGridContainer,\n children = [ \n html.Div(\n className = 'networkCheckGridElement',\n id = 'cssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'lteDataCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'volteCssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'lteVolteCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'dcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'lteDataDcrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'volteDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'lteVolteDcrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'hsdpaCssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'hsdpaCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'hsupaCssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'hsupaCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'umtsCssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'umtsCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'hsdpaDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'hsdpaDcrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'hsupaDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'hsupaDcrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'umtsDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'umtsDcrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'gsmCsCssrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'gsmCsCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'gsmPsDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'gsmPsCssrNetworkWideGraph'\n )\n ]\n ),\n html.Div(\n className = 'networkCheckGridElement',\n id = 'gsmCsDcrNetworkWideGraphGridElement',\n children = [\n dcc.Graph(\n id = 'gsmCsDcrNetworkWideGraph'\n )\n ]\n )\n ]\n ),\n # Graph Insight Tab\n html.Div(\n id = 'graphInsightFlexContainer',\n style = graphInsightStyles.graphInsightFlexContainer,\n children = [\n html.Div(\n id = 'graphInsightDropdownContainer',\n style = graphInsightStyles.graphInsightDropdownContainer,\n children = [\n dcc.Dropdown(\n id = 'graphInsightRat',\n style = graphInsightStyles.graphInsightRat,\n options = [\n {'label':'GSM', 'value':'GSM'},\n {'label':'UMTS', 'value':'UMTS'},\n {'label':'LTE', 'value':'LTE'}\n ],\n value = 'LTE'\n ),\n dcc.Dropdown(\n id = 'graphInsightGraphGroup',\n style = graphInsightStyles.graphInsightGraphGroup,\n value = 'None'\n ),\n dcc.Dropdown(\n id = 'graphInsightGraphType',\n style = graphInsightStyles.graphInsightGraphType,\n value = 'None'\n )\n ]\n ),\n html.Div(\n id = 'graphInsightGraphContainer',\n style = graphInsightStyles.graphInsightGraphContainer,\n children = [\n dcc.Graph(\n id = 'graphInsightGraph'\n )\n ]\n ),\n html.Div(\n dash_table.DataTable(\n id = 'graphInsightTable',\n columns = [{'name':'Parameter','id':'Parameter'}, {'name':'Last Week','id':'Last Week'}, {'name':'Current','id':'Current'}, {'name':'Delta','id':'Delta'}]\n )\n )\n ]\n ),\n # Tx Check Tab\n html.Div(\n id = 'txCheckGridContainer',\n style = txCheckStyles.txCheckGridContainer,\n children = [\n dcc.Graph(\n id = 'umtsNetworkPacketLossGraph'\n ),\n dcc.Graph(\n id = 'umtsNetworkDelayGraph'\n ),\n dcc.Graph(\n id = 'gsmNetworkPacketLossGraph'\n ),\n dcc.Graph(\n id = 'gsmNetworkDelayGraph'\n )\n ]\n ),\n dcc.Interval(\n id='dataUpateInterval', \n interval=300*1000, \n n_intervals=0\n )\n])\n\n# Callback to update Network Overview Tab\n@app.callback(\n [\n Output('networkMap', 'figure'),\n Output('gsmPieChart', 'figure'),\n Output('umtsPieChart', 'figure'),\n Output('ltePieChart', 'figure'),\n Output('neOosOverviewChart', 'figure')\n ],\n [\n Input('dataUpateInterval', 'n_intervals'),\n Input('tabsContainer', 'value'),\n Input('bscDropdown', 'value'),\n Input('rncDropdown', 'value'),\n Input('lteDropdown', 'value'),\n Input('gateOneDropdown', 'value'),\n Input('gateTwoDropdown', 'value')\n ],\n State('neOosListDataTable', 'data'),\n State('hiddenNeOosLineChartDatatable', 'data')\n)\ndef updateNetworkOverviewTab(interval, selectedTab, bscList, rncList, lteList, gateOneDropdown, gateTwoDropdown, neOosListDataTableData, hiddenNeOosLineChartDatatableValue):\n # Connect to DB\n mysqlConnector = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n mysqlPointer = mysqlConnector.cursor(buffered=True)\n if selectedTab == 'Network Overview':\n # NE OOS Graph\n startTime = (datetime.now() - timedelta(minutes=5)).strftime(\"%Y/%m/%d %H:%M:%S\")\n neOosLineChart = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n neOosPieChart, neOosLineChart, hiddenNeOosLineChartDatatableValue, neOosListDataTableData = ran_functions.neOosGraph(mysqlPointer, startTime, neOosLineChart, hiddenNeOosLineChartDatatableValue)\n neOosPieChart.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize, \n font_size=12, \n title='NE OOS Chart', \n margin=dict(l=10, r=10, t=40, b=10), \n legend=dict(orientation='h')\n )\n neOosPieChart.update_traces(\n textinfo = 'value',\n hoverinfo = 'all'\n )\n siteDataframe, bscPieChart, rncPieChart, ltePieChart = ran_functions.networkMapFunction(mysqlPointer, bscList, rncList, lteList, gateOneDropdown, gateTwoDropdown)\n neOosList = []\n # Check if dataframe is not empty first\n if str(type(neOosListDataTableData)) != '':\n # Loop through current NE OOS list\n for dic in neOosListDataTableData:\n tmpNE = dic['NE']\n # If the 2nd position is R or T, then we must remove the first 2 digits from the NE name (NR or LT scenarios)\n if tmpNE[1] == 'R' or tmpNE[1] == 'T':\n tmpNE = tmpNE[2:-1]\n else:\n tmpNE = tmpNE[1:-1]\n neOosList.append(tmpNE)\n # Add NE Status column\n siteDataframe['oos_status'] = 'Online'\n # Check in case there are no NE OOS\n if len(neOosList) > 0:\n for ne in neOosList:\n for i in range(len(siteDataframe['site'])):\n if siteDataframe['site'][i] == ne:\n siteDataframe['oos_status'][i] = 'Offline'\n map = px.scatter_mapbox(siteDataframe, lat='lat', lon='lon', hover_name='site', hover_data=['bsc', 'rnc'], zoom=7, color='oos_status')\n map.update_layout(\n mapbox_style='open-street-map',\n margin=dict(l=2, r=2, t=2, b=2)\n #height=450\n )\n map.update_traces(marker=dict(size=10))\n bscPieChart.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize, \n font_size=12, \n title='GSM Distribution Chart', \n margin=dict(l=10, r=10, t=40, b=10)\n )\n bscPieChart.update_traces(\n textinfo = 'value+percent',\n hoverinfo = 'all'\n )\n rncPieChart.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize, \n font_size=12, \n title='UMTS Distribution Chart', \n margin=dict(l=10, r=10, t=40, b=10)\n )\n rncPieChart.update_traces(\n textinfo = 'value+percent',\n hoverinfo = 'all'\n )\n ltePieChart.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize, \n font_size=12, \n title='LTE Band Distribution Chart', \n margin=dict(l=10, r=10, t=40, b=10)\n )\n ltePieChart.update_traces(\n textinfo = 'value+percent',\n hoverinfo = 'all'\n )\n # Close DB connection\n mysqlPointer.close()\n mysqlConnector.close()\n return map, bscPieChart, rncPieChart, ltePieChart, neOosPieChart\n else:\n # Close DB Connection\n mysqlPointer.close()\n mysqlConnector.close()\n # Used in case there is no update needed on callback\n raise PreventUpdate\n\n# Callback to update Engineering Dashboard Tab\n@app.callback(\n [\n Output('bscGraph', 'figure'), \n Output('rncGraph', 'figure'), \n Output('trxUsageGraph', 'figure'),\n Output('neOosGraph', 'figure'),\n Output('neOosLineChart', 'figure'),\n Output('hiddenNeOosLineChartDatatable', 'data'),\n Output('neOosListDataTable', 'columns'),\n Output('neOosListDataTable', 'data')\n ], \n [\n # We use the update interval function and both dropdown menus as inputs for the callback\n Input('dataUpateInterval', 'n_intervals'),\n Input('tabsContainer', 'value'),\n Input('timeFrameDropdown', 'value'),\n Input('dataTypeDropdown', 'value')\n ],\n State('hiddenNeOosLineChartDatatable', 'data')\n)\ndef updateEngDashboardTab(currentInterval, selectedTab, timeFrameDropdown, dataTypeDropdown, hiddenNeOosLineChartDatatableValue):\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n if selectedTab == 'Engineering Dashboard':\n # Instantiate the plots\n bscHighRefresh = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n rncHighRefresh = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n gsmGraphValueConversionDict = {'CS Call Setup Success Rate':'cssr', 'PS Call Setup Success Rate':'edgedlssr', 'CS Drop Call Rate':'dcr', 'PS Drop Call Rate':'edgedldcr', 'Assignment Success Rate':'assignmentsuccessrate', 'Location Update Success Rate':'luupdatesr'}\n umtsGraphValueConversionDict = {'CS Call Setup Success Rate':'csconnectionsuccessrate', 'PS Call Setup Success Rate':'psrtsuccessrate', 'CS Drop Call Rate':'csdropcallrate', 'PS Drop Call Rate':'psdropcallrate', 'Assignment Success Rate':'rrcconnectionsuccessrate', 'Location Update Success Rate':'pagingsuccessrate'}\n daysDelta = int(timeFrameDropdown)\n # starttime is the current date/time - daysdelta\n startTime = (datetime.now() - timedelta(days=daysDelta)).strftime(\"%Y/%m/%d %H:%M:%S\")\n bscHighRefresh = ran_functions.bscHighRefreshQuery(pointer, startTime, bscHighRefresh, ranController.bscNameList, gsmGraphValueConversionDict, dataTypeDropdown)\n # Set Graph background colores & title font size\n bscHighRefresh.update_layout(\n plot_bgcolor=graphSyles.plot_bgcolor, \n paper_bgcolor=graphSyles.paper_bgcolor, \n font_color=graphSyles.font_color, \n title_font_size=graphTitleFontSize,\n font_size=12,\n margin=dict(l=10, r=10, t=10, b=10),\n legend=dict(orientation='h')\n )\n rncHighRefresh = ran_functions.rncHighRefreshQuery(pointer, startTime, rncHighRefresh, ranController.rncNameList, umtsGraphValueConversionDict, dataTypeDropdown)\n # Set Graph background colores & title font size\n rncHighRefresh.update_layout(\n plot_bgcolor=graphSyles.plot_bgcolor, \n paper_bgcolor=graphSyles.paper_bgcolor, \n font_color=graphSyles.font_color, \n title_font_size=graphTitleFontSize,\n font_size=12,\n margin=dict(l=10, r=10, t=10, b=10),\n legend=dict(orientation='h')\n )\n # TRX Utilization Graph\n tempDataFrame = {'neName':[], 'ipPoolId':[], 'trxQty':[]}\n # Loop through BSC Names\n for ne in ranController.bscNameList:\n # Loop through Ip Pool ID range (10 - 12)\n for ippool in range(10,13):\n tempDataFrame['neName'].append(ne)\n # Must change ippool to string for the bar chart to display in group mode.\n tempDataFrame['ipPoolId'].append(str(ippool))\n pointer.execute('SELECT trxqty FROM ran_pf_data.trx_usage_data where lastupdate >= \\'' + datetime.now().strftime(\"%Y/%m/%d\") + '\\' and nename = \\'' + ne + '\\' and ippoolid = ' + str(ippool) + ' order by lastupdate desc;')\n queryPayload = pointer.fetchone()\n # Must check if query result is empty, to full with 0\n if queryPayload:\n # Take the latest value on the DB\n tempDataFrame['trxQty'].append(queryPayload[0])\n else:\n tempDataFrame['trxQty'].append(0)\n ipPoolReportDf = pd.DataFrame(tempDataFrame, columns = ['neName', 'ipPoolId', 'trxQty'])\n trxUsageGraph = px.bar(ipPoolReportDf, x='neName', y='trxQty', color='ipPoolId', barmode='group', template='simple_white')\n trxUsageGraph.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize,\n font_size=graphTitleFontSize,\n title='TRX Load per Interface'\n )\n # NE OOS Graph\n startTime = (datetime.now() - timedelta(minutes=5)).strftime(\"%Y/%m/%d %H:%M:%S\")\n neOosLineChart = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n neOosPieChart, neOosLineChart, hiddenNeOosLineChartDatatableValue, neOosListDataTableData = ran_functions.neOosGraph(pointer, startTime, neOosLineChart, hiddenNeOosLineChartDatatableValue)\n neOosPieChart.update_layout(\n plot_bgcolor='#000000', \n paper_bgcolor='#000000', \n font_color='#FFFFFF', \n title_font_size=graphTitleFontSize, \n font_size=12, \n title='NE OOS Chart', \n margin=dict(l=10, r=10, t=40, b=10), \n legend=dict(orientation='h')\n )\n neOosPieChart.update_traces(\n textinfo = 'value',\n hoverinfo = 'all'\n )\n # Set Graph background colores & title font size\n neOosLineChart.update_layout(\n plot_bgcolor=graphSyles.plot_bgcolor, \n paper_bgcolor=graphSyles.paper_bgcolor, \n font_color=graphSyles.font_color, \n title_font_size=graphTitleFontSize,\n margin=dict(l=10, r=10, t=10, b=10),\n )\n neOosListDataTableColumns = [{'name':'NE', 'id':'NE'}, {'name':'Reason', 'id':'Reason'}]\n # Close DB Connection\n pointer.close()\n connectr.close()\n return bscHighRefresh, rncHighRefresh, trxUsageGraph, neOosPieChart, neOosLineChart, hiddenNeOosLineChartDatatableValue, neOosListDataTableColumns, neOosListDataTableData\n else:\n # Close DB Connection\n pointer.close()\n connectr.close()\n # Used in case there is no update needed on callback\n raise PreventUpdate\n\n# Callback to update Top Worst Tab\n@app.callback(\n [\n Output('topWorst4GeRabSrTable', 'columns'),\n Output('topWorst4GeRabSrTable', 'data'),\n Output('topWorst4GeRabSrRecordTable', 'columns'),\n Output('topWorst4GDcrTable', 'columns'),\n Output('topWorst4GDcrTable', 'data'),\n Output('topWorst4GDcrRecordTable', 'columns'),\n Output('topWorst3GPsCssrTable', 'columns'),\n Output('topWorst3GPsCssrTable', 'data'),\n Output('topWorst3GPsCssrRecordTable', 'columns'),\n Output('topWorst3GCsCssrTable', 'columns'),\n Output('topWorst3GCsCssrTable', 'data'),\n Output('topWorst3GCsCssrRecordTable', 'columns'),\n Output('topWorst3GPsDcrTable', 'columns'),\n Output('topWorst3GPsDcrTable', 'data'),\n Output('topWorst3GPsDcrRecordTable', 'columns'),\n Output('topWorst3GCsDcrTable', 'columns'),\n Output('topWorst3GCsDcrTable', 'data'),\n Output('topWorst3GCsDcrRecordTable', 'columns'),\n Output('topWorst2GSpeechCssrTable', 'columns'),\n Output('topWorst2GSpeechCssrTable', 'data'),\n Output('topWorst2GSpeechCssrRecordTable', 'columns'),\n Output('topWorst2GSpeechDcrTable', 'columns'),\n Output('topWorst2GSpeechDcrTable', 'data'),\n Output('topWorst2GSpeechDcrRecordTable', 'columns')\n ], \n Input('tabsContainer', 'value')\n)\ndef updateTopWorstTab(selectedTab):\n # Ensure to refresh top worst tables only if that tab is selected\n if selectedTab == 'Top Worst Report':\n topWorstDirList = ran_functions.getFtpPathFileList(ftpLogin, topWorstFilePath)\n # Top Worst Reports Variables\n current2GTopWorstDcrFile = \"\"\n current2GTopWorstCssrFile = \"\"\n current3GTopWorstFile = \"\"\n current4GTopWorstFile = \"\"\n topWorstCurrentDate = str(datetime.now().strftime('%Y%m%d'))\n # find the latest file on the directory\n for file in topWorstDirList:\n if topWorstCurrentDate and \"2G\" and \"CSSR\" in file:\n current2GTopWorstCssrFile = file\n if topWorstCurrentDate and \"2G\" and \"DCR\" in file:\n current2GTopWorstDcrFile = file\n if topWorstCurrentDate and \"3G\" in file:\n current3GTopWorstFile = file\n if topWorstCurrentDate and \"LTE\" in file:\n current4GTopWorstFile = file\n # Open the latest files as dataframes\n current4GTopWorstDcrDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, topWorstFilePath, current4GTopWorstFile), sheet_name='TOP 50 Drop LTE', na_values='NIL')\n current4GTopWorsteRabSrDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, topWorstFilePath, current4GTopWorstFile), sheet_name='TOP 50 E-RAB Setup', na_values='NIL')\n current3GTopWorstDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, topWorstFilePath, current3GTopWorstFile), na_values=['NIL', '/0'])\n current2GTopWorstCssrDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, topWorstFilePath, current2GTopWorstCssrFile), sheet_name='Subreport 1', na_values='NIL')\n current2GTopWorstDcrDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, topWorstFilePath, current2GTopWorstDcrFile), sheet_name='Subreport 1', na_values='NIL')\n # Filter the selected columns\n topWorst4GeRabSrDataframe = current4GTopWorsteRabSrDataframe.filter(items = ['eNodeB Name', 'Cell FDD TDD Indication', 'Cell Name', 'E-RAB Setup Success Rate (ALL)[%](%)', 'Date'])\n # Fill N/A values as 0\n topWorst4GeRabSrDataframe = topWorst4GeRabSrDataframe.fillna(0)\n # Select top 10 results\n topWorst4GeRabSrDataframe = topWorst4GeRabSrDataframe.nsmallest(10, 'E-RAB Setup Success Rate (ALL)[%](%)')\n # Shape as a column list of dictionaries (Dash requirement)\n topWorst4GeRabSrColumns = [{'name': i, 'id': i} for i in topWorst4GeRabSrDataframe.columns]\n # Get the same column list, for the Records Tab\n topWorst4GeRabSrRecordColumns = topWorst4GeRabSrColumns.copy()\n topWorst4GeRabSrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst4GeRabSrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n\n topWorst4GDcrDataframe = current4GTopWorstDcrDataframe.filter(items = ['eNodeB Name', 'Cell FDD TDD Indication', 'Cell Name', 'Call Drop Rate (All)[%]', 'Date'])\n topWorst4GDcrDataframe = topWorst4GDcrDataframe.fillna(0)\n topWorst4GDcrDataframe = topWorst4GDcrDataframe.nlargest(10, 'Call Drop Rate (All)[%]')\n topWorst4GDcrColumns = [{'name': i, 'id': i} for i in topWorst4GDcrDataframe.columns]\n topWorst4GDcrRecordColumns = topWorst4GDcrColumns.copy()\n topWorst4GDcrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst4GDcrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n\n topWorst3GCsCssrDataframe = current3GTopWorstDataframe.filter(items = ['RNC Name', 'NodeB Name', 'Cell Name', 'HSDPA CSSR(%)', 'HSUPA CSSR(%)', 'Speech CSSR', 'Date'])\n topWorst3GCsCssrDataframe = topWorst3GCsCssrDataframe.fillna(0)\n topWorst3GCsCssrDataframe = topWorst3GCsCssrDataframe.nsmallest(10, 'Speech CSSR')\n topWorst3GCsCssrColumns = [{'name': i, 'id': i} for i in topWorst3GCsCssrDataframe.columns]\n topWorst3GCsCssrRecordColumns = topWorst3GCsCssrColumns.copy()\n topWorst3GCsCssrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst3GCsCssrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n \n topWorst3GPsCssrDataframe = current3GTopWorstDataframe.filter(items = ['RNC Name', 'NodeB Name', 'Cell Name', 'HSDPA CSSR(%)', 'HSUPA CSSR(%)', 'Total Fails', 'Date'])\n topWorst3GPsCssrDataframe = topWorst3GPsCssrDataframe.fillna(0)\n topWorst3GPsCssrDataframe = topWorst3GPsCssrDataframe.nlargest(10, 'Total Fails')\n topWorst3GPsCssrColumns = [{'name': i, 'id': i} for i in topWorst3GPsCssrDataframe.columns]\n topWorst3GPsCssrRecordColumns = topWorst3GPsCssrColumns.copy()\n topWorst3GPsCssrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst3GPsCssrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n \n topWorst3GCsDcrDataframe = current3GTopWorstDataframe.filter(items=['RNC Name', 'NodeB Name', 'Cell Name', 'Speech DCR(%)', 'HSDPA DCR(%)', 'HSUPA DCR(%)', 'Drops CS', 'Date'])\n topWorst3GCsDcrDataframe = topWorst3GCsDcrDataframe.fillna(0)\n topWorst3GCsDcrDataframe = topWorst3GCsDcrDataframe.nlargest(10, 'Drops CS')\n topWorst3GCsDcrColumns = [{'name': i, 'id': i} for i in topWorst3GCsDcrDataframe.columns]\n topWorst3GCsDcrRecordColumns = topWorst3GCsDcrColumns.copy()\n topWorst3GCsDcrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst3GCsDcrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n \n topWorst3GPsDcrDataframe = current3GTopWorstDataframe.filter(items=['RNC Name', 'NodeB Name', 'Cell Name', 'Speech DCR(%)', 'HSDPA DCR(%)', 'HSUPA DCR(%)', 'Drops PS', 'Date'])\n topWorst3GPsDcrDataframe = topWorst3GPsDcrDataframe.fillna(0)\n topWorst3GPsDcrDataframe = topWorst3GPsDcrDataframe.nlargest(10, 'Drops PS')\n topWorst3GPsDcrColumns = [{'name': i, 'id': i} for i in topWorst3GPsDcrDataframe.columns]\n topWorst3GPsDcrRecordColumns = topWorst3GPsDcrColumns.copy()\n topWorst3GPsDcrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst3GPsDcrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n \n topWorst2GSpeechCssrDataframe = current2GTopWorstCssrDataframe.filter(items = ['GBSC', 'Site Name', 'Cell Name', 'Call Setup Success Rate – Speech (%)', 'Date'])\n topWorst2GSpeechCssrDataframe = topWorst2GSpeechCssrDataframe.fillna(0)\n topWorst2GSpeechCssrDataframe = topWorst2GSpeechCssrDataframe.nsmallest(10, 'Call Setup Success Rate – Speech (%)')\n topWorst2GSpeechCssrColumns = [{'name': i, 'id': i} for i in topWorst2GSpeechCssrDataframe.columns]\n topWorst2GSpeechCssrRecordColumns = topWorst2GSpeechCssrColumns.copy()\n topWorst2GSpeechCssrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst2GSpeechCssrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n \n topWorst2GSpeechDcrDataframe = current2GTopWorstDcrDataframe.filter(items = ['GBSC', 'Site Name', 'Cell Name', 'Drop Call Rate – Speech (%)', 'Total Number of dropped Connections', 'Date'])\n topWorst2GSpeechDcrDataframe = topWorst2GSpeechDcrDataframe.fillna(0)\n topWorst2GSpeechDcrDataframe = topWorst2GSpeechDcrDataframe.nlargest(10, 'Total Number of dropped Connections')\n topWorst2GSpeechDcrColumns = [{'name': i, 'id': i} for i in topWorst2GSpeechDcrDataframe.columns]\n topWorst2GSpeechDcrRecordColumns = topWorst2GSpeechDcrColumns.copy()\n topWorst2GSpeechDcrRecordColumns.append({'name': 'TTK', 'id':'TTK'})\n topWorst2GSpeechDcrRecordColumns.append({'name': 'Responsable', 'id':'Responsable'})\n return topWorst4GeRabSrColumns, topWorst4GeRabSrDataframe.to_dict('records'), topWorst4GeRabSrRecordColumns, topWorst4GDcrColumns, topWorst4GDcrDataframe.to_dict('records'), topWorst4GDcrRecordColumns, topWorst3GPsCssrColumns, topWorst3GPsCssrDataframe.to_dict('records'), topWorst3GPsCssrRecordColumns, topWorst3GCsCssrColumns, topWorst3GCsCssrDataframe.to_dict('records'), topWorst3GCsCssrRecordColumns, topWorst3GPsDcrColumns, topWorst3GPsDcrDataframe.to_dict('records'), topWorst3GPsDcrRecordColumns, topWorst3GCsDcrColumns, topWorst3GCsDcrDataframe.to_dict('records'), topWorst3GCsDcrRecordColumns, topWorst2GSpeechCssrColumns, topWorst2GSpeechCssrDataframe.to_dict('records'), topWorst2GSpeechCssrRecordColumns, topWorst2GSpeechDcrColumns, topWorst2GSpeechDcrDataframe.to_dict('records'), topWorst2GSpeechDcrRecordColumns\n else:\n raise PreventUpdate\n\n# Callback to update Zero Traffic Tab\n@app.callback(\n [\n Output('zeroTraffic4GDl', 'columns'),\n Output('zeroTraffic4GDl', 'data'),\n Output('zeroTraffic4GUl', 'columns'),\n Output('zeroTraffic4GUl', 'data'),\n Output('zeroTraffic3GVoice', 'columns'),\n Output('zeroTraffic3GVoice', 'data'),\n Output('zeroTraffic3GHsdpa', 'columns'),\n Output('zeroTraffic3GHsdpa', 'data'),\n Output('zeroTraffic3GHsupa', 'columns'),\n Output('zeroTraffic3GHsupa', 'data'),\n Output('zeroTraffic2G', 'columns'),\n Output('zeroTraffic2G', 'data'),\n ], \n Input('innerTopWorstTabContainer', 'value')\n)\ndef updateZeroTrafficTab(selectedTab):\n if selectedTab == 'Zero Traffic':\n # Get Zero Traffic directory file list\n zeroTrafficDirList = ran_functions.getFtpPathFileList(ftpLogin, zeroTrafficFilePath)\n currentZeroTrafficFile = \"\"\n CurrentDate = str(datetime.now().strftime('%Y%m%d'))\n # Search for the current date on each filename on the directory\n for file in zeroTrafficDirList:\n if CurrentDate in file:\n currentZeroTrafficFile = file\n # Open all file tabs on dataframes\n zeroTraffic4GDlDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic LTE DL', na_values='NIL')\n zeroTraffic4GDlDataframe = zeroTraffic4GDlDataframe.filter(items = ['eNodeB Name', 'Cell Name', 'LocalCell Id', 'Date'])\n zeroTraffic4GDlColumns = [{'name': i, 'id': i} for i in zeroTraffic4GDlDataframe.columns]\n\n zeroTraffic4GUlDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic LTE UL', na_values='NIL')\n zeroTraffic4GUlDataframe = zeroTraffic4GUlDataframe.filter(items = ['eNodeB Name', 'Cell Name', 'LocalCell Id', 'Date'])\n zeroTraffic4GUlColumns = [{'name': i, 'id': i} for i in zeroTraffic4GUlDataframe.columns]\n\n zeroTraffic3GVoiceDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic 3G Voice', na_values='NIL')\n zeroTraffic3GVoiceDataframe = zeroTraffic3GVoiceDataframe.filter(items = ['RNC', 'CELLNAME', 'CellId', 'Date'])\n zeroTraffic3GVoiceColumns = [{'name': i, 'id': i} for i in zeroTraffic3GVoiceDataframe.columns]\n\n zeroTraffic3GHsdpaDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic HSDPA', na_values='NIL')\n zeroTraffic3GHsdpaDataframe = zeroTraffic3GHsdpaDataframe.filter(items = ['RNC', 'CELLNAME', 'CellId', 'Date'])\n zeroTraffic3GHsdpaColumns = [{'name': i, 'id': i} for i in zeroTraffic3GHsdpaDataframe.columns]\n\n zeroTraffic3GHsupaDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic HSUPA', na_values='NIL')\n zeroTraffic3GHsupaDataframe = zeroTraffic3GHsupaDataframe.filter(items = ['RNC', 'CELLNAME', 'CellId', 'Date'])\n zeroTraffic3GHsupaColumns = [{'name': i, 'id': i} for i in zeroTraffic3GHsupaDataframe.columns]\n\n zeroTraffic2GDataframe = pd.read_excel(ran_functions.downloadFtpFile(ftpLogin, zeroTrafficFilePath, currentZeroTrafficFile), sheet_name='Zero Traffic 2G', na_values='NIL')\n zeroTraffic2GDataframe = zeroTraffic2GDataframe.filter(items = ['GBSC', 'Site Name', 'Cell Name', 'Date'])\n zeroTraffic2GColumns = [{'name': i, 'id': i} for i in zeroTraffic2GDataframe.columns]\n\n return zeroTraffic4GDlColumns, zeroTraffic4GDlDataframe.to_dict('records'), zeroTraffic4GUlColumns, zeroTraffic4GUlDataframe.to_dict('records'), zeroTraffic3GVoiceColumns, zeroTraffic3GVoiceDataframe.to_dict('records'), zeroTraffic3GHsdpaColumns, zeroTraffic3GHsdpaDataframe.to_dict('records'), zeroTraffic3GHsupaColumns, zeroTraffic3GHsupaDataframe.to_dict('records'), zeroTraffic2GColumns, zeroTraffic2GDataframe.to_dict('records')\n else:\n raise PreventUpdate\n\n# Callback to add rows on Top Worst Records Tab. This tab's datatable data param must be updated on this callback to avoid callback output duplication.\n@app.callback(\n [\n Output('topWorst4GeRabSrRecordTable', 'data'), \n Output('topWorst4GDcrRecordTable', 'data'),\n Output('topWorst3GPsCssrRecordTable', 'data'),\n Output('topWorst3GCsCssrRecordTable', 'data'),\n Output('topWorst3GPsDcrRecordTable', 'data'),\n Output('topWorst3GCsDcrRecordTable', 'data'),\n Output('topWorst2GSpeechCssrRecordTable', 'data'),\n Output('topWorst2GSpeechDcrRecordTable', 'data')\n ],\n [\n Input('topWorst4GeRabSrRecordTableClicks', 'n_clicks'),\n Input('topWorst4GDcrRecordTableClicks', 'n_clicks'),\n Input('topWorst3GPsCssrRecordTableClicks', 'n_clicks'),\n Input('topWorst3GCsCssrRecordTableClicks', 'n_clicks'),\n Input('topWorst3GPsDcrRecordTableClicks', 'n_clicks'),\n Input('topWorst3GCsDcrRecordTableClicks', 'n_clicks'),\n Input('topWorst2GSpeechCssrRecordTableClicks', 'n_clicks'),\n Input('topWorst2GSpeechDcrRecordTableClicks', 'n_clicks'),\n Input('innerTopWorstTabContainer', 'value')\n ],\n State('topWorst4GeRabSrRecordTable', 'columns'),\n State('topWorst4GDcrRecordTable', 'columns'),\n State('topWorst3GPsCssrRecordTable', 'columns'),\n State('topWorst3GCsCssrRecordTable', 'columns'),\n State('topWorst3GPsDcrRecordTable', 'columns'),\n State('topWorst3GCsDcrRecordTable', 'columns'),\n State('topWorst2GSpeechCssrRecordTable', 'columns'),\n State('topWorst2GSpeechDcrRecordTable', 'columns')\n)\ndef addRow(topWorst4GeRabSrRecordTableClicks, topWorst4GDcrRecordTableClicks, topWorst3GPsCssrRecordTableClicks, topWorst3GCsCssrRecordTableClicks, topWorst3GPsDcrRecordTableClicks, topWorst3GCsDcrRecordTableClicks, topWorst2GSpeechCssrRecordTableClicks, topWorst2GSpeechDcrRecordTableClicks, selectedInnerTab, topWorst4GeRabSrRecordTableColumns, topWorst4GDcrRecordTableColumns, topWorst3GPsCssrRecordTableColumns, topWorst3GCsCssrRecordTableColumns, topWorst3GPsDcrRecordTableColumns, topWorst3GCsDcrRecordTableColumns, topWorst2GSpeechCssrRecordTableColumns, topWorst2GSpeechDcrRecordTableColumns):\n if selectedInnerTab == 'Records':\n # Instantiate the callback context, to find the button ID that triggered the callback\n callbackContext = dash.callback_context\n # Get button ID\n button_id = callbackContext.triggered[0]['prop_id'].split('.')[0]\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.recordsDataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n # Fill datatable data with db table content\n table = 'topworst4gerabsrrecord'\n topWorst4GeRabSrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst4GeRabSrRecordTableColumns, table)\n table = 'topworst4gdcrrecord'\n topWorst4GDcrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst4GDcrRecordTableColumns, table)\n table = 'topworst3gpscssrrecord'\n topWorst3GPsCssrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst3GPsCssrRecordTableColumns, table)\n table = 'topworst3gcscssrrecord'\n topWorst3GCsCssrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst3GCsCssrRecordTableColumns, table)\n table = 'topworst3gpsdcrrecord'\n topWorst3GPsDcrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst3GPsDcrRecordTableColumns, table)\n table = 'topworst3gcsdcrrecord'\n topWorst3GCsDcrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst3GCsDcrRecordTableColumns, table)\n table = 'topworst2gcssrrecord'\n topWorst2GSpeechCssrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst2GSpeechCssrRecordTableColumns, table)\n table = 'topworst2gdcrrecord'\n topWorst2GSpeechDcrRecordTableData = ran_functions.queryTopRecords(pointer, topWorst2GSpeechDcrRecordTableColumns, table)\n if button_id == 'topWorst4GeRabSrRecordTableClicks': \n topWorst4GeRabSrRecordTableData.append({column['id']: '' for column in topWorst4GeRabSrRecordTableColumns})\n if button_id == 'topWorst4GDcrRecordTableClicks':\n topWorst4GDcrRecordTableData.append({column['id']: '' for column in topWorst4GDcrRecordTableColumns})\n if button_id == 'topWorst3GPsCssrRecordTableClicks': \n topWorst3GPsCssrRecordTableData.append({column['id']: '' for column in topWorst3GPsCssrRecordTableColumns})\n if button_id == 'topWorst3GCsCssrRecordTableClicks':\n topWorst3GCsCssrRecordTableData.append({column['id']: '' for column in topWorst3GCsCssrRecordTableColumns})\n if button_id == 'topWorst3GPsDcrRecordTableClicks': \n topWorst3GPsDcrRecordTableData.append({column['id']: '' for column in topWorst3GPsDcrRecordTableColumns})\n if button_id == 'topWorst3GCsDcrRecordTableClicks':\n topWorst3GCsDcrRecordTableData.append({column['id']: '' for column in topWorst3GCsDcrRecordTableColumns})\n if button_id == 'topWorst2GSpeechCssrRecordTableClicks': \n topWorst2GSpeechCssrRecordTableData.append({column['id']: '' for column in topWorst2GSpeechCssrRecordTableColumns})\n if button_id == 'topWorst2GSpeechDcrRecordTableClicks':\n topWorst2GSpeechDcrRecordTableData.append({column['id']: '' for column in topWorst2GSpeechDcrRecordTableColumns})\n # Close DB Connection\n pointer.close()\n connectr.close()\n return topWorst4GeRabSrRecordTableData, topWorst4GDcrRecordTableData, topWorst3GPsCssrRecordTableData, topWorst3GCsCssrRecordTableData, topWorst3GPsDcrRecordTableData, topWorst3GCsDcrRecordTableData, topWorst2GSpeechCssrRecordTableData, topWorst2GSpeechDcrRecordTableData\n else:\n raise PreventUpdate\n\n# Callback to insert data to db (Top Worst Report Records)\n@app.callback(\n [\n # The output will be the button style, because a callback MUST have an output\n Output('topWorst4GeRabSrRecordTableSubmit', 'style'), \n Output('topWorst4GDcrRecordTableSubmit', 'style'), \n Output('topWorst3GPsCssrRecordTableSubmit', 'style'), \n Output('topWorst3GCsCssrRecordTableSubmit', 'style'), \n Output('topWorst3GPsDcrRecordTableSubmit', 'style'), \n Output('topWorst3GCsDcrRecordTableSubmit', 'style'), \n Output('topWorst2GSpeechCssrRecordTableSubmit', 'style'), \n Output('topWorst2GSpeechDcrRecordTableSubmit', 'style')\n ],\n [\n # Our triggers will be the submit buttons\n Input('topWorst4GeRabSrRecordTableSubmit', 'n_clicks'),\n Input('topWorst4GDcrRecordTableSubmit', 'n_clicks'),\n Input('topWorst3GPsCssrRecordTableSubmit', 'n_clicks'),\n Input('topWorst3GCsCssrRecordTableSubmit', 'n_clicks'),\n Input('topWorst3GPsDcrRecordTableSubmit', 'n_clicks'),\n Input('topWorst3GCsDcrRecordTableSubmit', 'n_clicks'),\n Input('topWorst2GSpeechCssrRecordTableSubmit', 'n_clicks'),\n Input('topWorst2GSpeechDcrRecordTableSubmit', 'n_clicks')\n ],\n # We must know the state of the datatable data\n State('topWorst4GeRabSrRecordTable', 'data'),\n State('topWorst4GeRabSrRecordTable', 'columns'),\n State('topWorst4GDcrRecordTable', 'data'),\n State('topWorst4GDcrRecordTable', 'columns'),\n State('topWorst3GPsCssrRecordTable', 'data'),\n State('topWorst3GPsCssrRecordTable', 'columns'),\n State('topWorst3GCsCssrRecordTable', 'data'),\n State('topWorst3GCsCssrRecordTable', 'columns'),\n State('topWorst3GPsDcrRecordTable', 'data'),\n State('topWorst3GPsDcrRecordTable', 'columns'),\n State('topWorst3GCsDcrRecordTable', 'data'),\n State('topWorst3GCsDcrRecordTable', 'columns'),\n State('topWorst2GSpeechCssrRecordTable', 'data'),\n State('topWorst2GSpeechCssrRecordTable', 'columns'),\n State('topWorst2GSpeechDcrRecordTable', 'data'),\n State('topWorst2GSpeechDcrRecordTable', 'columns')\n)\ndef insertData(topWorst4GeRabSrRecordTableSubmit, topWorst4GDcrRecordTableSubmit, topWorst3GPsCssrRecordTableSubmit, topWorst3GCsCssrRecordTableSubmit, topWorst3GPsDcrRecordTableSubmit, topWorst3GCsDcrRecordTableSubmit, topWorst2GSpeechCssrRecordTableSubmit, topWorst2GSpeechDcrRecordTableSubmit, topWorst4GeRabSrRecordTableData, topWorst4GeRabSrRecordTableColumns, topWorst4GDcrRecordTableData, topWorst4GDcrRecordTableColumns, topWorst3GPsCssrRecordTableData, topWorst3GPsCssrRecordTableColumns, topWorst3GCsCssrRecordTableData, topWorst3GCsCssrRecordTableColumns, topWorst3GPsDcrRecordTableData, topWorst3GPsDcrRecordTableColumns, topWorst3GCsDcrRecordTableData, topWorst3GCsDcrRecordTableColumns, topWorst2GSpeechCssrRecordTableData, topWorst2GSpeechCssrRecordTableColumns, topWorst2GSpeechDcrRecordTableData, topWorst2GSpeechDcrRecordTableColumns):\n # Instantiate the callback context, to find the button ID that triggered the callback\n callbackContext = dash.callback_context\n # Get button ID\n button_id = callbackContext.triggered[0]['prop_id'].split('.')[0]\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.recordsDataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n if button_id == 'topWorst4GeRabSrRecordTableSubmit':\n table = 'topworst4gerabsrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst4GeRabSrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst4GDcrRecordTableSubmit':\n table = 'topworst4gdcrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst4GDcrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst3GPsCssrRecordTableSubmit':\n table = 'topworst3gpscssrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst3GPsCssrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst3GCsCssrRecordTableSubmit':\n table = 'topworst3gcscssrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst3GCsCssrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst3GPsDcrRecordTableSubmit':\n table = 'topworst3gpsdcrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst3GPsDcrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst3GCsDcrRecordTableSubmit':\n table = 'topworst3gcsdcrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst3GCsDcrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst2GSpeechCssrRecordTableSubmit':\n table = 'topworst2gcssrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst2GSpeechCssrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}, {'backgroundColor': '#e7e7e7'}\n if button_id == 'topWorst2GSpeechDcrRecordTableSubmit':\n table = 'topworst2gdcrrecord'\n ran_functions.insertDataTable(pointer, connectr, table, topWorst2GSpeechDcrRecordTableData)\n # Close DB Connection\n pointer.close()\n connectr.close()\n return {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': '#e7e7e7'}, {'backgroundColor': 'green'}\n raise PreventUpdate\n\n# Callback to update Network Check Tab\n@app.callback(\n [\n Output('gsmCsCssrNetworkWideGraph', 'figure'), \n Output('gsmPsCssrNetworkWideGraph', 'figure'), \n Output('gsmCsDcrNetworkWideGraph', 'figure'),\n Output('umtsCssrNetworkWideGraph', 'figure'),\n Output('hsdpaCssrNetworkWideGraph', 'figure'),\n Output('hsupaCssrNetworkWideGraph', 'figure'),\n Output('umtsDcrNetworkWideGraph', 'figure'),\n Output('hsdpaDcrNetworkWideGraph', 'figure'),\n Output('hsupaDcrNetworkWideGraph', 'figure'),\n Output('lteVolteDcrNetworkWideGraph', 'figure'),\n Output('lteDataDcrNetworkWideGraph', 'figure'),\n Output('lteVolteCssrNetworkWideGraph', 'figure'),\n Output('lteDataCssrNetworkWideGraph', 'figure')\n ],\n [\n Input('tabsContainer', 'value'),\n Input('dataUpateInterval', 'n_intervals')\n ]\n)\ndef updateNetworkCheckTab(selectedTab, currentInterval):\n if selectedTab == 'Network Check': \n # starttime is the current date/time - daysdelta\n startTime = 7\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n # Create plots\n gsmCsCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n gsmPsCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n gsmCsDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n umtsCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n hsdpaCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n hsupaCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n umtsDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n hsdpaDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n hsupaDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n lteVolteDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n lteDataDcr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n lteVolteCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n lteDataCssr = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n # Function to populate graph data\n lteVolteCssr, lteDataCssr, lteVolteDcr, lteDataDcr = ran_functions.populateLteGraphs(pointer, startTime, ranController.lteBandList, lteVolteCssr, lteDataCssr, lteVolteDcr, lteDataDcr)\n # Customize graph layout\n lteDataCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='LTE Data eRAB SSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n lteVolteCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='VoLTE eRAB SSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n lteDataDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='LTE Data DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n lteVolteDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='VoLTE DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n umtsCssr, hsdpaCssr, hsupaCssr, umtsDcr, hsdpaDcr, hsupaDcr = ran_functions.populateUmtsGraphs(pointer, startTime, ranController.rncNameList, umtsCssr, hsdpaCssr, hsupaCssr, umtsDcr, hsdpaDcr, hsupaDcr)\n hsdpaCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='HSDPA CSSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n hsupaCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='HSUPA CSSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n umtsCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='UMTS CSSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n hsdpaDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='HSDPA DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n hsupaDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='HSUPA DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n umtsDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='UMTS DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n gsmCsCssr, gsmPsCssr, gsmCsDcr = ran_functions.populateGsmGraphs(pointer, startTime, ranController.bscNameList, gsmCsCssr, gsmPsCssr, gsmCsDcr)\n gsmCsCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='GSM CS CSSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n gsmPsCssr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='GSM PS CSSR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n gsmCsDcr.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='GSM CS DCR'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n # Close DB connection\n pointer.close()\n connectr.close()\n return gsmCsCssr, gsmPsCssr, gsmCsDcr, umtsCssr, hsdpaCssr, hsupaCssr, umtsDcr, hsdpaDcr, hsupaDcr, lteVolteDcr, lteDataDcr, lteVolteCssr, lteDataCssr\n else:\n raise PreventUpdate\n\n#Callback to update Graph Insight Dropdown\n@app.callback(\n [\n Output('graphInsightGraphType', 'options'),\n Output('graphInsightGraphGroup', 'options')\n ],\n Input('graphInsightRat', 'value')\n)\ndef updateGraphInsightDropdown(selectedRAT):\n startTime = 7\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n #startTimeNetworkWide = (datetime.now()-timedelta(days=startTime)).strftime(\"%Y-%m-%d\")\n typeReturnList = ['None']\n groupReturnList = [{'label':'none', 'value':'none'}]\n ratTypeTable = ''\n tableColumn = ''\n if selectedRAT == 'LTE':\n ratTypeTable = 'ran_report_4g_report_network_wide'\n tableColumn = 'ltecellgroup'\n typeReturnList = [{'label':'LTE Data DCR', 'value':'LTE Data DCR'}, {'label':'LTE Data CSSR', 'value':'LTE Data CSSR'}, {'label':'VoLTE DCR', 'value':'VoLTE DCR'}, {'label':'VoLTE CSSR', 'value':'VoLTE CSSR'}]\n elif selectedRAT == 'UMTS':\n ratTypeTable = 'ran_report_3g_report_network_wide'\n tableColumn = 'rncname'\n typeReturnList = [{'label':'UMTS DCR', 'value':'UMTS DCR'}, {'label':'UMTS CSSR', 'value':'UMTS CSSR'}, {'label':'HSDPA DCR', 'value':'HSDPA DCR'}, {'label':'HSDPA CSSR', 'value':'HSDPA CSSR'}, {'label':'HSUPA DCR', 'value':'HSUPA DCR'}, {'label':'HSUPA CSSR', 'value':'HSUPA CSSR'}]\n else:\n ratTypeTable = 'ran_report_2g_report_network_wide'\n tableColumn = 'gbsc'\n typeReturnList = [{'label':'GSM CS CSSR', 'value':'GSM CS CSSR'}, {'label':'GSM PS CSSR', 'value':'GSM PS CSSR'}, {'label':'GSM CS DCR', 'value':'GSM CS DCR'}]\n # Execute query to get graph group list\n pointer.execute('SELECT ' + tableColumn + ' FROM ran_pf_data.' + ratTypeTable + ' group by ' + tableColumn + ';')\n queryRaw = pointer.fetchall()\n groupReturnList = [{'label':i[0], 'value':i[0]} for i in queryRaw]\n groupReturnList.append({'label':'All', 'value':'All'})\n # Close DB connection\n pointer.close()\n connectr.close()\n return typeReturnList, groupReturnList\n\n# Callback to update Graph Inisight Graph\n@app.callback(\n [\n Output('graphInsightGraph', 'figure'),\n Output('graphInsightTable', 'data')\n ],\n [\n Input('graphInsightGraphType', 'value'),\n Input('graphInsightGraphGroup', 'value')\n ]\n)\ndef updateGraphInsightGraph(selectedKPI, selectedGroup):\n startTime = 7\n graphInsightValueList = []\n graphInsightValueDict = {}\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n currentGraph = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n currentGraph, graphInsightValueDict = ran_functions.graphInsightQuery(currentGraph, startTime, selectedKPI, pointer, selectedGroup, graphInsightValueDict)\n # Set graph visual theme\n currentGraph.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n legend=dict(orientation='h'),\n title=dict(text=selectedKPI),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n graphInsightValueList.append(graphInsightValueDict)\n # Close DB connection\n pointer.close()\n connectr.close()\n return currentGraph, graphInsightValueList\n\n# Callback to update Network Check Tab\n@app.callback(\n [\n Output('umtsNetworkPacketLossGraph', 'figure'), \n Output('umtsNetworkDelayGraph', 'figure'), \n Output('gsmNetworkPacketLossGraph', 'figure'), \n Output('gsmNetworkDelayGraph', 'figure')\n ],\n [\n Input('tabsContainer', 'value'),\n Input('dataUpateInterval', 'n_intervals')\n ]\n)\ndef updateTxCheckTab(selectedTab, currentInterval):\n if selectedTab == 'Tx Status': \n # starttime is the current date/time - daysdelta\n startTime = 7\n # Connect to DB\n connectr = mysql.connector.connect(user = dbPara.dbUsername, password = dbPara.dbPassword, host = dbPara.dbServerIp , database = dbPara.dataTable)\n # Connection must be buffered when executing multiple querys on DB before closing connection.\n pointer = connectr.cursor(buffered=True)\n # Create plots\n umtsNetworkPacketLossGraph = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n umtsNetworkDelayGraph = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n gsmNetworkPacketLossGraph = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n gsmNetworkDelayGraph = make_subplots(rows = 1, cols = 1, shared_xaxes = True, shared_yaxes = True)\n umtsNetworkPacketLossGraph, umtsNetworkDelayGraph, gsmNetworkPacketLossGraph, gsmNetworkDelayGraph = ran_functions.queryTxData(pointer, startTime, ranController.bscNameList, ranController.rncNameList, umtsNetworkPacketLossGraph, umtsNetworkDelayGraph, gsmNetworkPacketLossGraph, gsmNetworkDelayGraph)\n umtsNetworkPacketLossGraph.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='UMTS Network Packet Loss'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n umtsNetworkDelayGraph.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='UMTS Network Delay'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n gsmNetworkPacketLossGraph.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='GSM Network Packet Loss'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n gsmNetworkDelayGraph.update_layout(\n plot_bgcolor=graphColors.plot_bgcolor, \n paper_bgcolor=graphColors.paper_bgcolor, \n font_color=graphColors.font_color, \n margin=dict(l=10, r=10, t=90, b=10),\n #legend=dict(orientation='h'),\n title=dict(text='GSM Network Delay'),\n title_font=dict(size=graphColors.graphTitleFontSize),\n legend_font_size=graphColors.legend_font_size\n )\n # Close DB connection\n pointer.close()\n connectr.close()\n return umtsNetworkPacketLossGraph, umtsNetworkDelayGraph, gsmNetworkPacketLossGraph, gsmNetworkDelayGraph\n else:\n raise PreventUpdate\n\n# Callback to hide/display selected tab\n@app.callback(\n [\n Output('networkOverviewGridContainer', 'style'),\n Output('graphGridContainer', 'style'),\n Output('outerTopWorstReportFlexContainer', 'style'),\n Output('networkCheckGridContainer', 'style'),\n Output('graphInsightFlexContainer', 'style'),\n Output('txCheckGridContainer', 'style')\n ], \n Input('tabsContainer', 'value')\n)\ndef showTabContent(currentTab):\n networkOverview = networkOverviewStyles.networkOverviewGridContainerStyle\n engDashboard = engDashboardStyles.graphGridContainerStyle\n topWorst = dataTableStyles.outerTopWorstReportFlexContainer\n networkCheck = networkCheckStyles.networkCheckGridContainer\n graphInsight = graphInsightStyles.graphInsightFlexContainer\n txCheck = txCheckStyles.txCheckGridContainer\n if currentTab == 'Network Overview':\n networkOverview['display'] = 'grid'\n engDashboard['display'] = 'none'\n topWorst['display'] = 'none'\n networkCheck['display'] = 'none'\n graphInsight['display'] = 'none'\n txCheck['display'] = 'none'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n elif currentTab == 'Engineering Dashboard':\n networkOverview['display'] = 'none'\n engDashboard['display'] = 'grid'\n topWorst['display'] = 'none'\n networkCheck['display'] = 'none'\n graphInsight['display'] = 'none'\n txCheck['display'] = 'none'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n elif currentTab == 'Top Worst Report':\n networkOverview['display'] = 'none'\n engDashboard['display'] = 'none'\n topWorst['display'] = 'flex'\n networkCheck['display'] = 'none'\n graphInsight['display'] = 'none'\n txCheck['display'] = 'none'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n elif currentTab == 'Network Check':\n networkOverview['display'] = 'none'\n engDashboard['display'] = 'none'\n topWorst['display'] = 'none'\n networkCheck['display'] = 'grid'\n graphInsight['display'] = 'none'\n txCheck['display'] = 'none'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n elif currentTab == 'Graph Insight':\n networkOverview['display'] = 'none'\n engDashboard['display'] = 'none'\n topWorst['display'] = 'none'\n networkCheck['display'] = 'none'\n graphInsight['display'] = 'flex'\n txCheck['display'] = 'none'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n else:\n networkOverview['display'] = 'none'\n engDashboard['display'] = 'none'\n topWorst['display'] = 'none'\n networkCheck['display'] = 'none'\n graphInsight['display'] = 'none'\n txCheck['display'] = 'grid'\n return networkOverview, engDashboard, topWorst, networkCheck, graphInsight, txCheck\n\n# Callback to hide/display Top Worst inner tabs\n@app.callback(\n [\n Output('datatableGridContainer', 'style'),\n Output('zeroTrafficGridContainer', 'style'),\n Output('topReportRecordGridContainer', 'style')\n ], \n Input('innerTopWorstTabContainer', 'value')\n)\ndef showTopWorstInnerTabContent(currentTab):\n topWorstDaily = dataTableStyles.datatableGridContainer\n zeroTrafficDaily = dataTableStyles.zeroTrafficGridContainer\n topWorstRecord = dataTableStyles.topWorstRecordGridContainer\n if currentTab == 'Daily Report':\n topWorstDaily['display'] = 'grid'\n zeroTrafficDaily['display'] = 'none'\n topWorstRecord['display'] = 'none'\n return topWorstDaily, zeroTrafficDaily, topWorstRecord\n elif currentTab == 'Zero Traffic':\n topWorstDaily['display'] = 'none'\n zeroTrafficDaily['display'] = 'grid'\n topWorstRecord['display'] = 'none'\n return topWorstDaily, zeroTrafficDaily, topWorstRecord\n else:\n topWorstDaily['display'] = 'none'\n zeroTrafficDaily['display'] = 'none'\n topWorstRecord['display'] = 'grid'\n return topWorstDaily, zeroTrafficDaily, topWorstRecord\n\nif __name__ == '__main__':\n app.run_server(debug=True, host='0.0.0.0', port='5006', dev_tools_silence_routes_logging=False)\n\n","sub_path":"ranEngDashboard.py","file_name":"ranEngDashboard.py","file_ext":"py","file_size_in_byte":99608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"22196234","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. 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,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n\nfrom .core import AbstractXMLRemoteModel\nfrom .. import serializers, errors, compat\nfrom ..compat import Enum\n\n\nclass TaskType(Enum):\n UnknownTask = 'UNKNOWN'\n SQLTask = 'SQL'\n\n\ndef _get_task_type(name):\n try:\n return TaskType(name)\n except ValueError:\n return TaskType.UnknownTask\n\n\nclass Task(AbstractXMLRemoteModel):\n\n __slots__ = 'name', 'comment', 'properties'\n\n _type_indicator = 'type'\n\n name = serializers.XMLNodeField('Name')\n type = serializers.XMLTagField('.', parse_callback=lambda s: _get_task_type(s.upper()))\n comment = serializers.XMLNodeField('Comment')\n properties = serializers.XMLNodePropertiesField('Config', 'Property',\n key_tag='Name', value_tag='Value')\n\n def __new__(cls, *args, **kwargs):\n typo = kwargs.get('type')\n\n if typo is not None:\n task_cls = globals().get(typo.name, cls)\n else:\n task_cls = cls\n\n return object.__new__(task_cls)\n\n def set_property(self, key, value):\n if self.properties is None:\n self.properties = compat.OrderedDict()\n self.properties[key] = value\n\n def serialize(self):\n if self.type == TaskType.UnknownTask:\n raise errors.OdpsError('Unknown task type')\n return super(Task, self).serialize()\n\n\ndef format_cdata(query):\n stripped_query = query.strip()\n if not stripped_query.endswith(';'):\n stripped_query += ';'\n return '' % stripped_query\n\n\nclass SQLTask(Task):\n __slots__ = '_anonymous_sql_task_name',\n\n _root = 'SQL'\n _anonymous_sql_task_name = 'AnonymousSQLTask'\n\n query = serializers.XMLNodeField('Query', serialize_callback=format_cdata)\n\n def __init__(self, **kwargs):\n if 'name' not in kwargs:\n kwargs['name'] = SQLTask._anonymous_sql_task_name\n super(SQLTask, self).__init__(**kwargs)\n\n def serial(self):\n if self.properties is None:\n self.properties = compat.OrderedDict()\n\n key = 'settings'\n if key not in self.properties:\n self.properties[key] = '{\"odps.sql.udf.strict.mode\": \"true\"}'\n\n return super(SQLTask, self).serial()\n\ntry:\n from ..internal.models.tasks import *\nexcept ImportError:\n pass\n","sub_path":"odps/models/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648445934","text":"'''\nKevin Chen, James Marlin, Quoc Nguyen\n'''\n\nimport os\nimport json\n\ndef get_animes_imagepaths():\n '''\n Creates a dictionary where keys are\n the names of animes and values are\n the paths to their pictures. \n '''\n anime_imagespaths_dictionary = {}\n for images_root, letter_dir, images_files in os.walk('static/images/'):\n for anime in letter_dir:\n path = os.path.join(images_root, anime)\n for anime_folder_root, anime_folder_directories, anime_folder_files in os.walk(path):\n for current_file in anime_folder_files:\n anime_title = anime_folder_root.split('/')[-1]\n anime_imagespaths_dictionary[anime_title] = os.path.join(anime_folder_root, current_file) \n print(anime_title)\n return anime_imagespaths_dictionary\n\ndef put_dictionary_as_json():\n dictionary = get_animes_imagepaths()\n json_file = open('animes_imagepaths2.json', 'w')\n json_file.write(json.dumps(dictionary))\n\n#get_animes_imagepaths()\nput_dictionary_as_json()\n\n","sub_path":"image_related_functions.py","file_name":"image_related_functions.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"566220684","text":"import requests\nfrom bs4 import BeautifulSoup as BS\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.options import Options\nimport sqlite3\nfrom selenium.webdriver.common.action_chains import ActionChains\n\noptions = Options()\noptions.headless = False\nsoccer_url = 'https://www.oddsportal.com/results/#soccer'\nbookmaker_url = 'https://www.oddsportal.com/bookmakers/'\nimport time\n\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:71.0) Gecko/20100101 Firefox/71.0'\n}\ndb = 'oddsportal.db'\n\ndef main():\n browser = webdriver.Firefox(options=options)\n r = requests.get(soccer_url, headers=headers)\n html = BS(r.content, 'html.parser')\n body = html.select('table.table-main.sport')\n ligs = body[0].select('td')\n for lig in ligs:\n if len(lig.select('a'))>0:\n href_liga = lig.select('a')[0]['href']\n if href_liga.split('/')[1] == 'soccer':\n liga_request_allyears = requests.get('https://www.oddsportal.com' + href_liga, headers=headers)\n soup_liga = BS(liga_request_allyears.content, 'html.parser')\n years_menu = soup_liga.select('.main-menu2.main-menu-gray')\n years_pages = years_menu[0].select('a')\n browser.get('https://www.oddsportal.com' + years_pages[0]['href'])\n content_browser = browser.page_source\n soup_liga = BS(content_browser, 'html.parser')\n breadcrump = soup_liga.select('#breadcrumb')\n breadcrump_a = breadcrump[0].select('a')\n liga = breadcrump_a[3].text\n for page in years_pages:\n print(page['href'])\n browser.get('https://www.oddsportal.com' + page['href'])\n content_browser = browser.page_source\n soup_liga = BS(content_browser, 'html.parser')\n if len(soup_liga.select('#pagination')) == 0:\n matches = soup_liga.select('td.name.table-participant')\n else:\n matches = soup_liga.select('td.name.table-participant')\n max_page = soup_liga.select('#pagination')[0].select('a')[-1]['x-page']\n p = 2\n while p != int(max_page):\n browser.get('https://www.oddsportal.com' + page['href'] + '#/page/%s/' % str(p))\n content_browser = browser.page_source\n soup_liga = BS(content_browser, 'html.parser')\n matches += soup_liga.select('td.name.table-participant')\n p += 1\n for match in matches:\n match_url ='https://www.oddsportal.com' + match.select('a')[0]['href']\n print(match_url)\n browser.get(match_url)\n content_match = browser.page_source\n soup_liga = BS(content_match, 'html.parser')\n col_content = soup_liga.select('#col-content')\n name = col_content[0].select('h1')[0].text\n print(name)\n date = col_content[0].select('p.date')[0].text\n print(date)\n try:\n result = col_content[0].select('p.result')[0].text\n except IndexError:\n result = 'Canceled'\n print(result)\n breadcrump = soup_liga.select('#breadcrumb')\n breadcrump_a = breadcrump[0].select('a')\n sport = breadcrump_a[1].text\n country = breadcrump_a[2].text\n print(liga)\n print(sport)\n print(country)\n data_parsing = [name, match_url, date, result, sport, country, liga]\n if not add_game_in_db(data_parsing):\n continue\n table_odds = soup_liga.select('table.table-main.detail-odds')\n bets = table_odds[0].select('tr.lo')\n bets_dict = {}\n\n right_odds = table_odds[0].select('td.right.odds')\n right_odds_browser = browser.find_elements_by_css_selector('td.right.odds')\n\n for bet in bets:\n bookmaker = bet.select('a.name')[0].text\n print(bookmaker)\n right_odds_bet = bet.select('td.right.odds')\n odds_list = []\n for odd in right_odds_bet:\n try:\n if odd.select('div')[0]['onmouseout'] == \"delayHideTip()\":\n index = right_odds.index(odd)\n hov = ActionChains(browser).move_to_element(right_odds_browser[index])\n hov.perform()\n content_bet = browser.page_source\n soup = BS(content_bet, 'html.parser')\n help_box = soup.select('span.help')[0].text\n open_odds = help_box.split(' ')[-1]\n odds_list.append(open_odds)\n print(open_odds)\n except KeyError:\n open_odds = odd.select('div')[0].text\n odds_list.append(open_odds)\n print(open_odds)\n bets_dict[bookmaker] = odds_list\n print(bets_dict)\n add_bet_in_db(bets_dict,data_parsing)\n browser.quit()\n\n\n\n\n\ndef parsing_bookmaker():\n r = requests.get(bookmaker_url, headers=headers)\n html = BS(r.content, 'html.parser')\n bookmakers = html.select('a.no-ext') #\n for bookmaker in bookmakers:\n if bookmaker.text:\n add_bookmaker_in_db(bookmaker.text)\n\n\ndef add_game_in_db(data_parsing: list):\n con = sqlite3.connect('oddsportal.db')\n cur = con.cursor()\n query = 'SELECT name,url,date,result,sport,country,liga FROM game'\n cur.execute(query)\n data_game = [[el for el in name] for name in cur.fetchall()]\n if data_parsing in data_game:\n print('[INFO] %s игра уже есть в базе' % data_parsing[0])\n cur.close()\n con.close()\n return False\n else:\n cur.execute('INSERT INTO game (name,url,date,result,sport,country,liga) '\n 'VALUES(?,?,?,?,?,?,?)', data_parsing)\n con.commit()\n print('[INFO] игра %s добавлен в базу' % data_parsing[0])\n cur.close()\n con.close()\n return True\n\ndef add_bet_in_db(bets_dict:dict,data_parsing: list):\n con = sqlite3.connect('oddsportal.db')\n cur = con.cursor()\n query = 'SELECT id,name,url,date,result,sport,country,liga FROM game'\n cur.execute(query)\n data_game_dict = {}\n for game in cur.fetchall():\n data_game_dict[game[0]] = [el for el in game[1:]]\n key_game = None\n for key, item in data_game_dict.items():\n if item == data_parsing:\n key_game = key\n break\n for key, item in bets_dict.items():\n add_bookmaker_in_db(key)\n key_bookmaker = None\n query = 'SELECT * FROM bookmaker'\n cur.execute(query)\n data_bookmakers = [[el for el in bookmaker] for bookmaker in cur.fetchall()]\n for bookmaker in data_bookmakers:\n if bookmaker[1] == key:\n key_bookmaker = bookmaker[0]\n break\n data_out = [key_bookmaker, item[0], item[1], item[2], key_game]\n cur.execute('INSERT INTO bet (bookmaker_id,p1,x,p2,game_id) VALUES(?,?,?,?,?)', data_out)\n con.commit()\n print('[INFO] Ставка добавлена в базу')\n cur.close()\n con.close()\n\n\ndef add_bookmaker_in_db(name: str):\n con = sqlite3.connect('oddsportal.db')\n cur = con.cursor()\n query = 'SELECT * FROM bookmaker'\n cur.execute(query)\n data_name = [name[1] for name in cur.fetchall()]\n if name in data_name:\n print('[INFO] %s букмекер уже есть в базе' % name)\n else:\n cur.execute('INSERT INTO bookmaker (name) VALUES(?)', [name])\n con.commit()\n print('[INFO] Букмекер %s добавлен в базу' % name)\n cur.close()\n con.close()\n\nmain()\n\n\n# # for country in countrys:\n# # if str(country['style']) == 'display: table-row;':\n# # print(country['class'])\n# # print(len(countrys))\n# time.sleep(5)\n# browser.close()\n#main()","sub_path":"data_ repl.py","file_name":"data_ repl.py","file_ext":"py","file_size_in_byte":8975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"10363438","text":"#!/usr/bin/python3\nfrom flask import Flask, render_template\nfrom models import storage\nfrom models.state import State\nfrom models.city import City\napp = Flask(__name__)\n\n\n@app.route('/states')\ndef cities():\n my_list = storage.all(\"State\").values()\n return render_template('9-states.html', my_id=\"no_need\", my_ids=my_list)\n\n\n@app.route('/states/')\ndef city_ids(id):\n my_list = storage.all(\"State\").values()\n for x in my_list:\n if str(x.id) == id:\n found = x\n return render_template('9-states.html', my_id=found, my_ids=x)\n return render_template('9-states.html', my_id=\"None\", my_ids=x)\n\n\n@app.teardown_appcontext\ndef teardown(self):\n storage.close()\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=\"5000\")\n","sub_path":"web_flask/9-states.py","file_name":"9-states.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"541760373","text":"notas = []\r\n\r\ncantidad_notas = int(input('cantidad de notas: '))\r\n\r\nfor n in range(cantidad_notas):\r\n nota = float(input('nota No {}: '.format(n + 1)))\r\n notas.append(nota)\r\n\r\nsuma = 0\r\nfor nota in notas:\r\n suma += nota\r\n\r\npromedio = suma / len(notas)\r\nprint('tu promedio es {}'.format(promedio))\r\n\r\nif promedio < 11:\r\n print('jalaste :(')\r\nelse:\r\n print('aprobaste :)')\r\n","sub_path":"ppy/notas.py","file_name":"notas.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"606800655","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# This script contains only feature engineering and model training from which we obtain the best score.\n\n# In[ ]:\n\n\n#! pip install xgboost\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn import preprocessing\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nimport xgboost as xgb\nimport re\n\nplt.style.use('seaborn')\npd.set_option('display.max_column',100)\n\n\n# In[ ]:\n\n\nTrainData = pd.read_csv('train.csv')\nTestData = pd.read_csv('test.csv')\nSessData = pd.read_csv('sessions.csv')\nSessData.rename(columns={'user_id':'id'},inplace = True)\n\n\n# In[ ]:\n\n\naction_type = pd.pivot_table(SessData, index = ['id'],columns = ['action_type'],values = 'secs_elapsed',aggfunc=len,fill_value=0).reset_index()\naction = pd.pivot_table(SessData, index = ['id'],columns = ['action'],values = 'secs_elapsed',aggfunc=len,fill_value=0).reset_index()\naction_detail = pd.pivot_table(SessData, index = ['id'],columns = ['action_detail'],values = 'secs_elapsed',aggfunc=len,fill_value=0).reset_index()\ndevice_type = pd.pivot_table(SessData, index = ['id'],columns = ['device_type'],values = 'secs_elapsed',aggfunc=len,fill_value=0).reset_index()\n\n\n# In[ ]:\n\n\naction_type['booking_response'] = action_type['booking_response'].apply(lambda x: 1 if x>0 else 0)\naction_type['-unknown-'] = action_type['-unknown-'].apply(lambda x: 1 if x>0 else 0)\n\naction_detail['pending'] = action_detail['pending'].apply(lambda x: 1 if x>0 else 0)\naction_detail['at_checkpoint'] = action_detail['at_checkpoint'].apply(lambda x: 1 if x>0 else 0)\n\n\n# In[ ]:\n\n\nDataCom = pd.concat((TrainData.drop('country_destination',axis=1),TestData),axis=0,ignore_index=True)\ndef set_age_group(x):\n if x < 40:\n return 'Young'\n elif x >=40 and x < 60:\n return 'Middle'\n elif x >= 60 and x <= 125:\n return 'Old'\n else:\n return 'Unknown'\n\n\ndef set_age(x):\n if x>=16 and x<=120:\n return x\n elif x<16:\n return np.nan\n elif x>120:\n if 2015-x>16 and 2015-x<110:\n return 2015-x\n else:\n return np.nan\n\ndef set_categorial_values(x):\n l = ['first_browser','affiliate_provider','first_device_type','affiliate_channel','first_affiliate_tracked','signup_app']\n thresold = [0.00]*10\n \n i = l.index(x)\n l1 = DataCom[x].value_counts(normalize=True)\n l2 = l1[l1>thresold[i]].index.tolist()\n return DataCom[x].apply(lambda x: x if x in l2 else 'diff')\n\ndef feature_engineering(df):\n df['DAC_year'] = np.vstack(df['date_account_created'].astype(str).apply(lambda x: list(map(int, x.split('-')))).values)[:,0]\n df['DAC_month'] = np.vstack(df['date_account_created'].astype(str).apply(lambda x: list(map(int, x.split('-')))).values)[:,1]\n df['DAC_day'] = np.vstack(df['date_account_created'].astype(str).apply(lambda x: list(map(int, x.split('-')))).values)[:,2]\n df['DAC_dayofweek'] = pd.to_datetime(df['date_account_created']).dt.dayofweek\n \n df['TFA_year'] = np.vstack(df.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)[:,0]\n df['TFA_month'] = np.vstack(df.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)[:,1]\n df['TFA_day'] = np.vstack(df.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)[:,2]\n df['TFA_hour'] = np.vstack(df.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)[:,3]\n \n df['DFB_year'] = np.vstack(df['date_first_booking'].fillna(-1).astype(str).apply(lambda x: -1 if x=='-1' else list(map(int,x.split('-')))[0] ))\n df['DFB_month'] = np.vstack(df['date_first_booking'].fillna(-1).astype(str).apply(lambda x: -1 if x=='-1' else list(map(int,x.split('-')))[1] ))\n df['DFB_day'] = np.vstack(df['date_first_booking'].fillna(-1).astype(str).apply(lambda x: -1 if x=='-1' else list(map(int,x.split('-')))[2] ))\n df['DFB_dayofweek'] = pd.to_datetime(df['date_first_booking']).dt.dayofweek\n \n df['lag'] = (pd.to_datetime(df.date_account_created)-pd.to_datetime(df.timestamp_first_active,format='%Y%m%d%H%M%S')).dt.days\n \n df['age'] = df['age'].apply(set_age)\n df['age_group'] = df['age'].apply(set_age_group)\n df['age'] = df['age'].fillna(-1)\n \n \n df['has_booked'] = df['date_first_booking'].fillna(-1).apply(lambda x: 0 if x==-1 else 1)\n df['first_affiliate_tracked'] = df['first_affiliate_tracked'].fillna('unknown')\n \n l = ['first_browser','affiliate_provider','first_device_type','affiliate_channel','first_affiliate_tracked','signup_app']\n for x in l:\n df[x] = set_categorial_values(x)\n \n ohe = ['gender', 'signup_method', 'language', 'affiliate_channel', 'affiliate_provider', \n 'first_affiliate_tracked', 'signup_app', 'first_device_type', 'first_browser','age_group']\n \n for x in ohe:\n combined_data,_ = pd.factorize(df[x],sort=True)\n combined_data = pd.Series(combined_data).astype('int32')\n df[x] = combined_data.values \n \n droplist = ['date_account_created','timestamp_first_active','date_first_booking','signup_method']\n df = df.drop(droplist,axis=1)\n \n df = pd.merge(df, action_type[['id','booking_response','-unknown-']], how='left', on='id')\n df = pd.merge(df, action[['id','requested', 'confirm_email', 'update', 'cancellation_policies']], how='left', on='id')\n df = pd.merge(df, action_detail[['id','pending', 'at_checkpoint']], how='left', on='id')\n \n df = df.set_index('id')\n \n return df\n\n\nDataCom = feature_engineering(DataCom)\n\n\n# In[ ]:\n\n\ny = TrainData['country_destination'] \nlabels = y.values\nle = LabelEncoder()\ny = pd.Series(le.fit_transform(labels)) \n\nX = DataCom[:TrainData.shape[0]] # encoded train data x \ndf_test = DataCom[TrainData.shape[0]:] # encoded test data\n\nx_train, x_test, y_train, y_test = train_test_split(X, y,random_state=31, train_size=0.80, stratify=y)\n\n\n# In[ ]:\n\n\nxgb3 = xgb.XGBClassifier(max_depth=4, learning_rate=0.01, n_estimators=75,\n objective='multi:softprob', subsample=0.6, colsample_bytree=0.6, seed=40,reg_lambda=0.5)\nxgb3.fit(X, y)\n\n\n# In[ ]:\n\n\nprob = xgb3.predict_proba(df_test)\nid_test = TestData['id']\n\nid_final_sub = [] \npred_final_sub = [] \nfor i in range(len(id_test)):\n idx = id_test[i]\n id_final_sub += [idx] * 3\n pred_final_sub += le.inverse_transform(np.argsort(prob[i])[::-1])[:3].tolist()\n\nsub = pd.DataFrame(np.column_stack((id_final_sub,pred_final_sub)), columns=['id', 'country_destination'])\nsub.to_csv('submission.csv',index=False)\n\n","sub_path":"Final Code/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":6876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"98938688","text":"from manage import *\nfrom models import *\nimport query\n\nclass OverviewHandler(AdminBaseHandler):\n\n def get(self):\n config = ndb.Key(Settings, 'config').get()\n if not config:\n config = Settings(id='config')\n due_date = config.due_date\n\n applicants, applications = query.get_all_overview()\n template_values = {\n 'applicants': applicants,\n 'applications': applications,\n 'admin_url': '/admin/overview',\n 'DUE_DATE': due_date\n }\n self.render_template('admin-overview.html', template_values)\n","sub_path":"manage/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"69634179","text":"'''\nDesafio 056\n\nDesenvolva um programa que leia o nome, idade e\nsexo de 4 pessoas. No final do programa, mostre:\n- A média de idade do grupo.\n- Qual é o nome do homem mais velho.\n- Quantas mulheres tem menos de 20 anos.\n'''\nsoma_idade = 0\nmaior_idade_homem = 0\nqtd_mulheres_menor_20 = 0\n\nfor i in range(1, 5):\n print(f'----- {i}ª PESSOA -----')\n nome = str(input('Nome: ')).strip()\n idade = int(input('Idade: '))\n sexo = str(input('Sexo [M/F]: ')).strip()\n\n # Soma das idades\n soma_idade += idade\n\n # Homem mais velho e seu nome\n if sexo in 'Mm' and idade > maior_idade_homem:\n maior_idade_homem = idade\n nome_homem_mais_velho = nome\n\n # Mulher com menos de 20 anos\n if sexo in \"Ff\" and idade < 20:\n qtd_mulheres_menor_20 += 1\n\n\nmedia_idade = soma_idade / 4\nprint(f'A média de idade do grupo é de {media_idade} anos')\nprint(f'O homem mais velho tem {maior_idade_homem} anos e chama-se {nome_homem_mais_velho}')\nprint(f'Ao todo são {qtd_mulheres_menor_20} mulheres com menos de 20 anos')\n","sub_path":"Curso em Video/Aula 13 - Estruturas de Repetição/Desafio056.py","file_name":"Desafio056.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"607852802","text":"############################################################################################\n############################# MAIN CODE FOR STUDENT LOANS PROJECT ##########################\n############################# MODEL WITH ONTHEJOB-S + mrisk ##########################\n############################################################################################\n\n############################################################################################\n############################# MAIN CODE FOR STUDENT LOANS PROJECT ##########################\n############################# MODEL WITH SEARCH ON THE JOB ##########################\n############################################################################################\n\n\nimport numpy as np\n#import sys\n#import os\n#import math\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n#import numpy.matlib\nfrom scipy.optimize import fsolve\n#from scipy.optimize import brentq\n#from scipy.interpolate import griddata\n#from scipy.interpolate import CubicSpline\nfrom scipy.optimize import root\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom scipy import interpolate\nfrom scipy.interpolate import interpn\nfrom scipy.interpolate import griddata\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared, ConstantKernel as C\nimport warnings\n#from numpy import linalg as LA\nfrom numba import jit\nimport random\nimport pyipopt\nimport quantecon as qe\n\n\nfrom params import *\nfrom declare_arrays import *\n\n\n############################################################################################\n############################################################################################\n\n############################ DECLARATIONS : GENERAL USE FUNCTIONS ############################\n\ndef m_fun(theta):\n return 1 - np.exp(-eta * theta)\n\ndef m_funprime(theta):\n return eta * np.exp(-eta * theta)\n\ndef q_fun(theta):\n if (theta==0.0):\n return 1.0\n else:\n return (1 - np.exp(-eta * theta))/theta\n\ndef q_funprime(theta):\n if (theta==0.0):\n return 1.0\n else:\n return -np.exp(-eta * theta)*(np.exp(eta * theta)-eta*theta-1)/theta**2\n # -(1 / (theta ** 2)) + ((1+eta*theta) * np.exp(-eta * theta))/(theta**2)\n\ndef u(c):\n if nu == 1:\n return np.log(c)\n else:\n return (c ** (1 - nu)) / (1 - nu)\n\ndef uprime_inv(c):\n return c ** (-1 / nu)\n\ndef u_prime(c):\n return c ** (-nu)\n\ndef k_fun(y):\n return kappa*(y**gamma)/gamma\n\ndef k_funprime(y):\n return kappa*(y**(gamma-1))\n\ndef c_u(a, a_prime):\n return b + R * a - a_prime\n\ndef c_e(w, a, a_prime):\n return w + R * a - a_prime\n\ndef l_fun(dis):\n if np.abs(dis):\n dis = dis/np.abs(dis)\n return 1/(1+np.exp(-2*100*dis))\n\ndef l_fun_prime(dis):\n if np.abs(dis):\n dis = dis/np.abs(dis)\n return 2*100*np.exp(2*100*dis)/(1+np.exp(2*100*dis))**2\n\ndef g_fun(p, x):\n y = p*adj\n return x + phi*(y-x)*l_fun(y-x)\n\ndef g_fun_prime(p, x):\n y = p*adj\n return phi*l_fun(y-x) + phi*(y-x)*l_fun_prime(y-x)\n\ndef t_fun(p, x):\n y = p*adj\n if (y sitk.Image:\n pass\n\n\nclass ComposeTransform(Transform):\n\n def __init__(self, transforms) -> None:\n super().__init__()\n self.transforms = transforms\n\n def __call__(self, img: sitk.Image) -> sitk.Image:\n for transform in self.transforms:\n img = transform(img)\n return img\n\n\nclass RescaleIntensity(Transform):\n\n def __init__(self, min=0, max=65535) -> None:\n super().__init__()\n self.min = min\n self.max = max\n\n def __call__(self, img: sitk.Image) -> sitk.Image:\n return sitk.RescaleIntensity(img, self.min, self.max)\n\n\nclass Resample(Transform):\n\n def __init__(self, new_spacing: tuple) -> None:\n super().__init__()\n self.new_spacing = new_spacing\n\n def __call__(self, img: sitk.Image) -> sitk.Image:\n size, spacing, origin, direction = img.GetSize(), img.GetSpacing(), img.GetOrigin(), img.GetDirection()\n\n scale = [ns / s for ns, s in zip(self.new_spacing, spacing)]\n new_size = [int(sz/sc) for sz, sc in zip(size, scale)]\n # new_origin = [o / sc for o, sc in zip(origin, scale)]\n\n resampler = sitk.ResampleImageFilter()\n resampler.SetSize(new_size)\n # resampler.SetTransform(sitk.Transform())\n resampler.SetInterpolator(sitk.sitkLinear)\n resampler.SetOutputDirection(direction)\n # resampler.SetOutputOrigin(new_origin) # misfitted image when using adapted origin\n resampler.SetOutputOrigin(origin)\n resampler.SetOutputSpacing(self.new_spacing)\n\n return resampler.Execute(img)\n\n\nclass MergeLabel(Transform):\n\n def __init__(self, to_combine: dict) -> None:\n super().__init__()\n # to_combine is a dict with keys -> new label and values -> list of labels to merge\n self.to_combine = to_combine\n\n def __call__(self, img: sitk.Image) -> sitk.Image:\n np_img = sitk.GetArrayFromImage(img)\n merged_img = np.zeros_like(np_img)\n\n for new_label, labels_to_merge in self.to_combine.items():\n merged_img[np.in1d(np_img.ravel(), labels_to_merge, assume_unique=True).reshape(np_img.shape)] = new_label\n\n out_img = sitk.GetImageFromArray(merged_img)\n out_img.CopyInformation(img)\n return out_img\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='data preparation for the MIALab')\n parser.add_argument(\n '--data_dir',\n type=str,\n required=True,\n help='the path to the dataset'\n )\n\n args = parser.parse_args()\n main(args.data_dir)\n","sub_path":"bin/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":8760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"421269739","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport datetime\nimport khayyam\nfrom base_app.classes.debug import Debug\nfrom base_app.models.mongodb.base_model import MongodbModel, BaseModel\nfrom base_app.models.mongodb.user.general_info.general_info import UserModel\n\n__author__ = 'Morteza'\n\n\nclass NewsReportBrokenModel(BaseModel):\n def __init__(self, _id=None, news=None, title=None, user=None, text=None, link=None):\n BaseModel.__init__(self)\n self.id = _id\n self.news = news\n self.user = user\n self.text = text\n self.title = title\n self.link = link\n self.result = {'value': {}, 'status': False}\n\n def save(self):\n try:\n __body = {\n 'news': self.news,\n 'user': self.user,\n 'text': self.text,\n 'title': self.title,\n 'link': self.link,\n 'date': datetime.datetime.now(),\n }\n self.result['value'] = MongodbModel(collection='news_report_broken', body=__body).insert()\n self.result['status'] = True\n return self.result\n except:\n Debug.get_exception(sub_system='admin', severity='error', tags='mongodb > save', data='collection > subject')\n return self.result\n\n def get_all(self, _page=1, _size=20):\n try:\n __body = {}\n r = MongodbModel(collection='news_report_broken', body=__body, page=_page, size=_size).get_all_pagination()\n ls = []\n for i in r:\n i['date'] = khayyam.JalaliDatetime(i['date']).strftime('%Y/%m/%d %H:%M:%S')\n try:\n i['full_name'] = UserModel(_id=i['user']).get_one()['value']['full_name']\n except:\n i['full_name'] = ''\n ls.append(i)\n self.result['value'] = ls\n self.result['status'] = True\n return self.result\n except:\n Debug.get_exception(sub_system='admin', severity='error', tags='mongodb > get_all', data='collection > news_report_broken')\n return self.result\n\n @staticmethod\n def get_count_all():\n try:\n __body = {}\n r = MongodbModel(collection='news_report_broken', body=__body).count()\n if r:\n return r\n return 0\n except:\n Debug.get_exception(sub_system='admin', severity='error', tags='mongodb > get_all', data='collection > news_report_broken')\n return 0\n\n def delete(self):\n try:\n __body = {\"_id\": self.id}\n r = MongodbModel(collection='news_report_broken', body=__body).delete()\n self.result['value'] = r\n self.result['status'] = True\n except:\n Debug.get_exception(sub_system='admin', severity='error', tags='mongodb > get_all', data='collection > news_report_broken')\n return self.result","sub_path":"base_app/models/mongodb/news_report_broken/news_report_broken.py","file_name":"news_report_broken.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"612471075","text":"#!/usr/bin/python\nimport roslib\nimport rospy\nimport math as m\nfrom matplotlib.pyplot import *\nfrom geometry_msgs.msg import PoseStamped\nfrom geometry_msgs.msg import WrenchStamped\nfrom geometry_msgs.msg import Quaternion\nfrom std_msgs.msg import Float32\nimport numpy as np\nimport kalman as K\nimport tf\n\npub_r = rospy.Publisher(\"/direction/right_arm_estimate\", PoseStamped)\npub_r_t = rospy.Publisher(\"/direction/right_arm\", PoseStamped)\npub_l = rospy.Publisher(\"/direction/left_arm\", PoseStamped)\npub_theta_hat = rospy.Publisher(\"/theta_estimate\", Float32)\npub_mag_force = rospy.Publisher(\"/mag_force\", Float32)\n\nR_wrench = WrenchStamped()\nL_wrench = WrenchStamped()\nR_dir = PoseStamped()\nR_dir_t = PoseStamped()\nL_dir = PoseStamped()\ntheta_pub = Float32()\nmag_force = Float32()\nsave_arrays = True\n\n#--- Arrays to store values\nTHETA = np.array([])\nTHETA_2q = np.array([])\nTHETA_hat = np.array([])\nmag_FORCE = np.array([])\nKK_gain = np.array([])\nPP = np.array([])\nFx = np.array([])\nFy = np.array([])\nFz = np.array([])\nTx = np.array([])\nTy = np.array([])\nTz = np.array([])\n\n\n#--- Kalman Filter initial values\nxhat_0 = 1.57 # start estimate at pi/2 for theta\nP_0 = 1\nfirst_estimate = 1\nxhat = 0\nP = 0\nK_gain = 0\ntheta_euler = 0\nestimation_started = 0\nprint_once = 0\n\ndef r_direction_callback(msg):\n global first_estimate, xhat, P, theta_euler, K_gain, estimation_started, print_once\n global THETA, THETA_hat, THETA_2q, PP, KK_gain, Fx, Fy, Fz, Tx, Ty, Tz, mag_FORCE\n R_wrench = msg\n N_force = m.sqrt((m.pow(R_wrench.wrench.force.x, 2) + m.pow(R_wrench.wrench.force.z, 2)))\n \n if save_arrays == True:\n # --- Collect Fx, Fy and Fz to plot later --\n Fx = np.append(Fx, R_wrench.wrench.force.x)\n Fy = np.append(Fy, R_wrench.wrench.force.y)\n Fz = np.append(Fz, R_wrench.wrench.force.z)\n Tx = np.append(Tx, R_wrench.wrench.torque.x)\n Ty = np.append(Ty, R_wrench.wrench.torque.y)\n Tz = np.append(Tz, R_wrench.wrench.torque.z) \n mag_FORCE = np.append(mag_FORCE, N_force)\n # -----------------------------------------\n\n # --- Create new frame for publishing theta -----\n br_r = tf.TransformBroadcaster()\n br_r.sendTransform((0, 0, 0),\n tf.transformations.quaternion_from_euler(0.0, 1.57, 0.0),\n rospy.Time.now(),\n \"ft_transform_r\",\n \"r_gripper_motor_accelerometer_link\")\n # ----------------------------------------------\n \n if abs(R_wrench.wrench.torque.x) > 0.2 or estimation_started == 1:\n if estimation_started == 0:\n print(\"Someone started to hold the object!\")\n estimation_started = 1\n\n \n #theta = m.acos(R_wrench.wrench.force.x / N_force)\n # Fx_n = R_wrench.wrench.force.x/N_force\n # Fy_n = R_wrench.wrench.force.y/N_force\n # theta_n = m.acos(Fx_n)\n theta_r = m.atan2(R_wrench.wrench.force.z, R_wrench.wrench.force.x)\n if theta_r < 0:\n theta_r_2q = theta_r + m.pi\n else:\n theta_r_2q = theta_r\n if save_arrays == True:\n THETA = np.append(THETA, theta_r)\n THETA_2q = np.append(THETA_2q, theta_r_2q)\n\n # --- Theta_hat estimation with Kalman Filter ---\n if first_estimate == 1:\n (xhat, P, K_gain) = K.Kalman_Filter(theta_r_2q, xhat_0, P_0)\n first_estimate = 0\n \n else:\n (xhat, P, K_gain) = K.Kalman_Filter(theta_r_2q, xhat, P)\n if save_arrays == True:\n KK_gain = np.append(KK_gain, K_gain)\n PP = np.append(PP, P)\n THETA_hat = np.append(THETA_hat, xhat)\n # --- Publish Theta, mag_force for change_detection node ---\n theta_pub = xhat\n mag_force = N_force\n pub_theta_hat.publish(theta_pub)\n pub_mag_force.publish(mag_force)\n # ----------------------------------------------\n\n # --- Publish Theta from Force Measurements ---\n # Note: Theta is published negative for visualization\n R_dir.header.frame_id = 'ft_transform_r' \n theta_euler = xhat\n quat = tf.transformations.quaternion_from_euler(0.0, -theta_euler, 0.0)\n R_dir.pose.orientation.x = quat[0]\n R_dir.pose.orientation.y = quat[1]\n R_dir.pose.orientation.z = quat[2]\n R_dir.pose.orientation.w = quat[3]\n pub_r.publish(R_dir)\n # ---------------------------------------------\n\n # --- Publish estimated Theta -----------------\n # Note: Theta is published negative for visualization\n R_dir_t.header.frame_id = 'ft_transform_r' \n quat = tf.transformations.quaternion_from_euler(0.0, -theta_r, 0.0)\n R_dir_t.pose.orientation.x = quat[0]\n R_dir_t.pose.orientation.y = quat[1]\n R_dir_t.pose.orientation.z = quat[2]\n R_dir_t.pose.orientation.w = quat[3]\n pub_r_t.publish(R_dir_t) \n # ---------------------------------------------\n else:\n if estimation_started == 0:\n if print_once == 0:\n print(\"No one is holding the object\")\n print_once = 1\n else:\n if print_once == 1:\n print(\"Someone stopped holding the object! \\n Estimation has STOPPED\")\n print_once = 2\n return\n\ndef l_direction_callback(msg):\n L_wrench = msg\n br_l = tf.TransformBroadcaster()\n br_l.sendTransform((0, 0, 0),\n tf.transformations.quaternion_from_euler(0.0, 1.57, 0.0),\n rospy.Time.now(),\n \"ft_transform_l\",\n \"l_gripper_motor_accelerometer_link\")\n theta_l = m.atan2(L_wrench.wrench.force.z, L_wrench.wrench.force.x)\n ## -- 0 deg= 0 rad; 90 = pi/2; 180 = pi; 270 = -pi/2\n## if theta_l > 0:\n## theta_r += m.pi\n## rospy.loginfo(\"Theta: %f\", theta_r)\n L_dir.header.frame_id = 'ft_transform_l'\n quat_l = tf.transformations.quaternion_from_euler(0.0, theta_l, 0.0)\n \n L_dir.pose.orientation.x = quat_l[0]\n L_dir.pose.orientation.y = quat_l[1]\n L_dir.pose.orientation.z = quat_l[2]\n L_dir.pose.orientation.w = quat_l[3]\n \n pub_l.publish(L_dir) \n \n return\n\ndef direction_estimate():\n rospy.init_node('direction_estimate', anonymous=True)\n rospy.Subscriber(\"/ft_transformed/rig_arm\", WrenchStamped, r_direction_callback)\n #rospy.Subscriber(\"/ft_transformed/lef_arm\", WrenchStamped, l_direction_callback)\n\n rospy.spin()\n return\n\n \nif __name__ == '__main__':\n\n direction_estimate()\n\n try:\n rate = rospy.Rate(10.0)\n while not rospy.is_shutdown():\n\n rate.sleep()\n\n except KeyboardInterrupt:\n pass\n if save_arrays == True:\n raw_input(\"Press any key to see plot of theta\")\n X_axis = np.linspace(0,(len(THETA)-1),len(THETA))\n X_axis_F = np.linspace(0,(len(Fx)-1),len(Fx))\n print('len(X_axis)',len(X_axis))\n print('len(X_axis_F)',len(X_axis_F))\n freq = 1000\n # Create the ground truth vectors for plotting\n # theta_r_corner = 2.1\n # theta_l_corner = 1.038\n # gnd_truth = np.ones(18662)\n # gnd_truth[0:9331] = m.pi/2\n # gnd_truth[9331:18662] = theta_r_corner\n # x_gnd = X_axis[0:18662]\n # gnd_truth[18662:30001] = theta_l_corner\n # gnd_truth[30001:-1] = m.pi/2\n #angle1 = np.ones(10*freq) * m.pi/2\n #gnd_truth = np.append(gnd_truth, angle1)\n #angle2 = np.ones(10*freq) * m.pi/4\n \n \n \n # figure(1)\n # plot(x_gnd, THETA_2q[0:18662], 'g'), title('THETA_2q'), ylabel('[rad]')\n # plot(x_gnd, THETA_hat[0:18662], 'r'), title('THETA_hat'), ylabel('[rad]') \n # plot(x_gnd, gnd_truth, '--k'), title('Estimator of direction'), ylabel('[rad]')\n # legend(('Direction from measurements','Result of Kalman Filter, vStd = 20', 'Ground truth'),'lower right')\n \n figure(2)\n # #plot(X_axis, THETA, 'b'), title('THETA'), ylabel('[rad]')\n plot(X_axis, THETA_2q, 'g'), title('THETA_2q'), ylabel('[rad]')\n plot(X_axis, THETA_hat, 'r'), title('THETA_hat'), ylabel('[rad]')\n legend(('THETA_2q', 'Estimated THETA'),'upper right')\n\n figure(3) \n subplot(411)\n plot(X_axis_F, Fx, 'r'), title('Fx'), ylabel('[N]')\n subplot(412)\n plot(X_axis_F, Fy, 'g'), title('Fy'), ylabel('[N]')\n subplot(413)\n plot(X_axis_F, Fz, 'b'), title('Fz'), ylabel('[N]')\n subplot(414)\n plot(X_axis_F, mag_FORCE, 'b'), title('mag_FORCE'), ylabel('[N]')\n\n figure(4) \n subplot(311)\n plot(X_axis_F, Tx, 'r'), title('Tx'), ylabel('[Nm]')\n subplot(312)\n plot(X_axis_F, Ty, 'g'), title('Ty'), ylabel('[Nm]')\n subplot(313)\n plot(X_axis_F, Tz, 'b'), title('Tz'), ylabel('[Nm]')\n\n \n ## plot(X_axis, mag_FORCE, 'r'), title(\"Magnitude of Force\")\n\n # figure(\"Kalman Filter\")\n # subplot(211)\n # plot(X_axis, KK_gain),title(\"Kalman Gain for theta\")\n # subplot(212)\n # plot(X_axis, np.sqrt(PP)),title(\"Covariance for theta\")\n \n show()\n\n","sub_path":"scripts/direction_estimate.py","file_name":"direction_estimate.py","file_ext":"py","file_size_in_byte":9094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"546199259","text":"from __future__ import print_function\n\nimport datetime\nimport sys\nimport threading\nimport time\n\nimport usb1\n\nfrom LikeAG13 import G13Lcd, G13_KEYS, LedColors, MissingG13Error\n\n\nclass TerminalUI(object):\n BASE_X, BASE_Y = 0, 10\n scale_x, scale_y = 64, 32\n\n def __init__(self):\n self.prev_x, self.prev_y = 0, 0\n self.prev_keys = {k: 1 for k in G13_KEYS}\n\n def init_stick(self):\n self.reset()\n sys.stdout.write('-' * (self.scale_x + 2) + '\\n')\n for l in range(self.scale_y):\n sys.stdout.write('|' + ' ' * self.scale_x + '|\\n')\n sys.stdout.write('-' * (self.scale_x + 2) + '\\n')\n\n def reset(self):\n self.goto(0, 0)\n\n def goto(self, x, y):\n sys.stdout.write('\\033[%s;%sH' % (self.BASE_Y + y, self.BASE_X + x))\n\n def down(self, num):\n sys.stdout.write('\\033[%sB' % num)\n\n def right(self, num):\n sys.stdout.write('\\033[%sC' % num)\n\n def print_at(self, x, y, s):\n self.goto(x + 1, y + 1)\n sys.stdout.write(s)\n\n def flush(self):\n sys.stdout.flush()\n\n def print_stick(self, x, y):\n x /= 4\n y /= 8\n\n self.print_at(self.prev_x + 1, self.prev_y, ' ')\n self.print_at(x + 1, y, 'x')\n\n self.prev_x, self.prev_y = x, y\n\n def clear_keys(self):\n if not any(self.prev_keys.values()):\n return\n for i in range(5):\n self.print_at(0, self.scale_y + 1 + i, ' ' * (4 * 8))\n\n def set_key(self, key, value):\n if self.prev_keys[key] != value:\n idx = G13_KEYS.index(key)\n y = self.scale_y + 1 + idx / 8\n x = (idx % 8) * 4\n if value:\n out = key\n else:\n out = ' '\n self.print_at(x, y, out)\n\n self.prev_keys[key] = value\n\n\ndef try_get_g13():\n try:\n g13_instance = G13Lcd()\n return g13_instance\n except MissingG13Error:\n print('No G13 found.')\n sys.exit(1)\n\n\ndef lcd_only_ui():\n g13 = try_get_g13()\n\n g13.draw_image('x.png', scale=0.2, offset=(200, 0))\n\n start = time.time()\n\n try:\n while True: # for i in range(300):\n g13.print_time()\n t = 1 / (time.time() - start)\n time.sleep(1 - (time.time() % 1))\n start = time.time()\n g13.set_led_mode(int(time.time() % 16))\n g13.set_color((255, 0, 0))\n time.sleep(0.1)\n g13.set_led_mode(int(time.time() % 16))\n g13.set_color((255, 255, 255))\n except Exception as e:\n print(e)\n except KeyboardInterrupt:\n print('^C')\n finally:\n g13.close()\n return\n\n\ndef lcd_only_ui_colors():\n g13 = try_get_g13()\n\n g13.draw_image('x.png', scale=0.2, offset=(200, 0))\n\n g13.set_led_mode(16)\n\n try:\n while True: # for i in range(300):\n\n # YELLOW\n g13.print_text('255 0 0')\n g13.set_color_from_rgb(255, 0, 0)\n time.sleep(1)\n\n # WHITE\n g13.print_text('WHITE')\n g13.set_color_from_rgb(180, 180, 180)\n time.sleep(1)\n\n # TEAL\n g13.print_text('0 255 0')\n g13.set_color_from_rgb(0, 255, 0)\n time.sleep(1)\n\n # PINK\n g13.print_text('PINK')\n g13.set_color_from_rgb(200, 200, 200)\n time.sleep(1)\n\n # MAGENTA\n g13.print_text('MAGENTA')\n g13.set_color_from_rgb(40, 30, 200)\n time.sleep(1)\n\n # # LIGHTS OUT\n # g13.print_text('0 0 0')\n # g13.set_color_from_rgb(0, 0, 0)\n # time.sleep(1)\n\n for value0 in range(0, 255, 5):\n for value1 in range(0, 255, 5):\n for value2 in range(0, 255, 5):\n\n g13.print_text('{} {} {}'.format(value0, value1, value2))\n g13.set_color_from_rgb(value0, value1, value2)\n time.sleep(0.1)\n try:\n keypress = g13.get_key_press_bytes()\n if keypress is not None:\n print(keypress)\n except Exception as ex:\n print(ex)\n\n\n #\n # parsed_version = g13.parse_keys()\n # if parsed_version is not None:\n # print('parsed version: ')\n # print(parsed_version)\n\n # g13.print_text('AQUA')\n # g13.set_color_from_named_color(LedColors.AQUA)\n # time.sleep(1)\n #\n # g13.print_text('BLUE')\n # g13.set_color_from_named_color(LedColors.BLUE)\n # time.sleep(1)\n #\n # g13.print_text('FUSCHIA')\n # g13.set_color_from_named_color(LedColors.FUSCHIA)\n # time.sleep(1)\n #\n # g13.print_text('GREEN')\n # g13.set_color_from_named_color(LedColors.GREEN)\n # time.sleep(1)\n #\n # g13.print_text('LIME')\n # g13.set_color_from_named_color(LedColors.LIME)\n # time.sleep(1)\n #\n # g13.print_text('MAROON')\n # g13.set_color_from_named_color(LedColors.MAROON)\n # time.sleep(1)\n #\n # g13.print_text('PINK')\n # g13.set_color_from_named_color(LedColors.PINK)\n # time.sleep(1)\n #\n # g13.print_text('PURPLE')\n # g13.set_color_from_named_color(LedColors.PURPLE)\n # time.sleep(1)\n #\n # g13.print_text('RED')\n # g13.set_color_from_named_color(LedColors.RED)\n # time.sleep(1)\n #\n # g13.print_text('YELLOW')\n # g13.set_color_from_named_color(LedColors.YELLOW)\n # time.sleep(1)\n #\n\n except Exception as e:\n print(e)\n except KeyboardInterrupt:\n print('^C')\n finally:\n g13.close()\n return\n\ndef std_out_ui():\n g13 = try_get_g13()\n ui = TerminalUI()\n ui.init_stick()\n try:\n while True:\n try:\n keys = g13.get_key_press_bytes()\n\n g13.print_stick(keys.stick_x, keys.stick_y)\n ui.print_stick(keys.stick_x, keys.stick_y)\n\n parse_keys(ui, keys)\n\n ui.flush()\n g13.write_lcd_bg()\n except usb1.USBError as e:\n if e.value == -7:\n pass\n except Exception as e:\n print(e)\n except KeyboardInterrupt:\n print('^C')\n finally:\n g13.close()\n\n\ndef parse_keys(ui, keys):\n if not any(keys.keys):\n ui.clear_keys()\n return\n\n for i, key in enumerate(G13_KEYS):\n b = keys.keys[i / 8]\n ui.set_key(key, b & 1 << (i % 8))\n\n\nif __name__ == '__main__':\n lcd_only_ui_colors()\n","sub_path":"examples/ui_example.py","file_name":"ui_example.py","file_ext":"py","file_size_in_byte":6982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"335626765","text":"# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.\n# Lean CLI v1.0. Copyright 2021 QuantConnect Corporation.\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 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\nimport json\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom lean.commands.create_project import (DEFAULT_CSHARP_MAIN, DEFAULT_CSHARP_NOTEBOOK, DEFAULT_PYTHON_MAIN,\n DEFAULT_PYTHON_NOTEBOOK)\nfrom lean.models.api import QCLanguage, QCLiveResults, QCProject\n\n\ndef create_fake_lean_cli_directory() -> None:\n \"\"\"Creates a directory structure similar to the one created by `lean init` with a Python and a C# project.\"\"\"\n (Path.cwd() / \"data\").mkdir()\n\n files = {\n (Path.cwd() / \"lean.json\"): \"\"\"\n{\n // data-folder documentation\n \"data-folder\": \"data\"\n}\n \"\"\",\n (Path.cwd() / \"Python Project\" / \"main.py\"): DEFAULT_PYTHON_MAIN.replace(\"$NAME$\", \"PythonProject\"),\n (Path.cwd() / \"Python Project\" / \"research.ipynb\"): DEFAULT_PYTHON_NOTEBOOK,\n (Path.cwd() / \"Python Project\" / \"config.json\"): json.dumps({\n \"algorithm-language\": \"Python\",\n \"parameters\": {}\n }),\n (Path.cwd() / \"CSharp Project\" / \"Main.cs\"): DEFAULT_CSHARP_MAIN.replace(\"$NAME$\", \"CSharpProject\"),\n (Path.cwd() / \"CSharp Project\" / \"research.ipynb\"): DEFAULT_CSHARP_NOTEBOOK,\n (Path.cwd() / \"CSharp Project\" / \"config.json\"): json.dumps({\n \"algorithm-language\": \"CSharp\",\n \"parameters\": {}\n }),\n (Path.cwd() / \"CSharp Project\" / \"CSharp Project.csproj\"): \"\"\"\n\n \n Debug\n AnyCPU\n net5.0\n 9\n bin/$(Configuration)\n false\n CS0618\n \n \n \n \n\n \"\"\"\n }\n\n for path, content in files.items():\n path.parent.mkdir(parents=True, exist_ok=True)\n with open(path, \"w+\") as file:\n file.write(content)\n\n\ndef create_api_project(id: int, name: str) -> QCProject:\n \"\"\"Creates a fake API project response.\"\"\"\n return QCProject(\n projectId=id,\n organizationId=\"123\",\n name=name,\n description=\"Description\",\n modified=datetime.now(),\n created=datetime.now(),\n language=QCLanguage.Python,\n collaborators=[],\n leanVersionId=10500,\n leanPinnedToMaster=True,\n parameters=[],\n liveResults=QCLiveResults(eStatus=\"Unknown\"),\n libraries=[]\n )\n","sub_path":"tests/test_helpers.py","file_name":"test_helpers.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"628116857","text":"#!/usr/bin/python3\n\"\"\" This is a script that starts a Flask web application.\n Listening on 0.0.0.0\n - port 5000\n\"\"\"\n\n\nfrom models import State\nfrom models import storage\nfrom flask import abort, render_template, Flask\napp = Flask(__name__)\napp.url_map.strict_slashes = False\n\n\n@app.route('/states_list')\ndef display_states():\n ''' Display a HTML page with States\n '''\n states = storage.all(\"State\")\n return render_template('7-states_list.html', states=states)\n\n\n@app.teardown_appcontext\ndef tear_down(self):\n ''' Teardown\n '''\n storage.close()\n\n\nif __name__ == \"__main__\":\n app.run()\n","sub_path":"web_flask/7-states_list.py","file_name":"7-states_list.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"549499789","text":"from __future__ import division, print_function\nimport sys\nsys.path.append('.')\nsys.path.append('modules/question_generator')\nsys.path.append('modules/vocab_seq')\nfrom vocab_seq import Vocab_Seq\nimport nltk\nfrom nltk.corpus import wordnet\nimport gensim\nimport util\nfrom util.data import vg_load_annotation\nfrom collections import Counter\nimport numpy as np\nimport h5py\nimport os\n\n\ndef create_attribute_label_from_attribute():\n # anno_att, anno_obj, anno_image = vg_load_annotation(['attribute', 'object', 'image'])\n anno_att, = vg_load_annotation(['attribute'])\n attributes = [att for obj in anno_att.values() for att in obj['attributes']]\n attribute_counter = Counter(attributes).most_common()\n attribute_1000, count_1000 = zip(*(attribute_counter[0:1000]))\n s = sum(count_1000)\n print('num sample: %d' % s)\n percent_1000 = [c / s for c in count_1000]\n\n util.io.save_json({'attribute': attribute_1000, 'count': count_1000, 'precent': percent_1000},\\\n 'temp/obj_attribute_counting.json')\n\ndef create_attribute_label_from_region():\n qa_region_corpus_file = 'data/vqg/cache/qa_region_attribute_corpus.json'\n\n if os.path.isfile(qa_region_corpus_file):\n print('load qa region corpus ...')\n qa_region_corpus = util.io.load_json(qa_region_corpus_file)\n else:\n print('create qa region corpus ...')\n anno_qa, anno_qa_region, qa_to_region, region_list = \\\n vg_load_annotation(['qa', 'region_30_enlarge', 'qa_to_region_30', 'region_list'])\n region_list = {unicode(img['id']):img for img in region_list}\n\n qa_region_corpus = dict.fromkeys(anno_qa.keys())\n n = 0\n num_qa = len(anno_qa)\n for qa_id, qa in anno_qa.iteritems():\n sentences = []\n sentences.append(qa['answer'])\n region_qa = anno_qa_region[qa_to_region[qa_id]]\n bbox_qa = np.array([region_qa['x'], region_qa['y'], region_qa['x']+region_qa['width'], \\\n region_qa['y']+region_qa['height']], dtype = np.float32)\n\n regions = region_list[unicode(qa['image_id'])]['regions']\n bbox_rg = np.array([[rg['x'], rg['y'], rg['x']+rg['width'], rg['y']+rg['height']] for rg in regions],\\\n dtype = np.float32)\n overlap = util.image.compute_iou(bbox_rg, bbox_qa)\n for i, rg in enumerate(regions):\n if overlap[i] > 0.5:\n sentences.append(rg['phrase'])\n qa_region_corpus[qa_id] = sentences\n n += 1\n if n % 100 == 0:\n print('create corpus: %.2f%%' % (100 * n / num_qa))\n\n util.io.save_json(qa_region_corpus, qa_region_corpus_file)\n\n ######################################\n\n attribute_count_file = 'data/vqg/cache/region_attribute_pos_counting_3gram.json'\n attribute_corpus_file = 'data/vqg/cache/attribute_corpus_3gram.json'\n restart = False\n if not restart and os.path.isfile(attribute_count_file) and os.path.isfile(attribute_corpus_file):\n print('load region attributes ...')\n count_data = util.io.load_json(attribute_count_file)\n attribute = count_data['attribute']\n count = count_data['count']\n # attribute_corpus = util.io.load_json(attribute_corpus_file)\n else:\n print('extract region attributes ...')\n wnl = nltk.WordNetLemmatizer()\n stopwords = nltk.corpus.stopwords.words('english')\n stopwords_part = stopwords[0:stopwords.index('of')] + stopwords[(stopwords.index('under')+1)::]\n punc = Vocab_Seq.punctuations + ['\\'s']\n omitted_words = dict.fromkeys(stopwords_part + punc)\n synonym_dict = {u'grey': u'gray', u'racquet': u'racket', u'airplane': u'plane', u'cell phone': u'cellphone', \\\n u'blond': u'blonde', u'day time': u'daytime'}\n # u'two':u'mto', u'three':u'mto',u'four':u'mto',u'five':u'mto',u'six':u'mto',\\\n # u'2':u'mto',u'3':u'mto',u'4':u'mto',u'5':u'mto',u'6':u'mto', u'1': u'one'}\n # m_t_o means 'more than one'\n pos_mapping = Vocab_Seq.pos_mapping\n pos_mapping['CD'] = 'CD'\n\n # def _language_normalize(sent):\n # token = nltk.word_tokenize(unicode(sent).lower())\n # token = [wnl.lemmatize(w, wordnet.NOUN) for w in token if w not in omitted_words]\n # token = [wnl.lemmatize(w, wordnet.VERB) for w in token]\n # token = [wnl.lemmatize(w, wordnet.ADJ) for w in token]\n # token = [wnl.lemmatize(w, wordnet.ADV) for w in token]\n # sent = ' '.join(token)\n # for original_word, target_word in synonym_dict.iteritems():\n # sent = sent.replace(original_word, target_word)\n # return sent\n def _word_normalize(w):\n w = wnl.lemmatize(w, wordnet.NOUN)\n w = wnl.lemmatize(w, wordnet.VERB)\n w = wnl.lemmatize(w, wordnet.ADJ)\n w = wnl.lemmatize(w, wordnet.ADV)\n return w\n\n def _get_n_gram(sent, max_n):\n sent = unicode(sent).lower()\n for o_w, t_w in synonym_dict.iteritems():\n sent = sent.replace(o_w, t_w)\n token = nltk.pos_tag(nltk.word_tokenize(sent))\n token = [(_word_normalize(w), pos_mapping.get(p,p))\\\n for (w,p) in token if w not in omitted_words]\n if len(token) == 0:\n return []\n for i_w, (w, p) in enumerate(token):\n if p == 'CD':\n if w == '1' or w == 'one':\n token[i_w] = ('one', 'CD')\n elif w == '0' or w == 'zero':\n token[i_w] = ('zero', 'CD')\n else:\n token[i_w] = ('more_than_one', 'CD')\n \n grams = token[:]\n words, poss = zip(*token)\n for n in xrange(2, max_n+1):\n for j in xrange(0, len(token)-n+1):\n # grams.append(' '.join(token[j:(j+n)]))\n grams.append((' '.join(words[j:(j+n)]), ' '.join(poss[j:(j+n)])))\n return grams\n\n word_pos_list = []\n attribute_corpus = {}\n max_word_num = 3\n for i, qa_id in enumerate(qa_region_corpus.keys()):\n corpus = qa_region_corpus[qa_id]\n grams = []\n for sent in corpus:\n # sent = _language_normalize(sent)\n grams += _get_n_gram(sent, max_word_num)\n grams = list(set(grams))\n word_pos_list += grams\n attribute_corpus[qa_id] = grams\n if i % 100 == 0:\n print('counting attribute: %.2f%%' % (i*100/len(qa_region_corpus)))\n\n\n stopwords_dict = dict.fromkeys(stopwords)\n attribute_pos_counter = Counter(word_pos_list).most_common()\n # attribute_pos_counter = [a for a in attribute_pos_counter if a[0][0].split(' ')[0] not in stopwords_dict]\n\n attribute_counter = {}\n for (att, pos), c in attribute_pos_counter:\n if att not in attribute_counter:\n attribute_counter[att] = [pos, c]\n else:\n attribute_counter[att][1] += c\n attribute_counter = [((att, pos), c) for att, (pos, c) in attribute_counter.iteritems()]\n attribute_counter.sort(reverse = True, key = lambda x:x[1])\n\n attribute, count = zip(*(attribute_counter[0:10000]))\n util.io.save_json({'attribute': attribute, 'count': count}, attribute_count_file)\n util.io.save_json(attribute_corpus, attribute_corpus_file)\n\n ##########################\n # attribute = [(att, pos, c) for (att, pos), c in zip(attribute, count)]\n # attribute_dict = {}\n # for att in attribute:\n # if att[1] not in attribute_dict:\n # attribute_dict[att[1]] = [att]\n # else:\n # attribute_dict[att[1]].append(att)\n # pos_list = [(p, sum([att[2] for att in att_list])) \\\n # for p, att_list in attribute_dict.iteritems()]\n # pos_list.sort(reverse = True, key = lambda x:x[1])\n # output_list = []\n # for pos, c in pos_list:\n # if len(attribute_dict[pos]) >= 5:\n # att_top_5 = attribute_dict[pos][0:5]\n # else:\n # att_top_5 = attribute_dict[pos] + ['NONE'] * (5-len(attribute_dict[pos]))\n\n # output_list.append('%s\\t%d\\t%s\\t%s\\t%s\\t%s\\t%s' % ((pos, c) + tuple(att_top_5)))\n # util.io.save_str_list(output_list, 'data/vqg/cache/attribute_type.txt')\n # util.io.save_str_list(output_list, './attribute_type.txt')\n\n ############################\n\n\ndef create_attribute_dataset(set_name):\n assert(set_name in ['train', 'val', 'test'])\n dataset_filename = 'data/vqg/training_data/attribute/attribute_label_%s.h5' % set_name\n attribute_list_file = 'data/vqg/training_data/attribute/attribute_list_1.0.json'\n # dataset_filename = 'data/vqg/training_data/attribute/attribute_label_noNN_%s.h5' % set_name\n # attribute_list_file = 'data/vqg/training_data/attribute/attribute_list_noNN_1.0.json'\n\n # anno_qa, = vg_load_annotation(['qa'])\n qa_id_list = util.io.load_str_list('data/vqg/splits/vg_qa_split_%s.txt' % set_name)\n \n print('loading attribute corpus and attribute list...')\n attribute_corpus = util.io.load_json('data/vqg/cache/attribute_corpus_3gram.json')\n \n # if os.path.isfile(attribute_list_file):\n if False:\n attribute_list = util.io.load_json(attribute_list_file)\n assert(len(attribute_list) == 3000)\n print('load attribute_list from %s' % attribute_list_file)\n else:\n attribute_count = util.io.load_json('data/vqg/cache/region_attribute_pos_counting_3gram.json')\n attribute_type_list = util.io.load_str_list('data/vqg/training_data/attribute/attribute_type_list.txt')\n attribute_list = [[unicode(att), unicode(pos), count] for (att, pos), count in \\\n zip(attribute_count['attribute'], attribute_count['count']) if pos in attribute_type_list]\n attribute_list = attribute_list[0:3000]\n util.io.save_json(attribute_list, attribute_list_file)\n # attribute_dict = {'_'.join(att):i for i, att in enumerate(attribute_list)}\n\n # num_qa = len(qa_id_list)\n # data = np.zeros((num_qa, 3001), dtype = np.float32)\n\n # for i, qa_id in enumerate(qa_id_list):\n # corpus = attribute_corpus[qa_id]\n # for att in corpus:\n # idx = attribute_dict.get('_'.join(att), -1)\n # data[i, idx] = 1\n # if i % 100 == 0:\n # print('create %s attribute label: %.2f%%' % (set_name, i*100/num_qa))\n\n # h5_dataset = h5py.File(dataset_filename, 'w')\n # dataset = h5_dataset.create_dataset('attribute_1000', shape = (num_qa, 1000), dtype = np.float32)\n # dataset[:] = data[:, 0:1000]\n # dataset = h5_dataset.create_dataset('attribute_2000', shape = (num_qa, 2000), dtype = np.float32)\n # dataset[:] = data[:, 0:2000]\n # dataset = h5_dataset.create_dataset('attribute_3000', shape = (num_qa, 3000), dtype = np.float32)\n # dataset[:] = data[:, 0:3000]\n # h5_dataset.close()\n\n\nif __name__ == '__main__':\n # create_attribute_label_from_attribute()\n # create_attribute_label_from_region()\n create_attribute_dataset(sys.argv[1])\n\n # util.data.split_h5_file('data/vqg/training_data/attribute/attribute_label_train.h5',5)\n\n","sub_path":"modules/answer_predictor/create_attribute_label.py","file_name":"create_attribute_label.py","file_ext":"py","file_size_in_byte":11341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"505636531","text":"from datetime import datetime, timedelta\nfrom typing import List\n\nimport models.measurement\nimport schemas.measurement\nimport sqlalchemy as db\nfrom db.database import Base\nfrom db.workbench_helper import WorkbenchHelper\nfrom models.measurement import Measurement\nfrom sqlalchemy.orm import Session\n\n\nclass LoggingDatabase:\n # replace the user, password, hostname and database according to your configuration according to your information\n engine = db.create_engine(\n \"postgresql://read_write:simplepass1098@localhost:5432/logging\", echo=False\n )\n\n def __init__(self):\n self.connection = self.engine.connect()\n\n def create_tables(self):\n Base.metadata.create_all(self.engine)\n\n def get_all_measurements(self) -> List[Measurement]:\n self.session = Session(bind=self.connection)\n measurements: List[Measurement] = self.session.query(Measurement).all()\n\n for meas in measurements:\n print(meas)\n\n return measurements\n\n def get_by_query(self, query):\n fetchQuery = self.connection.execute(f\"SELECT * FROM {query}\")\n query_data = fetchQuery.fetchall()\n\n for data in query_data:\n print(data)\n\n return query_data\n\n def insert_measurement(\n self, measurement: schemas.measurement.MeasurementCreate\n ) -> models.measurement.Measurement:\n session = Session(bind=self.connection)\n db_measurement = models.measurement.Measurement(**measurement.dict())\n session.add(db_measurement)\n session.commit()\n\n return db_measurement\n\n def get_measurements_since_date(self, since_date: datetime) -> List[Measurement]:\n session = Session(bind=self.connection)\n measurements: List[Measurement] = session.query(Measurement).filter(\n Measurement.d_datetime > since_date\n ).order_by(Measurement.d_datetime.asc()).all()\n\n return measurements\n\n def get_measurements_in_last_timedelta(self, period: timedelta):\n since_date = WorkbenchHelper.get_datetime_now_to_nearest_sec() - period\n return self.get_measurements_since_date(since_date)\n\n\nif __name__ == \"__main__\":\n loggingDb = LoggingDatabase()\n results = loggingDb.get_by_query(\"public.measurements\")\n results = loggingDb.get_all_measurements()\n","sub_path":"workbench_web/backend/app/app/crud/measurement.py","file_name":"measurement.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"398098393","text":"import os.path\n\nfrom jinja2 import (\n Environment,\n FileSystemLoader,\n)\n\nimport random\n\n\ndef random_string():\n \"\"\"\n 生成一个随机的字符串\n \"\"\"\n seed = 'bdjsdlkgjsklgelgjelgjsegker234252542342525g'\n s = ''\n for i in range(16):\n # 这里 len(seed) - 2 是因为我懒得去翻文档来确定边界了\n random_index = random.randint(0, len(seed) - 2)\n s += seed[random_index]\n return s\n\n\ndef initialized_environment():\n path = os.path.join(os.path.dirname(__file__), 'templates')\n # 创建一个加载器, jinja2 会从这个目录中加载模板\n loader = FileSystemLoader(path)\n # 用加载器创建一个环境, 有了它才能读取模板文件\n e = Environment(loader=loader)\n return e\n\n\nclass GuaTemplate:\n # 初始化只执行一次（用类变量实现）\n e = initialized_environment()\n\n @classmethod\n def render(cls, filename, *args, **kwargs):\n # 调用 get_template() 方法加载模板并返回\n template = cls.e.get_template(filename)\n # 用 render() 方法渲染模板\n # 可以传递参数\n return template.render(*args, **kwargs)\n\n\ndef error(request, code=404):\n \"\"\"\n 根据 code 返回不同的错误响应\n 目前只有 404\n \"\"\"\n # 之前上课我说过不要用数字来作为字典的 key\n # 但是在 HTTP 协议中 code 都是数字似乎更方便所以打破了这个原则\n e = {\n 404: b'HTTP/1.x 404 NOT FOUND\\r\\n\\r\\n

NOT FOUND

',\n }\n return e.get(code, b'')\n\n\ndef formatted_header(headers, code=200):\n \"\"\"\n Content-Type: text/html\n Set-Cookie: user=gua\n \"\"\"\n header = 'HTTP/1.1 {} OK GUA\\r\\n'.format(code)\n header += ''.join([\n '{}: {}\\r\\n'.format(k, v) for k, v in headers.items()\n ])\n return header\n\n\ndef redirect(url, headers=None):\n \"\"\"\n 浏览器在收到 302 响应的时候\n 会自动在 HTTP header 里面找 Location 字段并获取一个 url\n 然后自动请求新的 url\n \"\"\"\n h = {\n 'Location': url,\n }\n if headers is None:\n headers = h\n else:\n headers.update(h)\n # 302 状态码的含义, Location 的作用\n # 注意, 没有 HTTP body 部分\n header = formatted_header(headers, 302)\n r = header + '\\r\\n'\n return r.encode()\n\n\ndef html_response(body, headers=None):\n h = {\n 'Content-Type': 'text/html',\n }\n if headers is None:\n headers = h\n else:\n headers.update(h)\n header = formatted_header(headers)\n r = header + '\\r\\n' + body\n return r.encode()\n\n\ndef static(request):\n \"\"\"\n 静态资源的处理函数, 读取图片并生成响应返回\n \"\"\"\n filename = request.query.get('file', 'doge.gif')\n path = 'static/' + filename\n with open(path, 'rb') as f:\n header = b'HTTP/1.x 200 OK\\r\\n\\r\\n'\n img = header + f.read()\n return img\n\n\ndef route_dict():\n \"\"\"\n 路由字典\n key 是路由(路由就是 path)\n value 是路由处理函数(就是响应)\n \"\"\"\n d = {\n '/': all,\n '/static': static,\n }\n return d\n","sub_path":"8.18/mario_python/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":3112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"486002376","text":"#创建二级子进程避免僵尸　\n#成为孤儿就不会成为僵尸\nimport os \nfrom time import sleep\nimport sys\n\ndef f1():\n sleep(3)\n print('第一件事')\n\ndef f2():\n sleep(4)\n print('第二件事')\n\n\npid = os.fork()\n\nif pid < 0:\n print('error')\n\nelif pid == 0:\n #创建二级子进程\n p = os.fork()\n if p == 0:\n f2()\n\n else:\n os._exit()\n # sys.exit()\n\n\n\nelse:\n os.wait()#等待一级子进程退出\n f1()\n\n\n#相当于多进程了\n","sub_path":"网络/pythonNet/day5/forkfujincheng.py","file_name":"forkfujincheng.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"83213668","text":"\"\"\"\nCreated by Sam Henly, somewhere in Washington, 2017\n\nFunctions to classify images as photos and/or as graphics.\n\nScheme\n\n\"\"\"\n\nimport sys\nsys.path.insert(0, '/home/sam/thesisOutput/stats/')\nsys.path.insert(0, '/home/sam/thesisOutput/postgres/')\nimport pandas as pd\nimport image_feature_retrieval, os, utility, time, pickle, shutil\nimport stat_management as sm\nimport sklearn.preprocessing as prep\nimport db_core_functions as db\nimport numpy as np\nfrom multiprocessing import Queue, Process\nimport ibank_map as imap\nimport sklearn.decomposition.pca as pca\n\n\ndef get_classified_image_features():\n \"\"\"Pull image features for classified images from aspasia.\"\"\"\n\n dataDirectory = '/Blackbird/graphics/'\n featureData = {}\n featureTable = 'image$common_features'\n\n for subdir in ['graphic','photo','mix']:\n\n images = os.listdir(dataDirectory + subdir)\n featureData[subdir] = image_feature_retrieval.pull_image_features(images, featureTable) \n\n photoOnlyDic = {'graphic' : 0,\n 'mix' : 0,\n 'photo' : 1}\n graphicOnlyDic = {'graphic': 1,\n 'mix' : 0,\n 'photo' : 0}\n\n for imageClass in featureData.keys():\n featureData[imageClass]['graphic_only'] = graphicOnlyDic[imageClass]\n featureData[imageClass]['photo_only'] = photoOnlyDic[imageClass]\n\n return pd.concat([x for x in featureData.values()])\n\n\ndef get_bw_columns():\n \"\"\"\n Return list of columns that should be present in all images,\n even those with only L band.\n \"\"\"\n\n return ['phash', 'pixels', 'height', 'width', 'aspect_ratio',\n 'l01_frac_empty_bn', 'l01_minbn_01pct_px', 'l01_minbn_05pct_px',\n 'l01_minbn_10pct_px', 'l01_minbn_25pct_px', 'l01_px_share_001bn',\n 'l01_px_share_005bn', 'l01_px_share_010bn', 'l01_px_share_025bn',\n 'l01_px_share_050bn', 'l01_px_share_100bn', 'l_cdf008',\n 'l_cdf016', 'l_cdf024', 'l_cdf032', 'l_cdf040', 'l_cdf048',\n 'l_cdf056', 'l_cdf064', 'l_cdf072', 'l_cdf080', 'l_cdf088',\n 'l_cdf096', 'l_cdf104', 'l_cdf112', 'l_cdf120', 'l_cdf128',\n 'l_cdf136', 'l_cdf144', 'l_cdf152', 'l_cdf160', 'l_cdf168',\n 'l_cdf176', 'l_cdf184', 'l_cdf192', 'l_cdf200', 'l_cdf208',\n 'l_cdf216', 'l_cdf224', 'l_cdf232', 'l_cdf240', 'l_cdf248',\n 'l_entropy_r3_cdf000', 'l_entropy_r3_cdf001',\n 'l_entropy_r3_cdf005', 'l_entropy_r3_cdf010',\n 'l_entropy_r3_cdf025', 'l_entropy_r3_cdf050',\n 'l_entropy_r3_cdf075', 'l_entropy_r3_cdf100',\n 'l_entropy_r3_cdf150', 'l_entropy_r3_cdf200',\n 'l_entropy_r3_cdf250', 'l_entropy_r3_cdf300',\n 'l_entropy_r3_cdf350', 'l_entropy_r3_cdf400',\n 'l_entropy_r3_cdf450', 'l_entropy_r3_cdf500',\n 'l_entropy_r3_cdf550', 'l_entropy_r3_cdf600',\n 'l_entropy_r3_kurt', 'l_entropy_r3_mean', 'l_entropy_r3_obs',\n 'l_entropy_r3_sdev', 'l_entropy_r3_skew', 'l_entropy_r6_cdf000',\n 'l_entropy_r6_cdf001', 'l_entropy_r6_cdf005',\n 'l_entropy_r6_cdf010', 'l_entropy_r6_cdf025',\n 'l_entropy_r6_cdf050', 'l_entropy_r6_cdf075',\n 'l_entropy_r6_cdf100', 'l_entropy_r6_cdf150',\n 'l_entropy_r6_cdf200', 'l_entropy_r6_cdf250',\n 'l_entropy_r6_cdf300', 'l_entropy_r6_cdf350',\n 'l_entropy_r6_cdf400', 'l_entropy_r6_cdf450',\n 'l_entropy_r6_cdf500', 'l_entropy_r6_cdf550',\n 'l_entropy_r6_cdf600', 'l_entropy_r6_kurt',\n 'l_entropy_r6_mean', 'l_entropy_r6_obs', 'l_entropy_r6_sdev',\n 'l_entropy_r6_skew', 'l_kurt', 'l_mean', 'l_sdev', 'l_skew',\n 'graphic_only', 'photo_only']\n\n\ndef process_raw_features(imageFeatures, popLabels = False):\n \"\"\"Split imageFeatures into bw/color set, process a bit.\"\"\"\n\n # get rid of bands column\n del imageFeatures['bands']\n\n # get set of columns that should be present in all images\n bwColumns = get_bw_columns()\n if popLabels:\n bwColumns.remove('graphic_only')\n bwColumns.remove('photo_only')\n\n # split data into black and white frame,\n # color frame\n bwData = imageFeatures[bwColumns].dropna()\n colorData = imageFeatures.dropna()\n\n # identify images that were originally color in bw dataset\n colorPhashes = set(colorData.phash.unique())\n bwData['originally_color'] = bwData.phash.apply(lambda x: int(x in colorPhashes))\n\n # return data\n return colorData, bwData\n\n\ndef make_graphics_data_sets():\n \"\"\"Create two project Datasets.\"\"\"\n\n colorX, bwX = process_raw_features(get_classified_image_features())\n\n colorY = colorX[['graphic_only','photo_only']]\n bwY = bwX[['graphic_only','photo_only']]\n\n for dSet in [colorX, bwX]:\n del dSet['graphic_only']\n del dSet['photo_only']\n del dSet['phash']\n\n subsets = {'photo': [[], 'photo_only'],\n 'graphic': [[], 'graphic_only']}\n\n XScaler = prep.StandardScaler\n\n description = \"Dataset containing features from image$common_features.\" \\\n \"\\nImages in this project are originally in color, with additional \"\\\n \"features extracted after originals were \"\\\n \"converted to black and white.\\nProject aims to classify images as \"\\\n \"purely graphical (or not) and purely photographic (or not).\"\n sm.prepare_dataset(colorX, colorY, 'cgraphics',\n description,\n subsets = subsets,\n XScaler = XScaler,\n testSize = 0.15)\n\n description = \"Dataset containing features from image$common_features.\" \\\n \"\\nImages in this project are either originally black and white or \"\\\n \"converted to black and white.\\nProject aims to classify images as \"\\\n \"purely graphical (or not) and purely photographic (or not).\"\n sm.prepare_dataset(bwX, bwY, 'bwgraphics',\n description,\n subsets = subsets,\n XScaler = XScaler,\n testSize = 0.15)\n\n pcaObj = pca.PCA()\n pcaObj.fit(colorX)\n description = \"Dataset containing features from image$common_features.\" \\\n \"\\nImages in this project are originally in color, with additional \"\\\n \"features extracted after originals were \"\\\n \"converted to black and white.\\nProject aims to classify images as \"\\\n \"purely graphical (or not) and purely photographic (or not).\"\\\n \"\\n\\nCovariates are transformed via default PCA.\"\n sm.prepare_dataset(pd.DataFrame(pcaObj.transform(colorX)), colorY, 'cgraphics_pca',\n description,\n subsets = subsets,\n XScaler = XScaler,\n pcaX = pcaObj,\n testSize = 0.15)\n\n pcaObj = pca.PCA()\n pcaObj.fit(bwX)\n description = \"Dataset containing features from image$common_features.\" \\\n \"\\nImages in this project are either originally black and white or \"\\\n \"converted to black and white.\\nProject aims to classify images as \"\\\n \"purely graphical (or not) and purely photographic (or not).\"\\\n \"\\n\\nCovariates are transformed via default PCA.\"\n sm.prepare_dataset(pd.DataFrame(pcaObj.transform(bwX)), bwY, 'bwgraphics_pca',\n description,\n subsets = subsets,\n XScaler = XScaler,\n pcaX=pcaObj,\n testSize = 0.15)\n\n\n\ndef fit_some_models():\n \"\"\"Fit a bunch of pre-specified classifiers on photo data.\"\"\"\n\n models = [sm.qd_gradiant_boost,\n sm.qd_sgd,\n sm.qd_logit,\n sm.qd_svm_classifier,\n sm.qd_random_forest_classifier,\n sm.test_constrained_adaboost]\n\n modelNameRoots = ['qd_rfClassifier',\n 'qd_svmCClassifier',\n 'qd_logitCVClassifier',\n 'qd_sdgCClassifier',\n 'qd_gbCClassifier',\n 'tc_adaBoost']\n\n projectNames = ['cgraphics', 'bwgraphics', 'cgraphics_pca', 'bwgraphics_pca']\n subsets = ['photo','graphic']\n\n for projectName in projectNames:\n for subset in subsets:\n for model in models:\n model(projectName, subset)\n modelNames = [x + '_%s' % subset for x in modelNameRoots]\n sm.CombinedWeightedModels(projectName, 'combined_%s' %subset,\n modelNames, 'classifier', subset = subset)\n\ndef print_model_fit(project):\n \"\"\"Print out fit for all models in project.\"\"\"\n\n modelDirectory = sm.model_directory(project)\n models = [pickle.load(modelDirectory + x ) for x in os.listdir(modelDirectory) if '.pickle' in x]\n\n for model in models:\n model.print_fit()\n\n\n\n#################################################################################################33\n\ndef classify_everything(modCut, startCut = 0):\n \"\"\"Pull all features + classify.\"\"\"\n\n def prep_data_for_prediction(rawPull):\n\n colorData, bwData = process_raw_features(rawPull, popLabels=True)\n colorData = colorData.set_index('phash')\n bwData = bwData.set_index('phash')\n\n dataSets = {'color': colorData, 'bw': bwData}\n\n return dataSets\n\n def scale_predict(model, data):\n \"\"\"Scale and make prediction.\"\"\"\n if model.XScaler is not None:\n return model.predict_c(model.XScaler.transform(data))\n return model.predict_c(data)\n\n def classify_task(data, outQueue, models):\n \"\"\"Classify images.\"\"\"\n\n data = prep_data_for_prediction(data)\n\n # get some useful indices and prepare export data frames\n cIndex = data['color'].index\n bwIndex = data['bw'].index\n colorOut = pd.DataFrame(index=cIndex)\n bwOut = pd.DataFrame(index = bwIndex)\n\n # make predictions\n for model in models.keys():\n modelObj = models[model]\n if model[0] == 'c':\n colorOut[model] = pd.Series(scale_predict(modelObj, data['color']), index=cIndex)\n else:\n bwOut[model] = pd.Series(scale_predict(modelObj, data['bw']), index=bwIndex)\n\n # make output dictionary for color images with empty dicts for bw-only images\n colorDict = colorOut.to_dict(orient = 'index')\n colorDict.update({x : {} for x in set(bwIndex)-set(cIndex)})\n\n # prepare . . . mostly . . . output\n mess = {'color': colorDict,\n 'bw' : bwOut.to_dict(orient='index'),\n 'phashes' : set(bwIndex)}\n\n # convert messy output to list of dicts for upload, and return to queue\n outQueue.put(process_dictionary_mess(mess))\n\n def get_models():\n \"\"\"Load models for predicting each of four variables of interest.\"\"\"\n\n models = dict()\n models['color_photo'] = sm.load_model('cgraphics', 'qd_gbCClassifier_photo')\n models['color_graphic'] = sm.load_model('cgraphics', 'qd_gbCClassifier_graphic')\n models['bw_photo'] = sm.load_model('bwgraphics', 'qd_gbCClassifier_photo')\n models['bw_graphic'] = sm.load_model('bwgraphics', 'qd_gbCClassifier_graphic')\n\n return models\n\n def process_dictionary_mess(d):\n \"\"\"\n Handle some preliminary output from classify_task;\n convert to list of dicts.\n \"\"\"\n\n dList = [{'phash':x} for x in d['phashes']]\n for x in dList:\n phash = x['phash']\n x.update(d['bw'][phash])\n x.update(d['color'][phash])\n\n return dList\n\n def process_cut(rawPull, models):\n \"\"\"Take result of one pull; process + upload.\"\"\"\n\n nWorkers = max(2, utility.get_thread_capacity() - 2)\n dataSets = [pd.DataFrame(x, columns = rawPull.columns) for x in np.array_split(rawPull, nWorkers)]\n\n outQueue = Queue()\n workers = [Process(target=classify_task, args=(dataSets[i], outQueue, models,)) for i in range(nWorkers)]\n\n for worker in workers:\n worker.start()\n\n # gather output\n outCount = 0\n outDicts = []\n while outCount < nWorkers:\n while not outQueue.empty():\n outDicts.extend(outQueue.get())\n outCount += 1\n time.sleep(4)\n\n # upload\n db.load_dictionaries(outDicts, 'image$graphic_classification')\n\n # load models and prepare list classification slices\n models = get_models()\n cuts = list(range(modCut))[startCut:]\n\n # truncate the table\n db.query(\"TRUNCATE TABLE image$graphic_classification;\")\n\n # classify and load each slice of the image features\n for cut in cuts:\n utility.feedback('Starting cut {0!s} of {1!s}.'.format(cut, modCut-1))\n process_cut(image_feature_retrieval.pull_image_features_by_mod(modCut, cut, 'image$common_features'),\n models)\n\n\n#################################################################################################33\n\ndef get_some_classified_images(chunk, summaryOnly = False):\n \"\"\"Get a bunch of images that were classified.\"\"\"\n\n def move_images_update_completed_phashes(condition, phashes, movedPhashes,\n archiveFolder, projectDirectory):\n \"\"\"Move files identified by queries w/o redundnacy.\"\"\"\n\n # get a list of files to be moved by condition\n # these files should not have already been moved\n filesToMove = [archiveFolder + x for x in os.listdir(archiveFolder)\n if utility.clean_phash(x) in phashes[condition] - movedPhashes]\n\n # make sure directory exists . . .\n targetDirectory = '{0}to_sort/{1}/'.format(projectDirectory, condition)\n if not os.path.isdir(targetDirectory):\n os.mkdir(targetDirectory)\n\n # move the files to specified condition directory\n for file in filesToMove:\n shutil.move(file, targetDirectory)\n\n # update set of moved hashes\n movedPhashes.update(phashes[condition])\n\n projectDirectory = '/Blackbird/graphics/'\n archiveBase = '/Carsomyr/image_bank/archives/0016/'\n\n # parameters for sorting images\n unsureThreshold = \"0.1\" # images w/ class probability for photo or graphic at 0.5 +/-\n # unsureThreshold are categorized as unsure\n confusionThreshold = \"0.3\" # \"confusion\" if pr(graphic) & pr(photo) both > confusionThreshold\n\n # bad/good cases\n conditions = {'unsure': \"(abs(0.5-color_photo) < {0} OR abs(0.5-bw_photo) < {0} OR \"\\\n \"abs(0.5-color_graphic) < {0} OR abs(0.5-bw_graphic) < {0});\".format(unsureThreshold),\n 'confused': \"((color_photo > {0} AND color_graphic > {0}) OR \"\\\n \"(bw_photo > {0} AND bw_graphic > {0}));\".format(confusionThreshold),\n 'photo_confident': '(round(color_photo)::int = 1 AND round(bw_photo)::int = 1);',\n 'graphic_confident': '(round(color_graphic)::int = 1 AND round(bw_graphic)::int = 1);'}\n\n\n # run query to get interesting images from photo subset\n # get phashes for those images\n phashes = {}\n print('Classifications of photos for chunk {0!s}:'.format(chunk))\n for condition, queryCondition in conditions.items():\n query = \"SELECT phash FROM image$graphic_classification WHERE \" \\\n \"abs(floor(phash/(10^16))) = {0!s} and {1};\".format(chunk, queryCondition)\n phashes[condition] = set([x for x in db.query(query)['phash']])\n print(condition,':',len(phashes[condition]))\n\n if summaryOnly:\n return None\n\n # extract an archive containing (most of) those images\n archivePaths = ['{1}{0!s}.tar.gz'.format(chunk, archiveBase)]\n archiveDir = '{0}to_sort/inbound/'.format(projectDirectory)\n archiveFolder = imap.archive_batch(archiveList = archivePaths, archiveDir=archiveDir)[0]\n\n # move files of interest\n movedPhashes = set()\n for condition in conditions.keys():\n move_images_update_completed_phashes(condition, phashes, movedPhashes,\n archiveFolder, projectDirectory)\n # remove archives\n imap.remove_completed_folders(archiveDir)\n\n#if __name__ == '__main__':\n\n # get_some_classified_images(481, summaryOnly = True)\n # get_some_classified_images(150, summaryOnly = True)\n #\n #make_graphics_data_sets()\n #fit_some_models()\n #classify_everything()\n\n # get_some_classified_images(481, summaryOnly = True)\n #get_some_classified_images(153)\n","sub_path":"photo_classification.py","file_name":"photo_classification.py","file_ext":"py","file_size_in_byte":16799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"634636986","text":"#\n# Description : MODEL INTERFACE WEB\n#\n# Author : J.P\n# Date : 26/06/2021\n# Version : 1.0\n#\n#\n# Example 1 : TBD\n#\n\nimport sys\nfrom PySide2 import QtCore, QtGui, QtWidgets\nimport numpy as np\n\n\nclass window(QtWidgets.QDialog):\n def __init__(self, parent=None):\n\n QtWidgets.QDialog.__init__(self,parent)\n\n # == Window Config. ==\n self.matrix_line_nb = 8\n if int(sys.argv[1]) < 1 or int(sys.argv[1]) > 8 :\n sys.exit(\"ARGV ERROR - argv[1] must be between [1:8]\")\n else:\n self.matrix_nb = int(sys.argv[1])\n self.grid = np.zeros([self.matrix_line_nb,8*self.matrix_nb], dtype=bool)\n self.grid_layout = QtWidgets.QGridLayout()\n self.setLayout(self.grid_layout)\n # ====================\n\n # == Set Checkboxes ==\n for j in range (0, self.matrix_line_nb):\n for i in range (0, 8*self.matrix_nb):\n check_btn = QtWidgets.QCheckBox()\n self.grid_layout.addWidget(check_btn,j,i)\n # ===================\n\n # == Set Fields Registers Configuration ==\n # self.field_decod_mode = QtWidgets.QLineEdit(\"\")\n # self.text_decod_mode = QtWidgets.QLabel(\"Decode Mode : \")\n \n # self.field_intensity = QtWidgets.QLineEdit(\"\")\n # self.text_intensity = QtWidgets.QLabel(\"Intensity : \")\n \n # self.field_scan_limit = QtWidgets.QLineEdit(\"\")\n # self.text_scan_limit = QtWidgets.QLabel(\"Scan Limit : \")\n \n # self.field_shutdown = QtWidgets.QLineEdit(\"\")\n # self.text_shutdown = QtWidgets.QLabel(\"Shutdown : \")\n \n # self.field_display_test = QtWidgets.QLineEdit(\"\")\n # self.text_display_test = QtWidgets.QLabel(\"Display Test : \")\n \n # self.grid_layout.addWidget(self.field_decod_mode, 8, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_decod_mode, 8, 8*self.matrix_nb)\n\n # self.grid_layout.addWidget(self.field_intensity, 9, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_intensity, 9, 8*self.matrix_nb)\n\n # self.grid_layout.addWidget(self.field_scan_limit, 10, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_scan_limit, 10, 8*self.matrix_nb)\n \n # self.grid_layout.addWidget(self.field_shutdown, 11, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_shutdown, 11, 8*self.matrix_nb)\n \n # self.grid_layout.addWidget(self.field_display_test, 12, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_display_test, 12, 8*self.matrix_nb)\n\n # self.field_decod_mode.setText(\"00\")\n # self.field_intensity.setText(\"00\")\n # self.field_scan_limit.setText(\"00\")\n # self.field_shutdown.setText(\"00\")\n # self.field_display_test.setText(\"00\")\n \n # =======================================\n\n # == Set Fields Memory File Configuration ==\n # self.field_instance_path = QtWidgets.QLineEdit(\"\")\n # self.text_instance_path = QtWidgets.QLabel(\"Instance : \")\n # self.grid_layout.addWidget(self.field_instance_path, 13, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_instance_path, 13, 8*self.matrix_nb)\n # self.field_instance_path.setText(\"/tb_top/i_dut/tdpram_inst_0/v_ram\")\n\n # self.field_memory_size = QtWidgets.QLineEdit(\"\")\n # self.text_memory_size = QtWidgets.QLabel(\"Memory Size : \")\n # self.grid_layout.addWidget(self.field_memory_size, 14, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_memory_size, 14, 8*self.matrix_nb)\n # self.field_memory_size.setText(\"256\")\n\n # self.field_data_size = QtWidgets.QLineEdit(\"\")\n # self.text_data_size = QtWidgets.QLabel(\"Memory Size (in bits) : \")\n # self.grid_layout.addWidget(self.field_data_size, 15, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_data_size, 15, 8*self.matrix_nb)\n # self.field_data_size.setText(\"16\")\n\n # self.field_file_name = QtWidgets.QLineEdit(\"\")\n # self.text_file_name = QtWidgets.QLabel(\"File Name : \")\n # self.grid_layout.addWidget(self.field_file_name, 16, 8*self.matrix_nb + 1)\n # self.grid_layout.addWidget(self.text_file_name, 16, 8*self.matrix_nb)\n # self.field_file_name.setText(\"max7219_ram.mem\")\n \n # ==========================================\n\n # == Bouton Inversion CheckBoxes Matrix ==\n self.__boutonInvCheckbox = QtWidgets.QPushButton(\"Inverser CheckBox\")\n self.grid_layout.addWidget(self.__boutonInvCheckbox, 17, 8*self.matrix_nb)\n self.__boutonInvCheckbox.clicked.connect(self.inv_checkbox)\n # =============================\n\n # == Set boutons generer ==\n self.__boutonGenerer = QtWidgets.QPushButton(\"Generer\")\n self.grid_layout.addWidget(self.__boutonGenerer, 18, 8*self.matrix_nb)\n self.__boutonGenerer.clicked.connect(self.generer_mem)\n\n #self.boutonGenCst = QtWidgets.QPushButton(\"Generer Constant\")\n #self.grid_layout.addWidget(self.boutonGenCst, 18, 8*self.matrix_nb + 1)\n #self.boutonGenCst.clicked.connect(self.generer_cst)\n # ========================\n\n # == DEBUG ==\n #print(dir(self.field_display_test))\n # ===========\n\n self.setWindowTitle(\"Memory Generator\")\n\n\n # Function : GetCheckboxes states\n def get_checkbox_states(self):\n for j in range(self.matrix_line_nb):\n for i in range(8*self.matrix_nb):\n item = self.grid_layout.itemAtPosition(j,i)\n self.widget = item.widget()\n self.grid[j][i] = self.widget.isChecked()\n\n \n # Function : Inversion de l'etat des checkboxes\n def inv_checkbox(self):\n for j in range(self.matrix_line_nb):\n for i in range(8*self.matrix_nb):\n item = self.grid_layout.itemAtPosition(j,i)\n widget = item.widget()\n self.grid[j][i] = widget.isChecked()\n\n if self.grid[j][i] == True :\n widget.setChecked(False)\n else:\n widget.setChecked(True)\n\n\n\n def generer_mem(self):\n print(self.grid)\n\n\n # Convert grid to a list[0:63]\n self.get_checkbox_states()\n matrix_out = np.zeros([64], int)\n\n list_out = []\n line_tmp = []\n\n for nb_line in range(0, 8):\n \n line_tmp = []\n for nb_col_i in range(0, 8*self.matrix_nb):\n if(self.grid[nb_line][nb_col_i] == True):\n line_tmp.append(1)\n else:\n line_tmp.append(0)\n list_out.append(line_tmp)\n\n \n\n print(list_out)\n # Generation of Memory file Function\n # def generer_mem(self):\n # #print(\"Generer Mem fctn\")\n\n # self.digit_7_matrix_n = []\n # self.digit_6_matrix_n = []\n # self.digit_5_matrix_n = []\n # self.digit_4_matrix_n = []\n # self.digit_3_matrix_n = []\n # self.digit_2_matrix_n = []\n # self.digit_1_matrix_n = []\n # self.digit_0_matrix_n = []\n \n # # Creates Array\n # for i in range (0, self.matrix_nb):\n # self.digit_7_matrix_n.append(\"\")\n # self.digit_6_matrix_n.append(\"\")\n # self.digit_5_matrix_n.append(\"\")\n # self.digit_4_matrix_n.append(\"\")\n # self.digit_3_matrix_n.append(\"\")\n # self.digit_2_matrix_n.append(\"\")\n # self.digit_1_matrix_n.append(\"\")\n # self.digit_0_matrix_n.append(\"\")\n \n # # == Save in self.grid the state of checkboxes\n # self.get_checkbox_states()\n \n # # == Fill Matrix af DIGITn Registers\n # for j in range (0, self.matrix_line_nb):\n # for i in range(0, self.matrix_nb):\n # self.digit_7_matrix_n[i] = self.digit_7_matrix_n[i] + (\"1\" if self.grid[j][i*8] else \"0\")\n # self.digit_6_matrix_n[i] = self.digit_6_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 1] else \"0\")\n # self.digit_5_matrix_n[i] = self.digit_5_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 2] else \"0\")\n # self.digit_4_matrix_n[i] = self.digit_4_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 3] else \"0\")\n # self.digit_3_matrix_n[i] = self.digit_3_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 4] else \"0\")\n # self.digit_2_matrix_n[i] = self.digit_2_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 5] else \"0\")\n # self.digit_1_matrix_n[i] = self.digit_1_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 6] else \"0\")\n # self.digit_0_matrix_n[i] = self.digit_0_matrix_n[i] + (\"1\" if self.grid[j][i*8 + 7] else \"0\")\n \n\n # for i in range (0, self.matrix_nb):\n # print(self.digit_7_matrix_n[i])\n # print(self.digit_6_matrix_n[i])\n # print(self.digit_5_matrix_n[i])\n # print(self.digit_4_matrix_n[i])\n # print(self.digit_3_matrix_n[i])\n # print(self.digit_2_matrix_n[i])\n # print(self.digit_1_matrix_n[i])\n # print(self.digit_0_matrix_n[i])\n\n\n #print()\n \n #self.write_file()\n\n\n # def write_file(self):\n\n # # == Data to Writes in Memory ==\n # wdata = []\n\n # # Init WDATA list\n # for i in range(0, int(self.field_memory_size.text())):\n # wdata.append(\"0000\")\n\n # for i in range (0, int(self.field_memory_size.text())): #5*self.matrix_nb):\n\n # # == Gestion Configuration Registres ==\n # if i < 1*self.matrix_nb :\n # if i == 1*self.matrix_nb - 1 :\n # wdata[i] = \"19\" + self.field_decod_mode.text()\n # else :\n # wdata[i] = \"09\" + self.field_decod_mode.text()\n \n # elif i >= 1*self.matrix_nb and i < 2*self.matrix_nb :\n # if i == 2*self.matrix_nb - 1 :\n # wdata[i] = \"1A\" + self.field_intensity.text()\n # else:\n # wdata[i] = \"0A\" + self.field_intensity.text()\n \n # elif i >= 2*self.matrix_nb and i < 3*self.matrix_nb :\n # if i == 3*self.matrix_nb - 1 :\n # wdata[i] = \"1B\" + self.field_scan_limit.text()\n # else:\n # wdata[i] = \"0B\" + self.field_scan_limit.text()\n \n # elif i >= 3*self.matrix_nb and i < 4*self.matrix_nb :\n # if i == 4*self.matrix_nb - 1 : \n # wdata[i] = \"1C\" + self.field_shutdown.text()\n # else:\n # wdata[i] = \"0C\" + self.field_shutdown.text()\n \n # elif i >= 4*self.matrix_nb and i < 5*self.matrix_nb :\n # if i == 5*self.matrix_nb - 1 :\n # wdata[i] = \"1F\" + self.field_display_test.text()\n # else:\n # wdata[i] = \"0F\" + self.field_display_test.text()\n # # ====================================\n\n # # == Gestion DIGIT pour affichage Matrix ==\n\n # # Write Digit 0\n # elif i >= 5*self.matrix_nb and i < 6*self.matrix_nb :\n # if i == 6*self.matrix_nb - 1 :\n # wdata[i] = \"11\" + format(int( (self.digit_0_matrix_n[self.matrix_nb - 1 - (i - 5*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"01\" + format(int( (self.digit_0_matrix_n[self.matrix_nb - 1 - (i - 5*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 1\n # elif i >= 6*self.matrix_nb and i < 7*self.matrix_nb :\n # if i == 7*self.matrix_nb - 1 :\n # wdata[i] = \"12\" + format(int( (self.digit_1_matrix_n[self.matrix_nb - 1 - (i - 6*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"02\" + format(int( (self.digit_1_matrix_n[self.matrix_nb - 1 - (i - 6*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 2\n # elif i >= 7*self.matrix_nb and i < 8*self.matrix_nb :\n # if i == 8*self.matrix_nb - 1 :\n # wdata[i] = \"13\" + format(int( (self.digit_2_matrix_n[self.matrix_nb - 1 - (i - 7*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"03\" + format(int( (self.digit_2_matrix_n[self.matrix_nb - 1 - (i - 7*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 3\n # elif i >= 8*self.matrix_nb and i < 9*self.matrix_nb :\n # if i == 9*self.matrix_nb - 1 :\n # wdata[i] = \"14\" + format(int( (self.digit_3_matrix_n[self.matrix_nb - 1 - (i - 8*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"04\" + format(int( (self.digit_3_matrix_n[self.matrix_nb - 1 - (i - 8*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 4\n # elif i >= 9*self.matrix_nb and i < 10*self.matrix_nb :\n # if i == 10*self.matrix_nb - 1 :\n # wdata[i] = \"15\" + format(int( (self.digit_4_matrix_n[self.matrix_nb - 1 - (i - 9*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"05\" + format(int( (self.digit_4_matrix_n[self.matrix_nb - 1 - (i - 9*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 5\n # elif i >= 10*self.matrix_nb and i < 11*self.matrix_nb :\n # if i == 11*self.matrix_nb - 1 :\n # wdata[i] = \"16\" + format(int( (self.digit_5_matrix_n[self.matrix_nb - 1 - (i - 10*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"06\" + format(int( (self.digit_5_matrix_n[self.matrix_nb - 1 - (i - 10*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 6\n # elif i >= 11*self.matrix_nb and i < 12*self.matrix_nb :\n # if i == 12*self.matrix_nb - 1 :\n # wdata[i] = \"17\" + format(int( (self.digit_6_matrix_n[self.matrix_nb - 1 - (i - 11*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"07\" + format(int( (self.digit_6_matrix_n[self.matrix_nb - 1 - (i - 11*self.matrix_nb)]), 2), '02x')\n\n # # Write Digit 7\n # elif i >= 12*self.matrix_nb and i < 13*self.matrix_nb :\n # if i == 13*self.matrix_nb - 1 :\n # wdata[i] = \"18\" + format(int( (self.digit_7_matrix_n[self.matrix_nb - 1 - (i - 12*self.matrix_nb)]), 2), '02x')\n # else:\n # wdata[i] = \"08\" + format(int( (self.digit_7_matrix_n[self.matrix_nb - 1 - (i - 12*self.matrix_nb)]), 2), '02x')\n \n \n # =========================================\n # =============================\n \n \n\n # f = open(self.field_file_name.text(), \"w\")\n # f.writelines(\"// memory data file (do not edit the following line - required for mem load use)\\n\")\n # f.writelines(\"// instance=\" + self.field_instance_path.text() + \"\\n\")\n # f.writelines(\"// format=mti addressradix=d dataradix=h version=1.0 wordsperline=1\\n\")\n # for i in range(0, int(self.field_memory_size.text())):\n # # /!\\ : Ajout des espaces pas bien gere\n # if len(str(i)) == 1 :\n # f.writelines(\" \" + str(i) + \": \" + wdata[i] + \"\\n\")\n # elif len(str(i)) == 2 :\n # f.writelines(\" \" + str(i) + \": \" + wdata[i] + \"\\n\")\n # else :\n # f.writelines(str(i) + \": \" + wdata[i] + \"\\n\")\n \n \n # f.close()\n\n # Generation of a VHD file with a Constant wich contain the pattern of the Matrix\n # def generer_cst(self):\n # print(\"Generer Constant Def\")\n # data_array = []\n # data_array_int = []\n # data_array_final = []\n # tmp = 0\n # #offset = 7# Initial Offset\n\n # # INIT Array\n # for i in range(8*self.matrix_nb):\n # data_array_int.append(\"\")\n # data_array_final.append(\"\")\n \n # #print(\"data array int : %s \\n\" % (data_array_int) )\n \n # self.get_checkbox_states()\n # for i in range(8*self.matrix_nb):\n # data_array.append(\"\")\n # #data_array_int.append(\"\")\n # data_array[i] = str(int(self.grid[0][i])) + str(int(self.grid[1][i])) + str(int(self.grid[2][i])) + str(int(self.grid[3][i])) \n # data_array[i] = data_array[i] + str(int(self.grid[4][i])) + str(int(self.grid[5][i])) + str(int(self.grid[6][i])) + str(int(self.grid[7][i]))\n\n \n # tmp = (int(self.grid[0][i]) << 7) | (int(self.grid[1][i]) << 6) | (int(self.grid[2][i]) << 5) | (int(self.grid[3][i]) << 4) \n # data_array_int[i] = hex(tmp | (int(self.grid[4][i]) << 3) | (int(self.grid[5][i]) << 2) | (int(self.grid[6][i]) << 1 ) | (int(self.grid[7][i])))\n # #print(\"tmp : %s \\n\\n\" %(tmp) )\n # tmp = 0\n \n # print(\"data array int for SCROLLER RAM: %s \\n\" % (data_array_int) )\n # #print(type(data_array_int[0]))\n\n # #print(\"data array : %s \\n \" %(data_array) )\n \n # f = open(\"constant_gen.vhd\", \"w\")\n # f.writelines(\"type t_cst_array is array (0 to %d) of std_logic_vector(7 downto 0);\\n\" %(self.matrix_nb * 8 - 1))\n # f.writelines(\"constant C_CST_0 : t_cst_array := (\\n\")\n # for i in range(0, self.matrix_nb * 8):\n # if (i < self.matrix_nb * 8 - 1):\n # f.writelines(\" %s => x\\\"%s\\\",\\n\" %(i , format(int(data_array[i], 2), '02x') ) )\n # else:\n # f.writelines(\" %s => x\\\"%s\\\"\\n\" %(i , format(int(data_array[i], 2), '02x') ) )\n # f.writelines(\");\") \n # f.close()\n\n # # Data array final update\n # #offset = 0\n # #for i in range(8*self.matrix_nb):\n # #data_array_final[i*8 + 7 - offset] = str(data_array_int[i])\n # #print(\"i : %d\\n\" %(i) )\n # #print(\"offset : %d\\n\" %(offset) )\n # #print(\"(offset*8 - 1) - i : %d\\n\" %((offset*8 - 1) - i) )\n # #if i == :\n # # offset = (i*8 - 1)\n\n\n\n # #print(data_array_final)\n\n # digit_7_matrix_n = []\n # digit_6_matrix_n = []\n # digit_5_matrix_n = []\n # digit_4_matrix_n = []\n # digit_3_matrix_n = []\n # digit_2_matrix_n = []\n # digit_1_matrix_n = []\n # digit_0_matrix_n = []\n \n # # Creates Array\n # for i in range (0, self.matrix_nb):\n # digit_7_matrix_n.append(\"\")\n # digit_6_matrix_n.append(\"\")\n # digit_5_matrix_n.append(\"\")\n # digit_4_matrix_n.append(\"\")\n # digit_3_matrix_n.append(\"\")\n # digit_2_matrix_n.append(\"\")\n # digit_1_matrix_n.append(\"\")\n # digit_0_matrix_n.append(\"\")\n \n # # == Save in self.grid the state of checkboxes\n # self.get_checkbox_states()\n # tmp_d7 = 0\n # tmp_d6 = 0\n # tmp_d5 = 0\n # tmp_d4 = 0\n # tmp_d3 = 0\n # tmp_d2 = 0\n # tmp_d1 = 0\n # tmp_d0 = 0\n \n # # == Fill Matrix af DIGITn Registers\n # for j in range (0, self.matrix_nb):\n\n\n # # Get Collumn 0 (D7 (Mi))\n # tmp_d7 = (int(self.grid[0][j*8]) << 7) | (int(self.grid[1][j*8]) << 6) | (int(self.grid[2][j*8]) << 5) | (int(self.grid[3][j*8]) << 4) \n # tmp_d7 = hex(tmp_d7 | (int(self.grid[4][j*8]) << 3) | (int(self.grid[5][j*8]) << 2) | (int(self.grid[6][j*8]) << 1 ) | (int(self.grid[7][j*8])))\n # digit_7_matrix_n[self.matrix_nb - 1 - j] = tmp_d7\n\n # #print(\"tmp_d7 : %s \\n\" %(tmp_d7) )\n\n \n # # Get Collumn 1 (D6 (Mi))\n # tmp_d6 = (int(self.grid[0][j*8 + 1]) << 7) | (int(self.grid[1][j*8 + 1]) << 6) | (int(self.grid[2][j*8 + 1]) << 5) | (int(self.grid[3][j*8 + 1]) << 4) \n # tmp_d6 = hex(tmp_d6 | (int(self.grid[4][j*8 + 1]) << 3) | (int(self.grid[5][j*8 + 1]) << 2) | (int(self.grid[6][j*8 + 1]) << 1 ) | (int(self.grid[7][j*8 + 1])))\n # digit_6_matrix_n[self.matrix_nb - 1 - j] = tmp_d6\n\n # #print(\"tmp_d6 : %s \\n\" %(tmp_d6) )\n\n # # Get Collumn 2 (D5 (Mi))\n # tmp_d5 = (int(self.grid[0][j*8 + 2]) << 7) | (int(self.grid[1][j*8 + 2]) << 6) | (int(self.grid[2][j*8 + 2]) << 5) | (int(self.grid[3][j*8 + 2]) << 4) \n # tmp_d5 = hex(tmp_d5 | (int(self.grid[4][j*8 + 2]) << 3) | (int(self.grid[5][j*8 + 2]) << 2) | (int(self.grid[6][j*8 + 2]) << 1 ) | (int(self.grid[7][j*8 + 2])))\n # digit_5_matrix_n[self.matrix_nb - 1 - j] = tmp_d5\n\n # # Get Collumn 3 (D4 (Mi))\n # tmp_d4 = (int(self.grid[0][j*8 + 3]) << 7) | (int(self.grid[1][j*8 + 3]) << 6) | (int(self.grid[2][j*8 + 3]) << 5) | (int(self.grid[3][j*8 + 3]) << 4) \n # tmp_d4 = hex(tmp_d4 | (int(self.grid[4][j*8 + 3]) << 3) | (int(self.grid[5][j*8 + 3]) << 2) | (int(self.grid[6][j*8 + 3]) << 1 ) | (int(self.grid[7][j*8 + 3])))\n # digit_4_matrix_n[self.matrix_nb - 1 - j] = tmp_d4\n\n # # Get Collumn 4 (D3 (Mi))\n # tmp_d3 = (int(self.grid[0][j*8 + 4]) << 7) | (int(self.grid[1][j*8 + 4]) << 6) | (int(self.grid[2][j*8 + 4]) << 5) | (int(self.grid[3][j*8 + 4]) << 4) \n # tmp_d3 = hex(tmp_d3 | (int(self.grid[4][j*8 + 4]) << 3) | (int(self.grid[5][j*8 + 4]) << 2) | (int(self.grid[6][j*8 + 4]) << 1 ) | (int(self.grid[7][j*8 + 4])))\n # digit_3_matrix_n[self.matrix_nb - 1 - j] = tmp_d3\n\n # # Get Collumn 5 (D2 (Mi))\n # tmp_d2 = (int(self.grid[0][j*8 + 5]) << 7) | (int(self.grid[1][j*8 + 5]) << 6) | (int(self.grid[2][j*8 + 5]) << 5) | (int(self.grid[3][j*8 + 5]) << 4) \n # tmp_d2 = hex(tmp_d2 | (int(self.grid[4][j*8 + 5]) << 3) | (int(self.grid[5][j*8 + 5]) << 2) | (int(self.grid[6][j*8 + 5]) << 1 ) | (int(self.grid[7][j*8 + 5])))\n # digit_2_matrix_n[self.matrix_nb - 1 - j] = tmp_d2\n\n # # Get Collumn 6 (D1 (Mi))\n # tmp_d1 = (int(self.grid[0][j*8 + 6]) << 7) | (int(self.grid[1][j*8 + 6]) << 6) | (int(self.grid[2][j*8 + 6]) << 5) | (int(self.grid[3][j*8 + 6]) << 4) \n # tmp_d1 = hex(tmp_d1 | (int(self.grid[4][j*8 + 6]) << 3) | (int(self.grid[5][j*8 + 6]) << 2) | (int(self.grid[6][j*8 + 6]) << 1 ) | (int(self.grid[7][j*8 + 6])))\n # digit_1_matrix_n[self.matrix_nb - 1 - j] = tmp_d1\n\n # # Get Collumn 7 (D0 (Mi))\n # tmp_d0 = (int(self.grid[0][j*8 + 7]) << 7) | (int(self.grid[1][j*8 + 7]) << 6) | (int(self.grid[2][j*8 + 7]) << 5) | (int(self.grid[3][j*8 + 7]) << 4) \n # tmp_d0 = hex(tmp_d0 | (int(self.grid[4][j*8 + 7]) << 3) | (int(self.grid[5][j*8 + 7]) << 2) | (int(self.grid[6][j*8 + 7]) << 1 ) | (int(self.grid[7][j*8 + 7])))\n # digit_0_matrix_n[self.matrix_nb - 1 - j] = tmp_d0\n\n \n # tmp_d7 = 0\n # tmp_d6 = 0\n # tmp_d5 = 0\n # tmp_d4 = 0\n # tmp_d3 = 0\n # tmp_d2 = 0\n # tmp_d1 = 0\n # tmp_d0 = 0\n\n # #print(\"\\n digit_7_matrix_n : %s \\n\" % (digit_7_matrix_n) )\n # #print(\"\\n digit_6_matrix_n : %s \\n\" % (digit_6_matrix_n) )\n # #print(\"\\n digit_5_matrix_n : %s \\n\" % (digit_5_matrix_n) )\n # #print(\"\\n digit_4_matrix_n : %s \\n\" % (digit_4_matrix_n) )\n # #print(\"\\n digit_3_matrix_n : %s \\n\" % (digit_3_matrix_n) )\n # #print(\"\\n digit_2_matrix_n : %s \\n\" % (digit_2_matrix_n) )\n # #print(\"\\n digit_1_matrix_n : %s \\n\" % (digit_1_matrix_n) )\n # #print(\"\\n digit_0_matrix_n : %s \\n\" % (digit_0_matrix_n) )\n \n # final_array = []\n\n # for i in range(0, 8*self.matrix_nb):\n # final_array.append(\"\")\n\n # for i in range(0, self.matrix_nb):\n # final_array[i] = digit_0_matrix_n[i]\n # final_array[i + 1*self.matrix_nb] = digit_1_matrix_n[i]\n # final_array[i + 2*self.matrix_nb] = digit_2_matrix_n[i]\n # final_array[i + 3*self.matrix_nb] = digit_3_matrix_n[i]\n # final_array[i + 4*self.matrix_nb] = digit_4_matrix_n[i]\n # final_array[i + 5*self.matrix_nb] = digit_5_matrix_n[i]\n # final_array[i + 6*self.matrix_nb] = digit_6_matrix_n[i]\n # final_array[i + 7*self.matrix_nb] = digit_7_matrix_n[i]\n\n\n # print(\"\\n\\nFinal array for STATIC RAM : %s\\n\" %(final_array))\n # #print(len(final_array))\n \n \n # # Save the status of checkboxes in order to load a specific pattern\n # def save_state_checkboxes(self):\n # print(\"Save States checkboxes\")\n\n\n\napp = QtWidgets.QApplication(sys.argv)\ndialog = window()\ndialog.exec_()\n","sub_path":"UART/scripts/model_interface_web/mem_gen.py","file_name":"mem_gen.py","file_ext":"py","file_size_in_byte":25048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"541140193","text":"\n\nfrom xai.brain.wordbase.nouns._brontosaurus import _BRONTOSAURUS\n\n#calss header\nclass _BRONTOSAURUSES(_BRONTOSAURUS, ):\n\tdef __init__(self,): \n\t\t_BRONTOSAURUS.__init__(self)\n\t\tself.name = \"BRONTOSAURUSES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"brontosaurus\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_brontosauruses.py","file_name":"_brontosauruses.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"577954106","text":"#/usr/bin/python\nfrom __future__ import print_function\nimport sys\nimport time\nimport rosbag\nfrom sets import Set\nimport numpy as np\nimport subprocess, yaml\nimport os\nimport xml.etree.ElementTree as ET\nimport re\nfrom xml.dom import minidom\n\ndef determine(xml_tree):\n\tcomment_div = \"\" % \"other\"\n\tnew_tree = ET.ElementTree()\n\tnew_root = ET.Element('root')\n\tnew_tree._setroot(new_root)\n\troot = xml_tree.getroot()\n\tfor child in root.iter(\"PPT\"):\n\t\tprint(child.find(\"PPTNAME\").text)\n\t\tans = raw_input(\"Useful? y/n: \")\n\t\tif('n' in ans):\n\t\t\troot.remove(child)\n\treturn xml_tree\n\ndef main():\n\t#first arg: property file\n\t# second arg: \n\toutput_filename=sys.argv[1].split(\".\")\n\txml_filehead=output_filename[0]\n\n\t#handle CL args\n\tregrouped_filename=xml_filehead+\"_usefulppts.xml\"\n\n\t#process .inv xml\n\tprint(\"Parsing \"+str(sys.argv[1]))\n\txml_tree = ET.parse(sys.argv[1])\n\tregrouped_xml_tree = determine(xml_tree)\n\troot = regrouped_xml_tree.getroot()\n\t#regrouped_xml_string.write(regrouped_filename, encoding=\"utf-8\", pretty_print=True)\n\n\txmlstr = minidom.parseString(ET.tostring(root)).toprettyxml(indent=\" \")\n\twith open(regrouped_filename, \"w\") as f:\n\t\tf.write(xmlstr)\n\tprint(\"Regrouped .xml file: \"+regrouped_filename)\n\nif __name__ == \"__main__\":\n # execute only if run as a script \n main()\n","sub_path":"clean_bags/determine_useful_ppts.py","file_name":"determine_useful_ppts.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"364959226","text":"import subprocess\nimport pynvim\nimport requests\nimport json\n\n\n@pynvim.plugin\nclass Hub(object):\n def __init__(self, nvim):\n self.nvim = nvim\n self.tmp_ctags_path = \"/tmp/{}.tags\"\n self.server_url = 'http://localhost:1337/{}'\n self.terminal_buf_id = 0\n\n @pynvim.autocmd('BufEnter', pattern='*.hub', sync=True)\n def autocmd_bufenter(self):\n \"\"\"\n This function is primarily for generating and loading ctags to the\n current nvim instance to enable convenient way to use our applications\n \"\"\"\n try:\n # Asking the server for installed applications\n r = requests.get(self.server_url.format('applications'))\n loaded_applications = r.json()\n for application in loaded_applications:\n ctags_path = self.tmp_ctags_path.format(application)\n self.generate_absolute_python_ctags(loaded_applications[application], ctags_path)\n self.load_ctags(ctags_path)\n except Exception as e:\n self.out(\"Hub: exception ({})\".format(str(e)))\n\n @pynvim.function('HubFunc')\n def function_handler(self, args):\n try:\n # Extract all information we might need.\n nvim_buffer = self.nvim.current.buffer\n\n # Current possion\n cursor_line = self.nvim.call('line', '.')\n cursor_column = self.nvim.call('col', '.')\n quickfix = self.nvim.call('getqflist')\n\n # Selection\n selected_start_line = self.nvim.call('getpos',\"'<\")[1]\n selected_start_column = self.nvim.call('getpos',\"'<\")[2]\n selected_end_line = self.nvim.call('getpos',\"'>\")[1]\n selected_end_column = self.nvim.call('getpos',\"'>\")[2]\n\n json_to_send = {\n 'nvim_buffer': '\\n'.join(nvim_buffer[:]),\n 'cursor_line': cursor_line,\n 'cursor_column': cursor_column,\n 'selected_start_line': selected_start_line,\n 'selected_start_column': selected_start_column,\n 'selected_end_line': selected_end_line,\n 'selected_end_column': selected_end_column\n }\n r = requests.post(self.server_url.format('execute'), json=json_to_send)\n self.out(str(r.status_code))\n self.out(str(r.text))\n except Exception as e:\n self.out(\"Hub: exception ({})\".format(str(e)))\n\n @pynvim.function('HubLaunchTerminal')\n def spawn_terminal_handler(self, args):\n try:\n command = args[0]\n\n # split to spawn te terminal\n self.nvim.command(\"split\")\n # create new buffer to put the terminal in.\n self.nvim.command(\"enew\")\n\n # keep track of the terminal buffer id\n self.terminal_buf_id = self.nvim.call('bufnr','%')\n\n # launch terminal with on_exit handler\n termopen_args = {'on_exit': 'HubTerminalOnExit'}\n self.nvim.call('termopen', command, termopen_args)\n\n # enter insert mode int terminal\n self.nvim.command(\"normal i\")\n except Exception as e:\n self.out(\"Hub: exception ({})\".format(str(e)))\n\n @pynvim.function('HubTerminalOnExit')\n def terminal_on_exit_handler(self, args):\n job_id = args[0]\n code = args[1]\n event = args[2]\n terminal_lines = self.nvim.call('nvim_buf_get_lines', self.terminal_buf_id, 0, -1, False)\n\n # self.out('\\n'.join(terminal_lines))\n self.nvim.command(\"close\")\n\n def out(self, message):\n message = message.replace(\"\\\"\", \"\\\\\\\"\")\n self.nvim.command(f\"echo \\\"{message}\\\"\")\n\n def set_quickfix_list(self, quickfix_list):\n \"\"\"\n [\n {\n 'filename':'',\n 'lnum': '',\n 'text': ''\n }\n ]\n \"\"\"\n self.nvim.call('setqflist', quickfix_list)\n\n def modify_buffer(self, buffer_number, lines):\n self.nvim.call('nvim_buf_set_lines', buffer_number, 0, -1, 0, lines)\n\n def load_ctags(self, ctags_path):\n self.nvim.command(f\"set tags+={ctags_path}\")\n\n def generate_absolute_python_ctags(self, project_path, ctags_path):\n p = subprocess.Popen(\n [\n \"ctags\",\n \"--python-kinds=-i\",\n \"-f\",\n ctags_path,\n \"-R\",\n project_path\n ], \n cwd=\"/\")\n p.wait()\n\n def generate_absolute_ctags(self, project_path, ctags_path):\n p = subprocess.Popen(\n [\n \"ctags\",\n \"-f\",\n ctags_path,\n \"-R\",\n project_path\n ], \n cwd=\"/\")\n p.wait()\n","sub_path":"vim_client/hub.py","file_name":"hub.py","file_ext":"py","file_size_in_byte":4892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"90077767","text":"#===================================================================#\n# Tool Name: FVS Test Module #\n# Version: 0.2 #\n# Edit by: Chris Liu 2017/05/27 #\n#===================================================================#\nfrom Module.common import *\n\nglobal TOOL_NAME\nTOOL_NAME = \"FVS Test Module\"\n\nglobal VER\nVER = \"0.2\"\n#===============================================================================\ndef Check_LED():\n\t'''Visual Check LEDs Function'''\n\n\tflag = False\n\n\tret = input(\"Press y/n for LED Function!!\\n\").strip().lower()\n\n\tif(ret == \"y\"):\n\t\tflag = True\n\n\tif(flag):\n\t\tLog(\"Check_LED Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tLog(\"Check_LED Fail\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef Check_BIOS():\n\t'''Check BIOS Version'''\n\n\tflag = False\n\n\tcmd = \"%s \\\"Get-WmiObject %s | format-list\\\"\"%(POWERSHELL, \"Win32_BIOS\")\n\tret = Input_CMD_OS(cmd)\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in ret:\n\t\tif(\"SMBIOSBIOSVersion\" in i and \"1.4.17\" in i):\n\t\t\tflag = True\n\n\tif(flag):\n\t\tLog(\"Check_BIOS Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tLog(\"Check_BIOS Fail\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef Check_Service_Tag():\n\t'''Check Service Tag'''\n\n\tflag = False\n\n\tcmd = \"%s \\\"Get-WmiObject %s | format-list\\\"\"%(POWERSHELL, \"Win32_BIOS\")\n\tret = Input_CMD_OS(cmd)\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in ret:\n\t\tif(\"SerialNumber\" in i and \"H2C1GC2\" in i):\n\t\t\tflag = True\n\n\tif(flag):\n\t\tLog(\"Check_Service_Tag Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tLog(\"Check_Service_Tag Fail\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef main():\n\ttest_items = [\n\t\t(\"Check_BIOS\", Check_BIOS),\n\t\t(\"Check_Service_Tag\", Check_Service_Tag),\n\t\t(\"Check_LED\", Check_LED),\n\t]\n\n\ttest_sequence = Argument_Parser(TOOL_NAME, VER, OrderedDict(test_items))\n\n\tBanner(\"%s, By Foxconn CESBG-EPDI-TE, Version: %s\"%(TOOL_NAME, VER))\n\tGet_PPID()\n\tConfig_Parser(VER)\n\n\tFVS_Test(test_sequence)\n#===============================================================================\nif(__name__ == \"__main__\"):\n\ttry:\n\t\tmain()\n\texcept Exception as e:\n\t\tprint(\"ERROR: %s\"%(str(e)))\n\t\tsys.exit(-1)\n\tsys.exit(0)\n","sub_path":"FVS/TEST.py","file_name":"TEST.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"181056717","text":"import data_loader as dl\nimport matplotlib.pyplot as plt\n\n\nfolder = '/home/shenghua/dl-cell-counting/mt-cell-train-data/dataset/'\n\n#imageFileName = folder + 'imageSet.dat'\n#densityFileName = folder + 'densitySet.dat'\n\nimageFileName = folder + 'realImages.dat'\ndensityFileName = folder + 'realDensities.dat'\n\n# x = dl.train_load(imageFileName)\n# y = dl.truth_load(densityFileName)\n\nx = dl.train_data_load(imageFileName,(512,512),10)\ny = dl.truth_data_load(densityFileName,(512,512),10)\n\nfig = plt.figure()\n\nax = fig.add_subplot(1,2,1)\nax.imshow(x[0,:,:])\nax.set_title('Synthetic images')\nax = fig.add_subplot(1,2,2)\nax.imshow(y[0,:,:])\nax.set_title('Density map')\nplt.show()\n\n\nplt.ion()\nfig = plt.figure()\nfor i in range(len(x)):\n\tax = fig.add_subplot(1,2,1)\n\tax.imshow(x[i])\n\tax = fig.add_subplot(1,2,2)\n\tax.imshow(y[i])\n\tplt.pause(0.5)\n\t\nimport keras.backend as K\nlr=0.0005\nmodel=cg.layer9_cnn()\nsgd = SGD(lr=lr, decay=1e-4, momentum=0.9, nesterov=True)\nmodel.compile(loss='mean_squared_error', optimizer=sgd)\n# model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])\n","sub_path":"dl-counting/data_check.py","file_name":"data_check.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"571952962","text":"import xlrd\nimport csv\n\n# CONSTANTS\nCUST_CODE = 2\nKRA_PIN = 5\nEMAIL = 12\nPHONE = 13\n\n\ndef read_data():\n workbook = xlrd.open_workbook(\"./customerdata.xls\")\n sheet = workbook.sheet_by_index(0)\n data = [sheet.row_values(rowx) for rowx in range(sheet.nrows)]\n return data\n\n\ndef find_duplicate_customer_pins(data):\n ALL_CUSTOMERS_BY_PIN = {}\n # DUPLICATE_CUSTOMERS_BY_PIN = {}\n\n for d in data:\n if(d[KRA_PIN] is not \"\"):\n if d[KRA_PIN] in ALL_CUSTOMERS_BY_PIN:\n ALL_CUSTOMERS_BY_PIN[d[KRA_PIN]].append(d[CUST_CODE])\n # if d[KRA_PIN] in DUPLICATE_CUSTOMERS_BY_PIN:\n # DUPLICATE_CUSTOMERS_BY_PIN[d[KRA_PIN]] += 1\n # else:\n # DUPLICATE_CUSTOMERS_BY_PIN[d[KRA_PIN]] = 2\n else:\n ALL_CUSTOMERS_BY_PIN[d[KRA_PIN]] = [d[CUST_CODE]]\n\n return ALL_CUSTOMERS_BY_PIN\n\n\ndef find_duplicate_customer_codes(data):\n ALL_CUSTOMERS_BY_CUST_CODE = {}\n DUPLICATE_CUSTOMER_CODES = []\n\n for d in data:\n if d[CUST_CODE] in ALL_CUSTOMERS_BY_CUST_CODE:\n DUPLICATE_CUSTOMER_CODES.append(d[CUST_CODE])\n else:\n ALL_CUSTOMERS_BY_CUST_CODE[d[CUST_CODE]] = d\n\n return DUPLICATE_CUSTOMER_CODES\n\n\ndef find_duplicate_emails(data):\n ALL_CUSTOMERS_BY_EMAIL = {}\n DUPLICATE_EMAIL_ADDRESSES = []\n DUPLICATE_RESULTS = {}\n\n for d in data:\n split_emails = d[EMAIL].split(',')\n for email in split_emails:\n if email in ALL_CUSTOMERS_BY_EMAIL:\n if email not in DUPLICATE_EMAIL_ADDRESSES:\n DUPLICATE_EMAIL_ADDRESSES.append(email)\n ALL_CUSTOMERS_BY_EMAIL[email].append(d)\n else:\n ALL_CUSTOMERS_BY_EMAIL[email] = [d]\n\n for dup in DUPLICATE_EMAIL_ADDRESSES:\n curDup = ALL_CUSTOMERS_BY_EMAIL[dup]\n for customer in curDup:\n for cust2 in curDup:\n if cust2 is customer:\n continue\n\n if customer[2] not in DUPLICATE_RESULTS:\n DUPLICATE_RESULTS[customer[2]] = []\n\n if cust2[2] not in DUPLICATE_RESULTS[customer[2]]:\n DUPLICATE_RESULTS[customer[2]].append(cust2[2])\n\n return DUPLICATE_RESULTS\n\n\ndef find_duplicate_phone_numbers(data):\n ALL_CUSTOMERS_BY_PHONE = {}\n DUPLICATE_PHONE_NUMBERS = []\n DUPLICATE_RESULTS = {}\n\n for d in data:\n split_phone_numbers = d[PHONE].split(',')\n for phone in split_phone_numbers:\n if phone in ALL_CUSTOMERS_BY_PHONE:\n if phone not in DUPLICATE_PHONE_NUMBERS:\n DUPLICATE_PHONE_NUMBERS.append(phone)\n ALL_CUSTOMERS_BY_PHONE[phone].append(d)\n else:\n ALL_CUSTOMERS_BY_PHONE[phone] = [d]\n\n for dup in DUPLICATE_PHONE_NUMBERS:\n curDup = ALL_CUSTOMERS_BY_PHONE[dup]\n for customer in curDup:\n for cust2 in curDup:\n if cust2 is customer:\n continue\n\n if customer[2] not in DUPLICATE_RESULTS:\n DUPLICATE_RESULTS[customer[2]] = []\n\n if cust2[2] not in DUPLICATE_RESULTS[customer[2]]:\n DUPLICATE_RESULTS[customer[2]].append(cust2[2])\n\n # for dup in DUPLICATE_RESULTS:\n # print(str(dup))\n\n return DUPLICATE_RESULTS\n\n\ndef output(pins, codes, emails, phones):\n output = \">>> Below are duplicate customers based on their customer codes, emails and phone numbers\"\n\n output += \"\\n\\n>>> Duplicate Customers by Customer KRA Pin\\n\\n\"\n for pin in pins:\n output += pin + \"\\n\"\n\n # output += \"\\n\\n>>> Duplicate Customers by Customer Codes\\n\\n\"\n # for code in codes:\n # output += code + \"\\n\"\n\n # output += \"\\n\\n>>> Duplicate Customers by Email Address\\n\\n\"\n # for email in emails:\n # output += email + \": \" + str(emails[email]) + \"\\n\"\n\n # output += \"\\n\\n>>> Duplicate Customers by Phone Numbers\\n\\n\"\n # for phone in phones:\n # output += phone + \": \" + str(phones[phone]) + \"\\n\"\n\n text_file = open(\"duplicates.txt\", \"w\")\n text_file.write(output)\n text_file.close()\n\n\ndef unique_duplicates(emails, phones):\n print('unique duplicates')\n UNIQUE_DUPLICATES = {}\n for email in emails:\n if email not in UNIQUE_DUPLICATES:\n UNIQUE_DUPLICATES[email] = emails[email]\n\n for phone in phones:\n if phone not in UNIQUE_DUPLICATES:\n UNIQUE_DUPLICATES[phone] = phones[phone]\n\n output = \">>> Unique Duplicates across Email and Phone\\n\\n\"\n for dup in UNIQUE_DUPLICATES:\n output += dup + \": \" + str(UNIQUE_DUPLICATES[dup]) + \"\\n\"\n\n text_file = open(\"unique_duplicates.txt\", \"w\")\n text_file.write(output)\n text_file.close()\n\n\ndef print_duplicate_customers_by_pins(customersByKRA):\n with open('duplicate_by_kra.csv', mode='w') as csv_file:\n fieldnames = ['kra_pin', 'cust_1', 'cust_2', 'cust_3', 'cust_4', 'cust_5', 'cust_6', 'cust_7', 'cust_8', 'cust_9', 'cust_10',\n 'cust_11', 'cust_12', 'cust_13', 'cust_14', 'cust_15', 'cust_16', 'cust_17', 'cust_18', 'cust_19', 'cust_20', 'cust_21', 'cust_22', 'cust_23']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n\n writer.writeheader()\n for kra in customersByKRA:\n data = {}\n data['kra_pin'] = kra\n for dup in range(0, 22):\n keyName = 'cust_' + str(dup+1)\n if len(customersByKRA[kra]) < dup+1:\n data[keyName] = \"\"\n else:\n data[keyName] = customersByKRA[kra][dup]\n writer.writerow(data)\n\n\ndef __main__():\n print('>>> read data')\n data = read_data()\n\n print('>>> find duplicate customer codes')\n # duplicate_customer_by_codes = find_duplicate_customer_codes(data)\n # RESULTS FOR ALL DUPLICATE CUSTOMER CODES: ['CCP0013263', 'CKS0000112', 'CLU0003919', 'CGI0000436', 'CMT0001267', 'CAI0000001', 'CFR0000422', 'CFN0000337', 'CFN0000896']\n\n print('>>> find duplicate customer pins')\n duplicate_customer_by_pins = find_duplicate_customer_pins(data)\n print_duplicate_customers_by_pins(duplicate_customer_by_pins)\n\n # print('>>> find duplicate email addresses')\n # duplicate_customers_by_emails = find_duplicate_emails(data)\n\n # print('>>> find duplicate phone numbers')\n # duplicate_customers_by_phone = find_duplicate_phone_numbers(data)\n\n # output(duplicate_customer_by_pins, duplicate_customer_by_codes,\n # duplicate_customers_by_emails, duplicate_customers_by_phone)\n\n # unique_duplicates(duplicate_customers_by_emails,\n # duplicate_customers_by_phone)\n print('>>> finished')\n\n\n__main__()\n\n# ALLEMAILS = {}\n# REPEATEDEMAILS = {}\n\n# for d in data:\n# splitEmails = d[EMAIL].split(\",\")\n# # print(splitEmails)\n# for email in splitEmails:\n# email = email.lower()\n# if email.find('purchases@silverstone.co.ke') >= 0:\n# print(str(d[CUST_CODE]) + ' ' + str(d[EMAIL]))\n\n# # if email in ALLEMAILS:\n# # # print('d[custcode]: ' + d[CUST_CODE] +\n# # # ' ALLEMAILS[email]: ' + ALLEMAILS[email][CUST_CODE])\n# # if d[CUST_CODE] is not ALLEMAILS[email][CUST_CODE]:\n# # print('cust codes dont match? ' +\n# # d[CUST_CODE] + ' ' + ALLEMAILS[email][CUST_CODE] + ' ' + email)\n# # # REPEATEDEMAILS[email] = d\n# # # # print('repeated emails: ' + email)\n# # else:\n# # print('cust codes match? ' +\n# # d[CUST_CODE] + ' ' + ALLEMAILS[email][CUST_CODE] + ' ' + email)\n# # # # same customer code, don't add\n# # # print('same cust code: ' + email)\n# # else:\n# # ALLEMAILS[email] = d\n# # ALLEMAILS\n\n# print(data[1][EMAIL])\n\n\n# thoughts:\n# - most accounts have multiple phone numbers or multiple email addresses\n# - many accounts have multiple people from the same company - is there a\n# way to handle companies differently if there's one \"bill\"\n# - some customers like CFN0000896 are duplicate entries (Silverstone Tyres (K) LTD)\n","sub_path":"sortByKra/sortCustomers.py","file_name":"sortCustomers.py","file_ext":"py","file_size_in_byte":8217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"332695510","text":"import bpy\r\nimport os\r\nimport logging\r\nimport math\r\nimport bmesh\r\nimport copy\r\ndef alert_error(title,message):\r\n def draw(self,context):\r\n self.layout.label(text=str(message))\r\n bpy.context.window_manager.popup_menu(draw,title=title,icon='ERROR')\r\n\r\nzero_roll_list=[\"Head\",\"Neck_Middle\",\"Neck\",\"LowerBody\",\"UpperBody\",\"UpperBody2\",\"spine.001\",\"UpperBody3\",\"Leg_L\",\"Leg_R\",\"Knee_L\",\"Knee_R\",\"Ankle_L\",\"Ankle_R\",\"toe.L\",\"toe.R\"]\r\n\r\n\r\n\r\ndef quad(rad):\r\n degree=rad*180/math.pi\r\n degree= degree % 360\r\n if degree <= 90:\r\n Quadrant = 1\r\n else:\r\n if degree <= 180:\r\n Quadrant = 2\r\n else:\r\n if degree <= 270:\r\n Quadrant = 3\r\n else:\r\n if degree <= 360:\r\n Quadrant = 4\r\n\r\n return Quadrant\r\n\r\ndef check_arm():\r\n global mmd_arm\r\n global mmd_bones_list\r\n\r\n mmd_arm=bpy.context.object\r\n have_rigify=False\r\n for addon in bpy.context.preferences.addons:\r\n if addon.module==\"rigify\":\r\n have_rigify=True\r\n if mmd_arm==None:\r\n logging.info(\"未选择骨骼！\")\r\n alert_error(\"提示\",\"未选择骨骼！\")\r\n return(False)\r\n if have_rigify==False:\r\n logging.info(\"检测到未开启rigify，已自动开启\")\r\n alert_error(\"提示\",\"检测到未开启rigify，已自动开启\")\r\n bpy.ops.preferences.addon_enable(module=\"rigify\")\r\n if \"_arm\" not in mmd_arm.name:\r\n logging.info(\"所选对象不是MMD骨骼！\")\r\n alert_error(\"提示\",\"所选对象不是MMD骨骼！\")\r\n return(False)\r\n elif mmd_arm.parent==None:\r\n logging.info(\"所选对象不是MMD骨骼！\")\r\n alert_error(\"提示\",\"所选对象不是MMD骨骼！\")\r\n return(False) \r\n elif mmd_arm.name.replace(\"_arm\",\"\") != mmd_arm.parent.name:\r\n logging.info(\"所选对象不是MMD骨骼！\")\r\n alert_error(\"提示\",\"所选对象不是MMD骨骼！\")\r\n return(False) \r\n bpy.ops.mmd_tools.translate_mmd_model(dictionary='INTERNAL', types={'BONE'}, modes={'MMD', 'BLENDER'})\r\n mmd_bones_list=mmd_arm.data.bones.keys()\r\n if \"UpperBodyB\" in mmd_bones_list:\r\n mmd_arm.data.bones[\"UpperBodyB\"].name=\"UpperBody2\"\r\n mmd_bones_list=mmd_arm.data.bones.keys()\r\n\r\n return (True)\r\n\r\n#Outdated methods\r\ndef fix_axial_simple():\r\n\r\n global mmd_arm\r\n global mmd_bones_list\r\n fix_bone_list=[\r\n \"Thumb0_L\",\"Thumb1_L\",\"Thumb2_L\",\"Thumb0_R\",\"Thumb1_R\",\"Thumb2_R\",\"IndexFinger1_L\",\"IndexFinger1_R\",\"IndexFinger2_L\",\"IndexFinger2_R\",\"IndexFinger3_L\",\"IndexFinger3_R\",\r\n \"MiddleFinger1_L\",\"MiddleFinger1_R\",\"MiddleFinger2_L\",\"MiddleFinger2_R\",\"MiddleFinger3_L\",\"MiddleFinger3_R\",\"RingFinger1_L\",\"RingFinger1_R\",\"RingFinger2_L\",\"RingFinger2_R\",\"RingFinger3_L\",\"RingFinger3_R\",\r\n \"LittleFinger1_L\",\"LittleFinger1_R\",\"LittleFinger2_L\",\"LittleFinger2_R\",\"LittleFinger3_L\",\"LittleFinger3_R\",\"Shoulder_L\",\"Shoulder_R\",\"Arm_L\",\"Arm_R\",\"Elbow_L\",\"Elbow_R\",\"Wrist_L\",\"Wrist_R\"\r\n ]\r\n p_operate_bones=[\r\n \"IndexFinger1_L\",\"IndexFinger2_L\",\"IndexFinger3_L\",\r\n \"MiddleFinger1_L\",\"MiddleFinger2_L\",\"MiddleFinger3_L\",\"RingFinger1_L\",\"RingFinger2_L\",\"RingFinger3_L\",\r\n \"LittleFinger1_L\",\"LittleFinger2_L\",\"LittleFinger3_L\",\"LittleFinger3_R\"]\r\n n_operate_bones=[\r\n \"IndexFinger1_R\",\"IndexFinger2_R\",\"IndexFinger3_R\",\r\n \"MiddleFinger1_R\",\"MiddleFinger2_R\",\"MiddleFinger3_R\",\"RingFinger1_R\",\"RingFinger2_R\",\"RingFinger3_R\",\r\n \"LittleFinger1_R\",\"LittleFinger2_R\",\"LittleFinger3_R\"]\r\n bpy.ops.armature.select_all(action='DESELECT')\r\n for name in fix_bone_list:\r\n if name in mmd_bones_list:\r\n mmd_arm.data.edit_bones[name].select=True\r\n bpy.ops.armature.calculate_roll(type='GLOBAL_NEG_Y')\r\n for name in p_operate_bones:\r\n if name in mmd_bones_list:\r\n mmd_arm.data.edit_bones[name].roll+=math.pi/2\r\n for name in n_operate_bones:\r\n if name in mmd_bones_list:\r\n mmd_arm.data.edit_bones[name].roll-=math.pi/2\r\n \r\n alert_error(\"提示\",\"轴向修正完成\")\r\n\r\n#Outdated methods\r\ndef fix_axial():\r\n global mmd_arm\r\n L_first_quadrant_list=[\r\n \"IndexFinger1_L\",\"IndexFinger2_L\",\"IndexFinger3_L\",\r\n \"MiddleFinger1_L\",\"MiddleFinger2_L\",\"MiddleFinger3_L\",\"RingFinger1_L\",\"RingFinger2_L\",\"RingFinger3_L\",\r\n \"LittleFinger1_L\",\"LittleFinger2_L\",\"LittleFinger3_L\",\"Shoulder_L\",\"Arm_L\",\"Elbow_L\"\r\n ]\r\n L_second_quadrant_bones_list=[\"Thumb0_L\",\"Thumb1_L\",\"Thumb2_L\",\"Wrist_L\"]\r\n R_first_quadrant_bones_list=[\r\n \"IndexFinger1_R\",\"IndexFinger2_R\",\"IndexFinger3_R\",\r\n \"MiddleFinger1_R\",\"MiddleFinger2_R\",\"MiddleFinger3_R\",\"RingFinger1_R\",\"RingFinger2_R\",\"RingFinger3_R\",\r\n \"LittleFinger1_R\",\"LittleFinger2_R\",\"LittleFinger3_R\",\"Shoulder_R\",\"Arm_R\",\"Elbow_R\"\r\n ]\r\n R_second_quadrant_bones_list=[\"Thumb0_R\",\"Thumb1_R\",\"Thumb2_R\",\"Wrist_R\"]\r\n\r\n for name in zero_roll_list:\r\n if name in mmd_bones_list:\r\n bone=mmd_arm.data.edit_bones[name]\r\n bone.roll=0\r\n\r\n for name in L_first_quadrant_list:\r\n if name in mmd_bones_list:\r\n bone=mmd_arm.data.edit_bones[name]\r\n roll=bone.roll\r\n old_quadrant=quad(roll)\r\n if old_quadrant==4:\r\n roll+=math.pi/2\r\n elif old_quadrant==3:\r\n roll+=math.pi\r\n elif old_quadrant==2:\r\n roll-=math.pi/2\r\n bone.roll=roll\r\n for name in L_second_quadrant_bones_list:\r\n if name in mmd_bones_list:\r\n bone=mmd_arm.data.edit_bones[name]\r\n roll=bone.roll\r\n old_quadrant=quad(roll)\r\n if old_quadrant==4:\r\n roll+=math.pi\r\n elif old_quadrant==3:\r\n roll-=math.pi/2\r\n elif old_quadrant==1:\r\n roll+=math.pi/2\r\n bone.roll=roll\r\n for name in R_first_quadrant_bones_list:\r\n if name in mmd_bones_list:\r\n bone=mmd_arm.data.edit_bones[name]\r\n roll=bone.roll\r\n old_quadrant=quad(roll)\r\n if old_quadrant==3:\r\n roll+=math.pi/2\r\n elif old_quadrant==2:\r\n roll+=math.pi\r\n elif old_quadrant==1:\r\n roll-=math.pi/2\r\n bone.roll=roll\r\n for name in R_second_quadrant_bones_list:\r\n if name in mmd_bones_list:\r\n bone=mmd_arm.data.edit_bones[name]\r\n roll=bone.roll\r\n old_quadrant=quad(roll)\r\n if old_quadrant==1:\r\n roll+=math.pi\r\n elif old_quadrant==4:\r\n roll-=math.pi/2\r\n elif old_quadrant==2:\r\n roll+=math.pi/2\r\n bone.roll=roll\r\n\r\n for name in L_first_quadrant_list+L_second_quadrant_bones_list:\r\n if name in mmd_bones_list:\r\n R_name=name.replace(\"_L\",\"_R\")\r\n if R_name in mmd_bones_list:\r\n mmd_arm.data.edit_bones[R_name].roll=-mmd_arm.data.edit_bones[name].roll\r\n alert_error(\"提示\",\"轴向修正完成\")\r\n\r\n#load Stance Pose\r\ndef load_pose():\r\n my_dir = os.path.dirname(os.path.realpath(__file__))\r\n vpd_file = os.path.join(my_dir, \"MMR_Rig_pose.vpd\")\r\n print(my_dir)\r\n print(vpd_file)\r\n bpy.ops.mmd_tools.import_vpd(filepath=vpd_file, files=[{\"name\":\"MMR_Rig_pose.vpd\", \"name\":\"MMR_Rig_pose.vpd\"}], directory=my_dir)\r\n\r\ndef add_constraint_execute():\r\n\r\n length=len(constraints_from)\r\n bpy.ops.object.mode_set(mode = 'EDIT')\r\n for i in range(length):\r\n From = constraints_from[i]\r\n To = constraints_to[i]\r\n parent_name=From + '_parent'\r\n parent_bone=rig.data.edit_bones.new(name=parent_name)\r\n parent_bone.head=mmd_arm2.data.edit_bones[From].head\r\n parent_bone.tail=mmd_arm2.data.edit_bones[From].tail\r\n parent_bone.roll=mmd_arm2.data.edit_bones[From].roll\r\n parent_bone.parent=rig.data.edit_bones[To]\r\n\r\n bpy.ops.object.mode_set(mode = 'POSE')\r\n for i in range(length):\r\n From = constraints_from[i]\r\n To = constraints_to[i]\r\n con= mmd_arm.pose.bones[From].constraints\r\n for c in con:\r\n c.mute=True\r\n parent_name=From + '_parent'\r\n rig.data.bones[parent_name].hide=True\r\n COPY_TRANSFORMS=con.new(type='COPY_TRANSFORMS')\r\n COPY_TRANSFORMS.target = rig\r\n COPY_TRANSFORMS.subtarget = parent_name\r\n COPY_TRANSFORMS.name=\"rel_transforms\"\r\n COPY_TRANSFORMS.mix_mode = 'REPLACE'\r\n COPY_TRANSFORMS.owner_space = 'WORLD'\r\n COPY_TRANSFORMS.target_space = 'WORLD'\r\n\r\ndef add_constraint(To,From,rotation=True):\r\n\r\n if From in mmd_bones_list:\r\n if rotation:\r\n constraints_from.append(From)\r\n constraints_to.append(To)\r\n else:\r\n COPY_LOCATION=mmd_arm.pose.bones[From].constraints.new(type='COPY_LOCATION')\r\n COPY_LOCATION.target = rig\r\n COPY_LOCATION.subtarget = To\r\n COPY_LOCATION.name=\"rel_location\"\r\n mmd_arm.data.bones[From].hide=False\r\n\r\ndef RIG(context):\r\n\r\n\r\n global mmd_arm\r\n global mmd_arm2\r\n global mmd_bones_list\r\n global rig\r\n global constraints_from\r\n global constraints_to\r\n\r\n scene=context.scene\r\n mmr_property=scene.mmr_property\r\n\r\n my_dir = os.path.dirname(os.path.realpath(__file__))\r\n rigify_blend_file = os.path.join(my_dir, \"MMR_Rig.blend\")\r\n\r\n if check_arm()==False:\r\n return{False}\r\n\r\n #建立骨骼关系字典\r\n mmd_bones_dict_j={}\r\n mmd_bones_dict_e={}\r\n for bone in mmd_arm.pose.bones:\r\n bone.bone.hide=True\r\n if bone.mmd_bone.name_e not in mmd_bones_dict_e:\r\n mmd_bones_dict_e[bone.mmd_bone.name_e]=bone.name\r\n if bone.mmd_bone.name_j not in mmd_bones_dict_j:\r\n mmd_bones_dict_j[bone.mmd_bone.name_j]=bone.name\r\n\r\n load_pose()\r\n\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n mmd_arm2=mmd_arm.copy()\r\n context.collection.objects.link(mmd_arm2)\r\n mmd_arm2.data=mmd_arm.data.copy()\r\n bpy.ops.object.select_all(action='DESELECT')\r\n bpy.context.view_layer.objects.active=mmd_arm2\r\n bpy.ops.object.mode_set(mode = 'POSE')\r\n bpy.ops.pose.armature_apply(selected=False)\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n\r\n rigify_arm_name=\"MMR_Rig_relative\"\r\n\r\n #导入metarig骨骼\r\n #import metarig armature\r\n rigify_arm=None\r\n with bpy.data.libraries.load(rigify_blend_file) as (data_from, data_to):\r\n data_to.objects = [name for name in data_from.objects if rigify_arm_name == name]\r\n for obj in data_to.objects:\r\n context.collection.objects.link(obj)\r\n rigify_arm=obj\r\n\r\n rigify_bones_list=rigify_arm.data.bones.keys()\r\n exist_bones=list(set(mmd_bones_list).intersection(rigify_bones_list))\r\n\r\n\r\n \r\n\r\n bpy.ops.object.select_all(action='DESELECT')\r\n bpy.context.view_layer.objects.active=rigify_arm\r\n bpy.ops.object.mode_set(mode = 'EDIT')\r\n bpy.ops.armature.select_all(action='DESELECT')\r\n\r\n #修正只有两节拇指骨骼的模型\r\n if \"Thumb1_L\" in exist_bones:\r\n if mmd_arm.data.bones[\"Thumb1_L\"].parent.name !=\"Thumb0_L\":\r\n rigify_arm.data.edit_bones.remove(rigify_arm.data.edit_bones[\"Thumb2_L\"])\r\n '''rigify_arm.data.edit_bones[\"Thumb2_L\"].select=True\r\n bpy.ops.armature.delete()'''\r\n rigify_arm.data.edit_bones[\"Thumb1_L\"].name='Thumb2_L'\r\n rigify_arm.data.edit_bones[\"Thumb0_L\"].name='Thumb1_L'\r\n\r\n if \"Thumb1_R\" in exist_bones:\r\n if mmd_arm.data.bones[\"Thumb1_R\"].parent.name !=\"Thumb0_R\":\r\n rigify_arm.data.edit_bones.remove(rigify_arm.data.edit_bones[\"Thumb2_R\"])\r\n '''rigify_arm.data.edit_bones[\"Thumb2_R\"].select=True\r\n bpy.ops.armature.delete()'''\r\n rigify_arm.data.edit_bones[\"Thumb1_R\"].name='Thumb2_R'\r\n rigify_arm.data.edit_bones[\"Thumb0_R\"].name='Thumb1_R'\r\n\r\n rigify_bones_list=rigify_arm.data.edit_bones.keys()\r\n\r\n #调整约束以匹配骨骼\r\n bpy.ops.object.mode_set(mode = 'POSE')\r\n for name in rigify_bones_list:\r\n bone=rigify_arm.pose.bones[name]\r\n parent_bone=None\r\n parent_bone=bone.parent\r\n if parent_bone!=None:\r\n parent_bone.constraints['stretch'].target=mmd_arm2\r\n parent_bone.constraints['stretch'].subtarget=name\r\n parent_bone.constraints[\"stretch\"].rest_length = parent_bone.length\r\n if name in exist_bones:\r\n bone.constraints['location'].target=mmd_arm2\r\n bone.constraints['location'].subtarget=bone.name\r\n else:\r\n bone.constraints['location'].mute=True\r\n bone.constraints['stretch'].mute=True\r\n\r\n '''vector_list=[]\r\n scale_list=[]\r\n for bone in rigify_arm.data.edit_bones:\r\n vector=[bone.tail[0]-bone.head[0],bone.tail[1]-bone.head[1],bone.tail[2]-bone.head[2]]\r\n scale=1\r\n if bone.parent!=None:\r\n scale=bone.length/bone.parent.length\r\n vector_list.append(vector)\r\n scale_list.append(scale)\r\n\r\n for i in range(len(rigify_arm.data.edit_bones)):\r\n bone=rigify_arm.data.edit_bones[i]\r\n name=bone.name\r\n parent_bone=bone.parent\r\n mmd_bone=mmd_arm2.pose.bone[name]\r\n if name in exist_bones:\r\n bone.head=mmd_bone.head\r\n if len(bone.children)==0:\r\n vector=vector_list[i]\r\n bone.tail=[bone.head[0]+vector[0],bone.head[1]+vector[1],bone.head[2]+vector[2]]\r\n if parent_bone!=None:\r\n bone.length=parent_bone.length*scale_list[i]'''\r\n\r\n\r\n #spine.001，Neck_Middle是多余骨骼\r\n rigify_arm.pose.bones['spine.001'].constraints[\"location\"].mute=True\r\n rigify_arm.pose.bones['spine.001'].constraints[\"stretch\"].mute=True\r\n\r\n rigify_arm.pose.bones['Neck_Middle'].constraints[\"location\"].mute=True\r\n rigify_arm.pose.bones['Neck_Middle'].constraints[\"stretch\"].mute=True\r\n\r\n rigify_arm.pose.bones['Neck'].constraints['stretch'].subtarget='Head'\r\n\r\n rigify_arm.pose.bones['LowerBody'].constraints[\"stretch\"].subtarget='UpperBody'\r\n rigify_arm.pose.bones[\"LowerBody\"].constraints[\"location\"].head_tail = 1\r\n\r\n rigify_arm.pose.bones['UpperBody2'].constraints[\"stretch\"].subtarget='UpperBody2'\r\n rigify_arm.pose.bones[\"UpperBody2\"].constraints[\"stretch\"].head_tail = 1\r\n\r\n rigify_arm.pose.bones['Ankle_L'].constraints[\"stretch\"].subtarget='ToeTipIK_L'\r\n\r\n rigify_arm.pose.bones['Ankle_R'].constraints[\"stretch\"].subtarget='ToeTipIK_R'\r\n \r\n rigify_arm.pose.bones['Head'].constraints['location'].target=mmd_arm\r\n rigify_arm.pose.bones['Head'].constraints['location'].subtarget='Head'\r\n scale=mmd_arm.data.bones['Head'].length/rigify_arm.data.bones['Head'].length\r\n rigify_arm.pose.bones['Head'].scale=[scale,scale,scale]\r\n rigify_arm.pose.bones['Head'].constraints['stretch'].target=mmd_arm\r\n rigify_arm.pose.bones['Head'].constraints['stretch'].subtarget='Head'\r\n rigify_arm.pose.bones['Head'].constraints[\"stretch\"].head_tail = 1\r\n #rigify_arm.pose.bones['Head'].constraints[\"stretch\"].rest_length = rigify_arm.data.bones['Head'].length\r\n\r\n rigify_arm.pose.bones['Wrist_L'].constraints[\"stretch\"].mute=True\r\n rigify_arm.pose.bones['Wrist_R'].constraints[\"stretch\"].mute=True\r\n\r\n rigify_arm.pose.bones['ToeTipIK_L'].constraints[\"stretch\"].mute=True\r\n rigify_arm.pose.bones['ToeTipIK_R'].constraints[\"stretch\"].mute=True\r\n\r\n #调整缺失UpperBody2情况\r\n no_UpperBody2=False\r\n if 'UpperBody2' not in exist_bones and 'UpperBody' in exist_bones:\r\n no_UpperBody2=True\r\n rigify_arm.pose.bones['UpperBody'].scale[0] = 0.0001\r\n rigify_arm.pose.bones['UpperBody'].scale[1] = 0.0001\r\n rigify_arm.pose.bones['UpperBody'].scale[2] = 0.0001\r\n rigify_arm.pose.bones['UpperBody'].constraints['location'].mute=True\r\n rigify_arm.pose.bones['UpperBody'].constraints['stretch'].mute=True\r\n rigify_arm.pose.bones['UpperBody2'].constraints['location'].subtarget='UpperBody'\r\n rigify_arm.pose.bones['UpperBody2'].constraints['stretch'].subtarget='UpperBody'\r\n rigify_arm.pose.bones['UpperBody2'].constraints['location'].mute=False\r\n rigify_arm.pose.bones['UpperBody2'].constraints['stretch'].mute=False\r\n\r\n bpy.ops.pose.armature_apply(selected=False)\r\n bpy.ops.pose.select_all(action='SELECT')\r\n bpy.ops.pose.constraints_clear()\r\n\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n mmd_arm2.select=True\r\n bpy.ops.object.mode_set(mode = 'EDIT')\r\n\r\n #修正末端骨骼长度\r\n rigify_arm.data.edit_bones[\"Thumb2_L\"].length=rigify_arm.data.edit_bones[\"Thumb1_L\"].length\r\n rigify_arm.data.edit_bones[\"Thumb2_R\"].length=rigify_arm.data.edit_bones[\"Thumb1_R\"].length\r\n rigify_arm.data.edit_bones[\"IndexFinger3_L\"].length=rigify_arm.data.edit_bones[\"IndexFinger2_L\"].length\r\n rigify_arm.data.edit_bones[\"IndexFinger3_R\"].length=rigify_arm.data.edit_bones[\"IndexFinger2_R\"].length\r\n rigify_arm.data.edit_bones[\"MiddleFinger3_L\"].length=rigify_arm.data.edit_bones[\"MiddleFinger2_L\"].length\r\n rigify_arm.data.edit_bones[\"MiddleFinger3_R\"].length=rigify_arm.data.edit_bones[\"MiddleFinger2_R\"].length\r\n rigify_arm.data.edit_bones[\"RingFinger3_L\"].length=rigify_arm.data.edit_bones[\"RingFinger2_L\"].length\r\n rigify_arm.data.edit_bones[\"RingFinger3_R\"].length=rigify_arm.data.edit_bones[\"RingFinger2_R\"].length\r\n rigify_arm.data.edit_bones[\"LittleFinger3_L\"].length=rigify_arm.data.edit_bones[\"LittleFinger2_L\"].length\r\n rigify_arm.data.edit_bones[\"LittleFinger3_R\"].length=rigify_arm.data.edit_bones[\"LittleFinger2_R\"].length\r\n rigify_arm.data.edit_bones[\"ToeTipIK_L\"].length=rigify_arm.data.edit_bones[\"Ankle_L\"].length/2\r\n rigify_arm.data.edit_bones[\"ToeTipIK_R\"].length=rigify_arm.data.edit_bones[\"Ankle_R\"].length/2\r\n rigify_arm.data.edit_bones[\"Wrist_L\"].length=rigify_arm.data.edit_bones[\"Elbow_L\"].length/4\r\n rigify_arm.data.edit_bones[\"Wrist_R\"].length=rigify_arm.data.edit_bones[\"Elbow_R\"].length/4\r\n\r\n #匹配眼睛骨骼\r\n if 'Eye_L' in mmd_bones_list and 'Eye_R' in mmd_bones_list:\r\n eye_L=rigify_arm.data.edit_bones['eye.L']\r\n mmd_eye_L=mmd_arm2.data.edit_bones['Eye_L']\r\n eye_L.head[2]=mmd_eye_L.head[2]\r\n eye_L.head[0]=max(mmd_eye_L.head[0],mmd_eye_L.tail[0])\r\n eye_L.head[1]=min(mmd_eye_L.head[1],mmd_eye_L.tail[1])\r\n eye_L.tail=eye_L.head\r\n eye_L.tail[1]-=0.1\r\n\r\n eye_R=rigify_arm.data.edit_bones['eye.R']\r\n mmd_eye_R=mmd_arm2.data.edit_bones['Eye_R']\r\n eye_R.head[2]=mmd_eye_R.head[2]\r\n eye_R.head[0]=min(mmd_eye_R.head[0],mmd_eye_R.tail[0])\r\n eye_R.head[1]=min(mmd_eye_R.head[1],mmd_eye_R.tail[1])\r\n eye_R.tail=eye_R.head\r\n eye_R.tail[1]-=0.1\r\n\r\n invert_eyes=False\r\n if eye_L.head[0]4:\r\n bm.faces.remove(f)'''\r\n #删除多余边\r\n #remove extra edge\r\n for e in bm.edges:\r\n true_edge=False\r\n for i in edge_index:\r\n if e.verts[0].index in i and e.verts[1].index in i:\r\n true_edge=True\r\n break\r\n if true_edge==False:\r\n bm.edges.remove(e)\r\n bm.faces.ensure_lookup_table()\r\n\r\n #尝试标记出头发,飘带\r\n #try mark hair or ribbon vertex\r\n\r\n '''bm.to_mesh(mesh)\r\n bpy.ops.object.mode_set(mode = 'EDIT')\r\n bpy.ops.mesh.select_all(action='DESELECT')\r\n bpy.ops.mesh.select_mode(type='EDGE')\r\n bpy.ops.mesh.select_non_manifold(extend=False, use_wire=True, use_boundary=False, use_multi_face=False, use_non_contiguous=False, use_verts=False)\r\n bpy.ops.mesh.select_linked(delimit=set())\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n bm.clear()\r\n bm.from_mesh(mesh)'''\r\n\r\n ribbon_verts=[v for v in bm.verts if v.is_wire]\r\n if mmr_property.extend_ribbon:\r\n boundary_verts=set(ribbon_verts)\r\n boundary_verts2=[]\r\n while len(boundary_verts) != 0:\r\n boundary_verts2.clear()\r\n for v in boundary_verts:\r\n for e in v.link_edges:\r\n for v2 in e.verts:\r\n if v2 not in ribbon_verts:\r\n ribbon_verts.append(v2)\r\n boundary_verts2.append(v2)\r\n boundary_verts=set(boundary_verts2)\r\n boundary_verts2.clear()\r\n\r\n all_ribbon=True\r\n for f in bm.faces:\r\n ribbon_face=False\r\n for v in f.verts:\r\n if v in ribbon_verts:\r\n ribbon_face=True\r\n if ribbon_face==False:\r\n all_ribbon=False\r\n\r\n #标记出特殊边和点\r\n #These are special edge and vertex\r\n\r\n up_edges=[]\r\n down_edges=[]\r\n side_edges=[]\r\n up_verts=[]\r\n down_verts=[]\r\n side_verts=[]\r\n\r\n #标出头部，尾部，飘带顶点\r\n #try mark head,tail,ribbon vertex\r\n bm.verts.ensure_lookup_table()\r\n bm.edges.ensure_lookup_table()\r\n for i in range(len(bm.verts)):\r\n v=bm.verts[i]\r\n bone=bones_list[i]\r\n if bone.bone.use_connect==False and v.is_boundary:\r\n up_verts.append(v)\r\n elif bone.parent not in bones_list:\r\n up_verts.append(v)\r\n elif len(bone.children)==0:\r\n down_verts.append(v)\r\n elif bone.children[0] not in bones_list:\r\n down_verts.append(v)\r\n if v in ribbon_verts and mmr_property.cloth_convert_mod==1 or mmr_property.cloth_convert_mod==2:\r\n v.co=bone.tail\r\n\r\n #标出头部，尾部，飘带边\r\n #try mark head,tail,ribbon edge\r\n for i in range(len(bm.edges)):\r\n e=bm.edges[i]\r\n vert1=e.verts[0]\r\n vert2=e.verts[1]\r\n if e.is_boundary:\r\n if vert1 in up_verts and vert2 in up_verts:\r\n up_edges.append(e)\r\n elif vert1 in down_verts and vert2 in down_verts:\r\n down_edges.append(e)\r\n else:\r\n side_edges.append(e)\r\n if e.verts[0] not in side_verts:\r\n side_verts.append(e.verts[0])\r\n if e.verts[1] not in side_verts:\r\n side_verts.append(e.verts[1])\r\n\r\n #延长头部顶点 \r\n #extend root vertex\r\n new_up_verts=[None for i in range(len(bm.verts))]\r\n new_down_verts=[None for i in range(len(bm.verts))]\r\n for v in up_verts:\r\n new_location=bones_list[v.index].head\r\n if mmr_property.cloth_convert_mod==1 and v not in ribbon_verts or mmr_property.cloth_convert_mod==3:\r\n for e in v.link_edges:\r\n if e not in up_edges:\r\n if e.verts[0]==v:\r\n new_location=v.co*2-e.verts[1].co\r\n else:\r\n new_location=v.co*2-e.verts[0].co\r\n break\r\n new_vert=bm.verts.new(new_location,v)\r\n new_edge=bm.edges.new([v,new_vert])\r\n\r\n deform_layer = bm.verts.layers.deform.active\r\n if deform_layer != None:\r\n deform_vert = v[deform_layer]\r\n for i in skin_vertex_groups_index:\r\n if i in deform_vert:\r\n deform_vert[i]=0\r\n\r\n new_up_verts[v.index]=new_vert\r\n if v in side_verts:\r\n side_verts.append(new_vert)\r\n side_edges.append(new_edge)\r\n\r\n #延长尾部顶点\r\n #extend tail vertex\r\n for v in down_verts:\r\n if v not in up_verts:\r\n new_location=[0,0,0]\r\n for e in v.link_edges:\r\n if e not in down_edges:\r\n if e.verts[0]==v:\r\n new_location=v.co*2-e.verts[1].co\r\n else:\r\n new_location=v.co*2-e.verts[0].co\r\n break\r\n new_vert=bm.verts.new(new_location,v)\r\n new_edge=bm.edges.new([v,new_vert])\r\n new_down_verts[v.index]=new_vert\r\n if v in side_verts:\r\n side_verts.append(new_vert)\r\n side_edges.append(new_edge)\r\n\r\n for e in up_edges:\r\n vert1=e.verts[0]\r\n vert2=e.verts[1]\r\n vert3=new_up_verts[vert2.index]\r\n vert4=new_up_verts[vert1.index]\r\n if vert3 != None and vert4 != None:\r\n bm.faces.new([vert1,vert2,vert3,vert4])\r\n\r\n\r\n for e in down_edges:\r\n vert1=e.verts[0]\r\n vert2=e.verts[1]\r\n vert3=new_down_verts[vert2.index]\r\n vert4=new_down_verts[vert1.index]\r\n if vert3 != None and vert4 != None:\r\n bm.faces.new([vert1,vert2,vert3,vert4])\r\n \r\n #延长侧边顶点\r\n #extend side vertex\r\n bm.verts.index_update( ) \r\n bm.faces.ensure_lookup_table()\r\n new_side_verts=[None for i in range(len(bm.verts))]\r\n for v in side_verts:\r\n for e in v.link_edges:\r\n if e not in side_edges:\r\n if e.verts[0]==v:\r\n new_location=v.co*2-e.verts[1].co\r\n else:\r\n new_location=v.co*2-e.verts[0].co\r\n break\r\n new_vert=bm.verts.new(new_location,v)\r\n new_side_verts[v.index]=new_vert\r\n\r\n for e in side_edges:\r\n vert1=e.verts[0]\r\n vert2=e.verts[1]\r\n vert3=new_side_verts[vert2.index]\r\n vert4=new_side_verts[vert1.index]\r\n if vert3 != None and vert4 != None:\r\n bm.faces.new([vert1,vert2,vert3,vert4])\r\n\r\n bm.verts.ensure_lookup_table()\r\n bmesh.ops.recalc_face_normals(bm,faces=bm.faces)\r\n bm.normal_update()\r\n\r\n #挤出飘带顶点\r\n #extrude ribbon edge\r\n new_extrude_verts=[None for i in range(len(bm.verts))]\r\n for v in bm.verts[:]:\r\n if v.is_wire:\r\n new_location=[v.co[0],v.co[1]+0.01,v.co[2]]\r\n new_vert=bm.verts.new(new_location,v)\r\n new_extrude_verts[v.index]=new_vert\r\n else:\r\n v.co[0]-=v.normal[0]*mean_radius\r\n v.co[1]-=v.normal[1]*mean_radius\r\n v.co[2]-=v.normal[2]*mean_radius\r\n\r\n bm.verts.ensure_lookup_table()\r\n bm.edges.ensure_lookup_table()\r\n for e in bm.edges[:]:\r\n if e.is_wire:\r\n vert1=e.verts[0]\r\n vert2=e.verts[1]\r\n vert3=new_extrude_verts[vert2.index]\r\n vert4=new_extrude_verts[vert1.index]\r\n if vert3 != None and vert4 != None:\r\n bm.faces.new([vert1,vert2,vert3,vert4])\r\n\r\n #删除孤立顶点\r\n #remove single vertex\r\n bm.verts.ensure_lookup_table()\r\n bm.edges.ensure_lookup_table()\r\n\r\n '''for v in bm.verts[:]:\r\n if len(v.link_edges)==0:\r\n bm.verts.remove(v)\r\n\r\n bm.verts.ensure_lookup_table()'''\r\n bm.to_mesh(mesh)\r\n\r\n bpy.ops.object.mode_set(mode = 'EDIT')\r\n bpy.ops.mesh.select_all(action='SELECT')\r\n bpy.ops.mesh.normals_make_consistent(inside=False)\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n\r\n for obj in joints:\r\n bpy.data.objects.remove(obj)\r\n for obj in side_joints:\r\n bpy.data.objects.remove(obj)\r\n\r\n deform_vertex_group=mmd_mesh_object.vertex_groups.new(name='mmd_cloth_deform')\r\n\r\n cloth_obj.display_type = 'WIRE'\r\n\r\n mod=cloth_obj.modifiers.new('mmd_cloth_subsurface','SUBSURF')\r\n mod.levels = mmr_property.subdivide\r\n mod.render_levels = mmr_property.subdivide\r\n mod.boundary_smooth = 'PRESERVE_CORNERS'\r\n mod.show_only_control_edges = False\r\n\r\n\r\n mod=cloth_obj.modifiers.new('mmd_cloth_skin','ARMATURE')\r\n mod.object = mmd_arm\r\n mod.vertex_group = \"mmd_cloth_pin\"\r\n\r\n mod=cloth_obj.modifiers.new('mmd_cloth','CLOTH')\r\n mod.settings.vertex_group_mass = \"mmd_cloth_pin\"\r\n\r\n mod=cloth_obj.modifiers.new('mmd_cloth_smooth','CORRECTIVE_SMOOTH')\r\n mod.smooth_type = 'LENGTH_WEIGHTED'\r\n mod.rest_source = 'BIND'\r\n bpy.ops.object.correctivesmooth_bind(modifier=\"mmd_cloth_smooth\")\r\n if mmr_property.subdivide==0:\r\n mod.show_viewport = False\r\n\r\n bpy.context.view_layer.objects.active=mmd_mesh_object\r\n\r\n #写入形变权重或骨骼约束\r\n #Add weight or constrain\r\n #准备阶段\r\n # preparation\r\n unnecessary_vertex_groups: type.List[bpy.types.VertexGroup] = []\r\n mmd_mesh: bpy.types.Mesh = mmd_mesh_object.data\r\n mmd_bm: bmesh.types.BMesh = bmesh.new()\r\n mmd_bm.from_mesh(mmd_mesh)\r\n\r\n mmd_bm.verts.layers.deform.verify()\r\n deform_layer = mmd_bm.verts.layers.deform.active\r\n\r\n for i in range(rigid_bodys_count):\r\n v=bm.verts[i]\r\n obj=rigid_bodys[i]\r\n bone=bones_list[i]\r\n name=bone.name\r\n if v in ribbon_verts and mmr_property.cloth_convert_mod==1 or mmr_property.cloth_convert_mod==2 :\r\n line_vertex_group=cloth_obj.vertex_groups.new(name=name)\r\n line_vertex_group.add([i],1,'REPLACE')\r\n for c in bone.constraints:\r\n bone.constraints.remove(c)\r\n con=bone.constraints.new(type='STRETCH_TO')\r\n con.target = cloth_obj\r\n con.subtarget = name\r\n con.rest_length = bone.length\r\n else:\r\n from_vertex_group = mmd_mesh_object.vertex_groups[name]\r\n from_index = from_vertex_group.index\r\n unnecessary_vertex_groups.append(from_vertex_group)\r\n\r\n vert: bmesh.types.BMVert\r\n for vert in mmd_bm.verts:\r\n deform_vert: bmesh.types.BMDeformVert = vert[deform_layer]\r\n if from_index not in deform_vert:\r\n continue\r\n\r\n to_index = deform_vertex_group.index\r\n deform_vert[to_index] = deform_vert.get(to_index, 0.0) + deform_vert[from_index]\r\n \r\n bpy.data.objects.remove(obj)\r\n\r\n mmd_bm.to_mesh(mmd_mesh)\r\n mmd_bm.free()\r\n for vertex_group in unnecessary_vertex_groups:\r\n mmd_mesh_object.vertex_groups.remove(vertex_group)\r\n\r\n if all_ribbon == False and mmr_property.cloth_convert_mod!=2:\r\n bpy.context.view_layer.objects.active=mmd_mesh_object\r\n mod=mmd_mesh_object.modifiers.new('mmd_cloth_deform','SURFACE_DEFORM')\r\n mod.target = cloth_obj\r\n mod.vertex_group = deform_vertex_group.name\r\n bpy.ops.object.surfacedeform_bind(modifier=mod.name)\r\n\r\n bm.free()\r\n\r\ndef export_vmd(context,vmd_path,rigify_arm,scale,use_pose_mode,set_action_range,start_frame,end_frame):\r\n PMX_list=[\r\n '全ての親','センター','下半身','上半身','上半身2','首','頭','両目','左目','右目',\r\n '左足','左足ＩＫ','左つま先ＩＫ','右足','右足ＩＫ','右つま先ＩＫ',\r\n '左肩','左腕','左ひじ','左手首','右肩','右腕','右ひじ','右手首',\r\n '左親指０','左親指１','左親指２',\r\n '左人指１','左人指２','左人指３',\r\n '左中指１','左中指２','左中指３',\r\n '左薬指１','左薬指２','左薬指３',\r\n '左小指１','左小指２','左小指３',\r\n '右親指０','右親指１','右親指２',\r\n '右人指１','右人指２','右人指３',\r\n '右中指１','右中指２','右中指３',\r\n '右薬指１','右薬指２','右薬指３',\r\n '右小指１','右小指２','右小指３'\r\n ]\r\n\r\n if rigify_arm.type!='ARMATURE':\r\n return(False)\r\n if vmd_path==None:\r\n return(False)\r\n\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n bpy.ops.object.select_all(action='DESELECT')\r\n #复制骨骼\r\n #duplicate armature\r\n mmd_arm=None\r\n for obj in rigify_arm.children[0].children:\r\n if obj.type=='ARMATURE':\r\n mmd_arm=obj\r\n break\r\n if mmd_arm==None:\r\n return(False)\r\n\r\n mmd_arm2=mmd_arm.copy()\r\n context.collection.objects.link(mmd_arm2)\r\n bpy.ops.object.select_all(action='DESELECT')\r\n bpy.context.view_layer.objects.active=mmd_arm2\r\n mmd_arm2.select=True\r\n print(vmd_path)\r\n\r\n if set_action_range:\r\n start_frame1=start_frame\r\n end_frame1=end_frame\r\n else:\r\n rigify_action=rigify_arm.animation_data.action\r\n if rigify_action ==None:\r\n return(False)\r\n start_frame1=rigify_action.frame_range[0]\r\n end_frame1=rigify_action.frame_range[1]\r\n\r\n bpy.ops.object.mode_set(mode = 'POSE')\r\n bpy.ops.pose.select_all(action='SELECT')\r\n\r\n for bone in mmd_arm2.pose.bones:\r\n if bone.mmd_bone.name_j in PMX_list:\r\n bone.bone.select=True\r\n else:\r\n bone.bone.select=False\r\n\r\n bpy.ops.nla.bake(frame_start=start_frame1, frame_end=end_frame1, only_selected=True, visual_keying=True,clear_constraints=True, bake_types={'POSE'})\r\n bpy.ops.object.mode_set(mode = 'OBJECT')\r\n bpy.ops.mmd_tools.export_vmd(filepath=vmd_path,scale=scale, use_pose_mode=use_pose_mode,use_frame_range=False)\r\n bpy.data.objects.remove(mmd_arm2)\r\n\r\n return(True)","sub_path":"MikuMikuRig/MMR_Core.py","file_name":"MMR_Core.py","file_ext":"py","file_size_in_byte":75962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"131480352","text":"import logging\n\nfrom django.utils.translation import ugettext as _\nfrom django.utils.translation import ugettext_lazy as lazy_\n\n# Choices for POLLER_PING_INTERVAL and POLLER_SNMP_INTERVAL settings\npoller_interval_choices = (\n (1, '1 ' + _('minute')),\n (2, '2 ' + _('minutes')),\n (3, '3 ' + _('minutes')),\n (4, '4 ' + _('minutes')),\n (5, '5 ' + _('minutes')),\n (10, '10 ' + _('minutes')),\n (20, '20 ' + _('minutes')),\n (30, '30 ' + _('minutes')),\n)\n\n# Choices for SNMP_TIMEOUT setting\nsnmp_timeout_choices = (\n (500, '0.5 ' + _('second')),\n (1000, '1.0 ' + _('second')),\n (1500, '1.5 ' + _('seconds')),\n (2000, '2.0 ' + _('seconds')),\n (3000, '3.0 ' + _('seconds')),\n (4000, '4.0 ' + _('seconds')),\n)\n\n# Choices for SNMP_RETRIES setting\nsnmp_retries_choices = (\n (0, '0 ' + _('times')),\n (1, '1 ' + _('time')),\n (2, '2 ' + _('times')),\n (3, '3 ' + _('times')),\n (4, '4 ' + _('times')),\n (5, '5 ' + _('times')),\n)\n\n# DB Log Handler log levels\nlog_level_choices = (\n (logging.INFO, lazy_('Information')),\n (logging.WARNING, lazy_('Warning')),\n (logging.ERROR, lazy_('Error')),\n (logging.FATAL, lazy_('Fatal')),\n (logging.DEBUG, lazy_('Debug')),\n)","sub_path":"sentinel/system/choices.py","file_name":"choices.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"572320749","text":"\nfrom trajectory.messages import AxisConfig, OutVal, OutMode\nfrom trajectory.proto import SyncProto\n\n\ndef rpm_to_usps(rpm, usteps, steps_per_rotation=200):\n \"\"\"Return the number of microsteps per second at full velocity\"\"\"\n rps = rpm / 60 # rotations per second\n fsps = rps * steps_per_rotation # Full steps per second\n usps = fsps * usteps\n return usps\n\n\ndef make_axes(rpm, accel, usteps=1, steps_per_rotation=200,\n output_mode=OutMode.OUTPUT, highval=OutVal.HIGH):\n \"rpm is target RPM. acell is, I think, the time in sec to reach full velocity. \"\n s = rpm_to_usps(rpm, usteps)\n\n mx = ( highval, output_mode, s, s / accel) # Max Velocity , Max Acceleration\n\n _axes = {\n 'x': (18, 19, 20, *mx), # X Waist, Axis 0\n 'y': (21, 22, 23, *mx), # Y Shoulder 1, Axis 1\n 'z': (5, 6, 7, *mx), # Z Shoulder 2, Axis 2\n 'a': (15, 16, 17, *mx), # A Elbow, Axis 3\n 'b': (8, 9, 10, *mx), # B Wrist 1, Axis 4\n 'c': (2, 3, 4, *mx), # C Wrist 2, Axis 5\n }\n\n\n return {\n \"axes1\": [AxisConfig(0, *_axes['a'])],\n \"axesb\": [AxisConfig(0, *_axes['b'])],\n \"axesz\": [AxisConfig(0, *_axes['z'])],\n \"axes2\": [AxisConfig(0, *_axes['a']), AxisConfig(1, *_axes['z'])],\n \"axes3\": [AxisConfig(0, *_axes['a']), AxisConfig(1, *_axes['z']), AxisConfig(2, *_axes['b'])],\n \"axes6\": [AxisConfig(i, *e) for i, e in enumerate(_axes.values())],\n \"x_1sec\": rpm_to_usps(rpm, usteps),\n \"mspr\": usteps * steps_per_rotation, # microsteps per rotation\n \"axes\": _axes\n }\n\nimport unittest\n\nclass TestStepper(unittest.TestCase):\n # Determines wether the steppers are enables with an output value of high or low\n # Different for different stepper drivers\n ENABLE_OUTPUT = False\n\n def setUp(self) -> None:\n pass\n\n def tearDown(self) -> None:\n pass\n\n def init(self, packet_port, encoder_port, v=800, axes_name='axes1', usteps=16, a=.1,\n highvalue=OutVal.HIGH, outmode=OutMode.OUTPUT_OPENDRAIN,\n segment_pin=27, limit_pint=29, period=4,\n use_encoder=True):\n\n d = make_axes(v, a, usteps=usteps, steps_per_rotation=200,\n highval=highvalue, output_mode=outmode)\n\n p = SyncProto(packet_port, encoder_port if use_encoder else None)\n p.encoder_multipliers[0] = 1 + (1 / 3)\n\n p.config(period, segment_pin, limit_pint, False, False, axes=d[axes_name]);\n\n p.mspr = d['mspr']\n p.x_1sec = d['x_1sec']\n\n return p\n","sub_path":"trajectory/trajectory/test/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"56631483","text":"__author__ = 'gambler'\nclass Solution:\n def solve(self):\n N = ar[0]\n J = ar[1]\n divline = \" 3 4 5 6 7 8 9 10 11\\n\"\n di = dict()\n if N==16:\n even = [2,4,6,8,10,12,14]\n odd = [1,3,5,7,9,11,13]\n for e in even:\n for o in odd:\n ans = list('1' + 14*'0' + '1')\n ans[e] = '1'\n ans[o] = '1'\n val = \"\".join(ans)\n di[val] = 1\n fout.write(val + divline)\n fout.write(\"1000000000000001\"+divline)\n else:\n even = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30]#,32,34,36,38,40,42,44,46,48]\n odd = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29]#,31,33,35,37,39,41,43,45,47,49]\n for e1 in even:\n for o1 in odd:\n for e2 in even:\n for o2 in odd:\n ans = list('1' + 30*'0' + '1')\n ans[e1] = '1'\n ans[o1] = '1'\n ans[e2] = '1'\n ans[o2] = '1'\n val = \"\".join(ans)\n di[val] = 1\n fout.write(val + divline)\n if len(di)==499:\n break\n if len(di)==499:\n break\n if len(di)==499:\n break\n if len(di)==499:\n break\n fout.write(\"10000000000000000000000000000001\"+divline)\n #print len(di)\n\n\n\n#fin = open(\"/Users/gambler/Documents/pycharm/input.txt\", \"r\")\nfin = open(\"/Users/gambler/Documents/pycharm/C-small-attempt0.in\", \"r\")\nfout = open(\"/Users/gambler/Documents/pycharm/output.txt\", \"w\")\ncases = int(fin.readline().strip())\ns = Solution()\nfor case in range(cases):\n ar = map(int, fin.readline().strip().split())\n fout.write(\"Case #\"+str(case+1)+\":\\n\")\n s.solve()\n\nfin.close()\nfout.close()\n","sub_path":"codes/CodeJamCrawler/16_0_3/theGamblerRises/CoinJam.py","file_name":"CoinJam.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"222042965","text":"import requests\nimport bs4\n\n\ndef extraccionDatos(url):\n contenido = \"\"\n page = requests.get(url).text\n soup = bs4.BeautifulSoup(page, 'html.parser')\n titulo = str(soup.h1.string)\n contenido = contenido + titulo + \" \"\n try:\n subtitulo = str(soup.find(id=\"article-summary\").string)\n contenido = contenido + subtitulo + \" \"\n listaTexto = soup.find(itemprop=\"articleBody\").contents\n for i in range(0, len(listaTexto)):\n contenido += listaTexto[i].text\n except:\n next\n return contenido\n","sub_path":"ExtraccionTexto.py","file_name":"ExtraccionTexto.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"490337777","text":"from django.contrib.auth.decorators import login_required\nfrom django.http.response import HttpResponse\nfrom django.shortcuts import render\nfrom plotly.offline import plot\nfrom plotly import graph_objects as graphs\n\nfrom .utils import get_books_reviewed_by_month, get_xlsx_data_list_of_books_reviewed\n\n\n@login_required\ndef profile(request):\n user = request.user\n permissions = user.get_all_permissions()\n # Get the books reviewed in different months this year\n books_by_month = get_books_reviewed_by_month(user.username)\n # Initialize the Axis for graphs, X-Axis is months, Y-axis is books read.\n # Since `books_by_month` only contain months that actually have values,\n # we initialize all `books_read` for every month to 0.\n months = [i + 1 for i in range(12)]\n books_read = [0 for _ in range(12)]\n # Set the value for books read per month on Y-Axis\n for num_books_read in books_by_month:\n list_index = num_books_read['date_created__month'] - 1\n books_read[list_index] = num_books_read['book_count']\n\n # Generate a scatter plot HTML\n figure = graphs.Figure()\n scatter = graphs.Scatter(x=months, y=books_read)\n figure.add_trace(scatter)\n figure.update_layout(xaxis_title=\"Month\", yaxis_title=\"No. of books read\")\n plot_html = plot(figure, output_type='div')\n\n return render(\n request,\n 'profile.html',\n {'user': user, 'permissions': permissions, 'books_read_plot': plot_html},\n )\n\n\n@login_required\ndef download_user_book_data(request):\n xlsx_data = get_xlsx_data_list_of_books_reviewed(request.user.username)\n response = HttpResponse(content_type='application/vnd.ms-excel')\n response['Content-Disposition'] = 'attachment; filename=books_reviewed.xlsx'\n response.write(xlsx_data)\n return response\n","sub_path":"bookr/bookr/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"570194951","text":"def get_breadcrumb(category):\n \"\"\"\n 获取面包屑导航\n :param category: 商品类别\n :return: 面包屑导航字典\n \"\"\"\n breadcrumb = dict(\n cat1='',\n cat2='',\n cat3=''\n )\n\n # 当前类别为三级\n breadcrumb['cat3'] = category.name\n breadcrumb['cat2'] = category.parent.name\n breadcrumb['cat1'] = category.parent.parent.name\n\n return breadcrumb","sub_path":"meiduo_mall/apps/goods/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"46429886","text":"import math\n\nimport numpy as np\nimport gym\nfrom gym import wrappers\n\nimport environments\nfrom agents.dqn.dqn import *\n\n# for debug\nfrom gym import logger\n# logger.set_level(10)\n\n\nTIME_STEP = 0.005\n\ndef main():\n env = gym.make('LidarBat-v0')\n # env = gym.make('CartPole-v0')\n # env = gym.make('Pendulum-v0')\n # env = gym.make('BipedalWalker-v2')\n env = wrappers.Monitor(env, 'data/env_test_videos', force=True)\n for i_episode in range(5):\n observation = env.reset()\n for t in range(1000):\n print(f'----step {t}----')\n # print(f'bat angle: {env.bat.angle *180 / math.pi:2f} [degree]')\n print('observation:')\n print(observation)\n action = env.action_space.sample()\n action[0] = 0\n # action[1] = math.pi/2\n # action[2] = 0.9\n # action[3] = 0\n print(f'action: {action}')\n observation, reward, done, info = env.step(action)\n print(f'reward: {reward}')\n print(f'done: {done}')\n # print(f'time: {env.t:2f} [sec]')\n if done:\n print(f\"Episode finished after {t+1} timesteps\")\n break\n env.reset()\n env.close()\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"628521381","text":"import requests\nfrom random import choice\nimport pyfiglet\n\nheader = pyfiglet.figlet_format('DAD JOKE !!')\nurl = 'https://icanhazdadjoke.com/search'\n\ndef game(header, url):\n\tprint(header)\n\n\tuser_input = input('What would you like to search for ? Enter (quit) to quit the game! ')\n\n\tres = requests.get(\n\t\turl, \n\t\theaders= {'accept': 'application/json'},\n\t\tparams = {'term': user_input}\n\t\t).json()\n\n\tnum_jokes = res['total_jokes']\n\tresults = res['results']\n\t\n\n\twhile user_input != 'quit':\n\n\t\tif num_jokes > 1:\n\t\t\tprint (f\"I found {num_jokes} jokes about {user_input}. Here's one: \")\n\t\t\tprint (choice(results)['joke'])\n\t\telif num_jokes == 1:\n\t\t\tprint(f\"I found One joke about {user_input}. \")\n\t\t\tprint (results[0][\"joke\"])\n\t\telse:\n\t\t\tprint( \"Sorry I couldn't find a joke with your term\")\n\n\t\tuser_input = input('What would you like to search for ? Enter (quit) to quit the game! ')\n\n\n\nif __name__ == '__main__':\n\tgame(header, url)\n","sub_path":"jokes.py","file_name":"jokes.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"492428304","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[153]:\n\n\nimport numpy as np\nimport random\nimport math\nimport sys\nimport collections\nfrom numpy.random import seed\nseed(1)\nif len(sys.argv) != 5:\n sys.exit() \nX = np.genfromtxt(sys.argv[1],delimiter=\",\")\n\n\n# In[154]:\n\n\nm = X.shape[0]\n\n\n# In[155]:\n\n\nprev_labels = np.zeros(m)\n\n\n# In[156]:\n\n\nk = int(sys.argv[2])\nr = int(sys.argv[3])\niterations = 0\n\n\n# In[157]:\n\n\nOverall_Q = sys.maxsize\nwhile(iterations < r):\n Centroids = {}\n counter = 1\n Centroids[counter] = X[random.randint(1, m)]\n \n #Compute the Centroids\n while(counter < k):\n Max_Dist = -sys.maxsize - 1 \n for i in range(m):\n val_dist = 0\n Sum_dist = 0\n for key in Centroids:\n val = X[i] - Centroids.get(key)\n val_dist = np.sum(val**2)\n Sum_dist = Sum_dist + val_dist\n if(Sum_dist >= Max_Dist):\n Max_Dist = Sum_dist\n Coordinates = X[i] \n counter = counter + 1\n Centroids[counter] = Coordinates \n \n #Running the iterations for Convergence\n while True: \n #Assign the labels based on distance to each centroids \n Labels = np.zeros(m)\n for i in range(m):\n Min_Distance = sys.maxsize\n for c in range(1,len(Centroids.keys())+1):\n temp = X[i] - Centroids.get(c)\n temp_dist = np.sum(temp**2)\n if (temp_dist <= Min_Distance):\n Min_Distance = temp_dist\n Labels[i] = c \n if(collections.Counter(Labels) == collections.Counter(prev_labels)): \n break;\n else: \n Temp_Labels = np.reshape(Labels,(m,1))\n Overall = np.concatenate((X,Temp_Labels),axis=1) \n last_col = Overall.shape[1] - 1\n \n #Compute the Quantization Error based on the Clusters\n Overall_Error = 0\n for i in range(m):\n Sum_d = 0\n val = Overall[i][last_col]\n Computation = Centroids.get(val)\n for j in range(last_col):\n Sum_d = Sum_d + (Computation[j] - Overall[i][j])**2\n Overall_Error = Overall_Error + Sum_d\n \n #Compute the New Centroids\n Unique_vals,count_vals = np.unique(Overall[:,last_col], return_counts=True)\n Centroids = {}\n for i in range(len(Unique_vals)):\n Dat_subset = Overall[Overall[:,last_col]==Unique_vals[i]]\n Mean_subset = np.mean(Dat_subset[:,0:last_col],axis = 0)\n Centroids[Unique_vals[i]] = Mean_subset \n prev_labels = Labels\n if(Overall_Error < Overall_Q):\n Overall_Q = Overall_Error\n final_labels = Labels\n iterations = iterations + 1\nprint(Overall_Q)\nnp.savetxt(sys.argv[4], final_labels, delimiter =',')\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"kmeanspp.py","file_name":"kmeanspp.py","file_ext":"py","file_size_in_byte":2918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"102727201","text":"import os,time,xlrd,sys\n# 为了将多个地址合并在一起需要导入Reduce包\nfrom functools import reduce\n# 导入本地库\nsys.path.append(r\"..\")\nimport Lib.LF as Com\n\nclass GetAddr():\n time.sleep(1)\n # 定义当前路径\n if getattr(sys, 'frozen', False):\n CurrentPath = os.path.dirname(sys.executable)\n elif __file__:\n CurrentPath = os.path.dirname(__file__)\n else:\n CurrentPath = os.getcwd()\n Sta = []\n SQLiteDataBaseFile = \"AGEO.db\"\n SQLiteDataBase = os.path.join(CurrentPath, SQLiteDataBaseFile)\n\n def __init__(self,SQLiteDataBase,Sta):\n self.SQLiteDataBase = SQLiteDataBase\n self.Sta = Sta\n\n def By(self,Mode,Code):\n\n if Mode == 'code':\n Addr = Com.Infra.SQLite3(SQL=\"select Addr from JsSrvAGEOMaster WHERE Code = '%s'\" % Code,\n Data=None, Database=GetAddr.SQLiteDataBase, NumberOfRow=0, OutPutType='List')\n if Addr == []:return False\n else:\n print(\"[**]通过Code查询地址成功\")\n return Addr[0]\n\n if Mode == 'codename':\n Addr = Com.Infra.SQLite3(\n SQL=\"select Addr from JsSrvAGEOMaster WHERE CodeName = '%s'\" % Code,\n Data=None, Database=GetAddr.SQLiteDataBase, NumberOfRow=0, OutPutType='List')\n if Addr == []:\n return False\n else:\n print(\"[**]通过CodeName查询地址成功\")\n return Addr[0]\n","sub_path":"ACTA.py","file_name":"ACTA.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"272060816","text":"list1 = [12,-7,5,64,-14]\n\nfor j in list1:\n if j>=0:\n print(j,end=\", \")\nprint(\"\\n\")\nlist2 = [12,14,-95,3]\nlist3=[]\n\nfor i in list2:\n if i>=0:\n list3.append(i)\n\nprint(list3) \n \n","sub_path":"positivenumber.py","file_name":"positivenumber.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"384955756","text":"import heapq\n\nclass Recursive_Best_First:\n\n def __init__(self,expand, goal_test, heuristic, root):\n self.expand = expand\n self.goal_test = goal_test\n self.heuristic = heuristic\n self.root = root\n\n\n def search(self):\n return self.search(self.root, float('inf'))\n\n\n def search(self,root,f_limit):\n # The node we are at is the goal state return a solution\n if self.goal_test(root): self.constructsolution(root)\n\n successors = self.expand(root)\n\n if len(successors) == 0: return 'Failure',float('inf') # this node is a failure\n\n for s in successors:\n s.fcost = max(s.gcost + s.hcost, root.fcost)\n heapq.heapify(successors)\n while len(successors) > 0:\n best = heapq.heappop(successors) # get the best cost so far\n if best.fcost > f_limit: return 'Failure', best.fcost\n alternative = successors[0].fcost # get the second best\n result,best.fcost = self.search(best, min(f_limit, alternative))\n if result != 'Failure': return result\n return 'Failure', float('inf')\n\n\n\n def constructsolution(self, node):\n solution = []\n cur_node = node\n while cur_node != None:\n solution.append(cur_node)\n cur_node = cur_node.parent\n return solution\n","sub_path":"search/rbfs.py","file_name":"rbfs.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"411887014","text":"\"\"\"add computational level to lambda\n\nRevision ID: e527d9001345\nRevises: e809b7eb2bcc\nCreate Date: 2018-12-13 19:52:56.453443\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'e527d9001345'\ndown_revision = 'e809b7eb2bcc'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n level = postgresql.ENUM('COMPUTABLE', 'QUERYABLE', 'LEARNABLE', name='level', create_type=False)\n level.create(op.get_bind())\n op.add_column('lambdas', sa.Column('level', level, nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('lambdas', 'level')\n op.execute(\"DROP TYPE level;\")\n # ### end Alembic commands ###\n","sub_path":"superset/migrations/versions/e527d9001345_add_computational_level_to_lambda.py","file_name":"e527d9001345_add_computational_level_to_lambda.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"527575755","text":"# This file is part of QSM-blender-addons.\n# \n# QSM-blender-addons is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n# \n# QSM-blender-addons 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 General Public License for more details.\n# \n# You should have received a copy of the GNU General Public License\n# along with QSM-blender-addons. If not, see .\n\nbl_info = {\n \"name\": \"Tree model (QSM) and leaf model (L-QSM) importer\",\n \"category\": \"Learnbgame\",\n \"author\": \"Markku Åkerblom\",\n \"version\": (0, 8, 0),\n \"blender\": (2, 79 ,0),\n \"location\": \"View 3D > Tool Shelf > QSM\",\n \"description\": \"Addon that imports Quantitative Structure Models as either individual cylinders, or as continuous surfaces. A branch is either a collection of Bezier line segments (cylinders) or a Bezier curve. Each type of Bezier curve is lofted by applying a bevel object such as a Bezier circle. The add-on can also import leaf models in Wavefront OBJ format. A single leaf should be either a triangle or a rectangle.\",\n 'support': 'TESTING',\n}\n\nimport bpy\nimport sys\nimport os\nimport math\nimport bmesh\nimport mathutils\nfrom mathutils import Vector, Matrix\nimport datetime\nimport numpy as np\nfrom random import uniform\n\n# Print progress in the console every Nth row.\ndef print_progress(NLine, iLine, d, last):\n \n # Current percentage with precision d.\n p = float(math.floor(d*iLine/NLine))/d\n\n # Display if first line, or if percentage has changed.\n if iLine == 0 or p > last:\n\n # Update last percentage.\n last = p\n\n # Number of digits in line count.\n w = len(str(NLine))\n\n # Message string.\n msg = \"Processing line %\" + str(w) + \"i of %i (%2i%%)\"\n\n # Format message with current numbers.\n msg = msg % (iLine+1, NLine,p*100)\n\n # Display message.\n sys.stdout.write(msg + chr(8) * len(msg))\n sys.stdout.flush()\n\n return last\n\nclass QSMPanel(bpy.types.Panel):\n \"\"\"Creates a Panel in the scene context of the properties editor\"\"\"\n\n bl_label = \"QSM Import\"\n bl_idname = \"SCENE_PT_qsm\"\n bl_space_type = 'VIEW_3D'\n bl_region_type = 'TOOLS'\n bl_category = \"QSM\"\n bl_context = \"objectmode\"\n\n # Layout of the QSM import panel.\n def draw(self, context):\n\n layout = self.layout\n scene = context.scene\n settings = scene.qsmImportSettings\n\n # Data variable for searching materials.\n data = bpy.data\n\n # Import mode as a button selector.\n row = layout.row()\n row.prop(settings, \"qsmImportMode\", expand=True)\n \n \n # Input file.\n row = layout.row()\n row.prop(settings, \"qsm_file_path\")\n\n # Stem material select.\n row = layout.row()\n row.prop_search(settings, \"qsmStemMaterial\", data, \"materials\")\n\n # Branch material select.\n row = layout.row()\n row.prop_search(settings, \"qsmBranchMaterial\", data, \"materials\")\n\n layout.separator()\n\n # Branch separation.\n row = layout.row()\n row.prop(settings, \"qsmSeparation\")\n\n #layout.separator()\n\n # UI elements for mesh objects.\n if settings.qsmImportMode == 'mesh_cylinder':\n\n # Vertex count inputs.\n row = layout.row()\n layout.label(\"Vertex count:\")\n\n row = layout.row(align=True)\n row.prop(settings,\"qsmVertexCountMin\", text='Min')\n row.prop(settings,\"qsmVertexCountMax\", text='Max')\n\n # UI elements for bezier objects.\n elif settings.qsmImportMode == 'bezier_cylinder' or \\\n settings.qsmImportMode == 'bezier_branch':\n\n # Bevel object generation.\n row = layout.row()\n row.prop(settings, \"qsmGenerateBevelObject\")\n\n if not settings.qsmGenerateBevelObject:\n\n # Bevel object selector.\n row = layout.row()\n row.prop_search(settings, \"qsmBevelObject\", scene, \"objects\")\n \n layout.separator()\n\n # Colormap update button.\n if settings.qsmImportMode == 'mesh_cylinder':\n row = layout.row()\n row.operator(\"qsm.update_colourmap\")\n\n # Import buttons.\n row = layout.row()\n row.operator(\"qsm.qsm_import\")\n\n\n\n \nclass LeafModelPanel(bpy.types.Panel):\n \"\"\"Creates a Panel in the scene context of the properties editor\"\"\"\n\n bl_label = \"Leaf model import\"\n bl_idname = \"SCENE_PT_leaf_model\"\n bl_space_type = 'VIEW_3D'\n bl_region_type = 'TOOLS'\n bl_category = 'QSM'\n bl_context = \"objectmode\"\n\n # Layout of the leaf model import panel.\n def draw(self, context):\n\n layout = self.layout\n scene = context.scene\n settings = scene.leafModelImportSettings\n\n # Data variable for searching materials.\n data = bpy.data\n\n # Input data type.\n row = layout.row()\n row.prop(settings, \"importType\", expand=False)\n \n # Input file.\n row = layout.row()\n row.prop(settings, \"leaf_model_file_path\")\n\n # Bevel object selector.\n row = layout.row()\n row.prop_search(settings, \"leafModelMaterial\", data, \"materials\") \n\n layout.separator()\n\n\n if settings.importType == 'obj_ext':\n\n # Boolean: generate vertex colors.\n row = layout.row()\n row.prop(settings, \"vertexColorGeneration\")\n\n # Color source.\n if settings.vertexColorGeneration:\n row = layout.row()\n row.prop(settings, \"vertexColorMode\", expand=False)\n\n # Boolean: generate shapekeys.\n row = layout.row()\n row.prop(settings, \"shapekeyGeneration\")\n\n # Boolean: generate UVs.\n row = layout.row()\n row.prop(settings, \"leafUvGeneration\")\n\n if settings.leafUvGeneration:\n layout.separator()\n\n # Dropdown: UV type.\n row = layout.row()\n row.prop(settings, \"leafUvType\", expand=False)\n\n if settings.leafUvType == 'custom':\n\n # Mesh selector: UV source mesh.\n row = layout.row()\n row.prop_search(settings, \"leafUvSource\", bpy.data, \"meshes\")\n\n layout.separator()\n \n # Import button.\n row = layout.row()\n row.operator(\"leaf.import_leaves\")\n\n\n\n\nclass ImportLeafModel(bpy.types.Operator):\n \"\"\"Import leaves as planes\"\"\"\n\n bl_idname = \"leaf.import_leaves\"\n bl_label = \"Import leaf model\"\n\n def import_obj(self, file_path):\n\n # Use built-in OBJ-importer with fixed parameters.\n bpy.ops.import_scene.obj \\\n (\n filepath=file_path,\n axis_forward='Y',\n axis_up='Z',\n filter_glob=\"*.obj;*.mtl\",\n use_edges=True,\n use_smooth_groups=True,\n use_split_objects=False,\n use_split_groups=False,\n use_groups_as_vgroups=False,\n use_image_search=False,\n split_mode='OFF',\n global_clamp_size=0\n )\n\n # Get imported objects, assumed to be selected.\n return bpy.context.selected_objects[:]\n\n def import_ext_obj(self, file_path, fShapeKeyGeneration, fVertexColor, color_mode):\n\n # Array of base vertices.\n base_vert = []\n # Array of base faces.\n base_face = []\n\n # Flag: vertex addition completed.\n fVertDone = False\n # Flag: face addition completed.\n fFaceDone = False\n # Flag: read vertex colors from file.\n fFromFile = False\n # Flag: randomize vertex colors.\n fRandomColor = False\n\n # Number of added base vertices.\n NVert = 0\n # Number of added face vertices.\n NFace = 0\n # Number of added leaves.\n NLeaf = 0\n\n # Resulting object.\n ob = None\n\n # Name of shape key for growth animation.\n GrowthName = 'ReverseGrowth'\n\n # Set boolean flags for simplicity.\n if color_mode == 'from_file':\n fFromFile = True\n elif color_mode == 'random':\n fRandomColor = True\n else:\n # Otherwise, ensure that vertex colors are not\n # added if color mode is unknown.\n fVertexColor = False\n\n with open(file_path) as lines:\n\n # Number of lines in input file.\n NLine = sum(1 for line in lines)\n\n # Last displayed percentage.\n PLast = 0\n\n # Number of digits to use in object naming.\n NDigit = len(str(NLine))\n \n # Return to file beginning for second iteration.\n lines.seek(0)\n \n # Iterate over rows in input file.\n for iLine, line in enumerate(lines):\n\n # Split row into parameters.\n params = line.split(' ',1)\n\n # Ignore rows with too few parameters.\n if len(params) > 2:\n continue\n \n # Print progress in the console every nth row.\n PLast = print_progress(NLine, iLine, 10, PLast)\n\n # Type of line.\n\n # Base vertex.\n if params[0] == 'v':\n\n # If vertex adding has been closed,\n # ignore further vertex lines.\n if fVertDone:\n continue\n\n # Get vertex coordinates.\n co = params[1].split()\n\n # Should have three coordinates.\n if len(co) != 3:\n continue\n\n # Append new base vertex.\n base_vert.append(np.array([float(co[0]), float(co[1]), float(co[2])]))\n\n # Increase base vertex count.\n NVert += 1\n\n # Base face.\n elif params[0] == 'f':\n\n # If face adding has been closed,\n # ignore further face lines.\n if fFaceDone:\n continue\n\n # Close vertex adding.\n if not fVertDone:\n fVertDone = True\n\n # Indices of face vertices.\n ind = params[1].split()\n\n # Faces have to have at least three vertices.\n if len(ind) < 3:\n continue\n\n # Append new face.\n base_face.append(np.array([int(x)-1 for x in ind]))\n\n # Increase base face count.\n NFace += 1\n\n # Leaf transformation parameters.\n elif params[0] == 'L':\n\n # Close vertex and face adding.\n if not fFaceDone:\n fFaceDone = True\n fVertDone = True\n\n # If no geometry, unable to create leaf.\n if NVert == 0 or NFace == 0:\n self.report({'ERROR_INVALID_INPUT'}, \\\n 'Input file missing vertices or faces.')\n break\n\n # Transformation configuration.\n config = params[1].split()\n\n # Line should have at least 15 parameters.\n if len(config) < 15:\n continue\n\n # Check that vertex color values are present in file,\n # if vertex color generation active.\n # Otherwise, ignore line.\n if fVertexColor and fFromFile:\n if len(config) < 18:\n continue\n\n # Increase leaf count.\n NLeaf += 1\n\n # Initialize object and mesh data, and optionally\n # shape key and color map layers.\n if NLeaf == 1:\n\n # Create mesh.\n me = bpy.data.meshes.new('LeafModel')\n\n # Create object.\n ob = bpy.data.objects.new('LeafModel',me)\n\n # Bmesh.\n bm = bmesh.new()\n\n if fShapeKeyGeneration:\n # Add default shape key.\n ob.shape_key_add('Basis')\n # Additional shape key for growth animation.\n sk = bm.verts.layers.shape.new(GrowthName)\n\n if fVertexColor:\n # Create new layer for colourmap.\n cl = bm.loops.layers.color.new(\"Color\")\n\n if fShapeKeyGeneration:\n # Start point of twig used for growth animation.\n twig_start = tuple([float(x) for x in config[0:3]])\n\n # Leaf parameters.\n leaf_start = np.array([float(x) for x in config[3:6]])\n leaf_dir = np.array([float(x) for x in config[6:9]])\n leaf_normal = np.array([float(x) for x in config[9:12]])\n leaf_scale = np.array([float(x) for x in config[12:15]])\n\n # Get vertex color value if necessary.\n if fVertexColor:\n # Additional color elements should be present on line.\n if fFromFile:\n vert_color = [float(x) for x in config[15:18]]\n # Generate random 3-element array from uniform distribution.\n elif fRandomColor:\n vert_color = [uniform(0,1) for x in \"rgb\"]\n\n\n # Scaling.\n vert = np.multiply(base_vert,leaf_scale)\n\n # Coordinate change matrix.\n E = np.array([np.cross(leaf_normal, leaf_dir), leaf_dir, leaf_normal])\n\n # Rotation.\n vert = np.dot(vert, E)\n\n # Transition.\n vert += leaf_start\n\n # Added vertices.\n bm_vert = []\n\n # Add vertices to bmesh.\n for v in vert:\n\n # Add vertex.\n bv = bm.verts.new(tuple(v))\n \n if fShapeKeyGeneration:\n # Set shape key value.\n bv[sk] = twig_start\n\n # Append to list.\n bm_vert.append(bv)\n\n # Convert to numpy array for easy indexing.\n bm_vert = np.array(bm_vert)\n\n # Iterate over face indices in base.\n for f in base_face:\n\n # Create new face to mesh.\n bf = bm.faces.new(tuple(bm_vert[f]))\n\n # If vertex color information is present in the input file\n # add color layer and assign color for each vertex.\n if fVertexColor:\n for loop in bf.loops:\n loop[cl] = vert_color\n \n\n # Unknown line type.\n else:\n continue\n\n if ob is not None:\n\n # Bind bmesh to mesh.\n bm.to_mesh(me) \n\n # Link object to scene.\n scene = bpy.context.scene\n scene.objects.link(ob)\n\n # Set as active and selected.\n scene.objects.active = ob\n ob.select = True\n\n # Return a list of objects for compatibility\n # with OBJ-importer.\n return [ob]\n else:\n # Return empty array if no object was created.\n return []\n \n # Operator for importing leaf model.\n def execute(self, context):\n\n print('Importing leaves.')\n\n # Current scene for reading parameters for importing. \n scene = context.scene\n settings = scene.leafModelImportSettings\n \n # Path to input file.\n filestr = settings.leaf_model_file_path\n\n # Check for empty filepath.\n if len(filestr) == 0:\n self.report({'ERROR_INVALID_INPUT'},'Missing input file path.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Convert to absolute path.\n file_path = bpy.path.abspath(filestr)\n\n # Check that file exists.\n if not os.path.isfile(file_path):\n self.report({'ERROR_INVALID_INPUT'},'No file with given path.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Check if UVs are to be generated.\n fUvGeneration = settings.leafUvGeneration\n\n if fUvGeneration:\n if settings.leafUvType == 'custom':\n SourceName = settings.leafUvSource\n\n UvSource = bpy.data.meshes.get(SourceName)\n\n if not UvSource:\n self.report({'ERROR_INVALID_INPUT'}, \\\n 'Custom UV generation selected, but UV mesh not found.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Record start time.\n start = datetime.datetime.now()\n \n # Format of input data.\n import_type = settings.importType\n\n # Import using built-in OBJ-importer.\n if import_type == 'obj':\n leaf_objects = self.import_obj(file_path)\n\n # Import using custom extended OBJ-format.\n elif import_type == 'obj_ext':\n # Check if shape keys are to be generated.\n fShapeKeyGeneration = settings.shapekeyGeneration\n\n # Check if vertex colors should be assigned.\n fVertexColor = settings.vertexColorGeneration\n\n # Vertex color mode.\n color_mode = settings.vertexColorMode\n\n leaf_objects = self.import_ext_obj(file_path, \\\n fShapeKeyGeneration, \\\n fVertexColor, \\\n color_mode)\n\n # If import generated no objects, stop execution.\n if len(leaf_objects) == 0:\n self.report({'ERROR_INVALID_INPUT'},'No leaf object generated!')\n return {'CANCELLED'}\n\n # Selected material for leaves.\n matname = settings.leafModelMaterial\n\n # Check if material selected.\n if len(matname) == 0:\n print('No material set.')\n mat = None\n else:\n # Try to get material by input name.\n mat = bpy.data.materials.get(matname)\n\n # Print warning if does not exist.\n if not mat:\n print('Material not found.')\n\n # Flag: skip UV generation due to input errors.\n fSkipUv = False\n \n if fUvGeneration:\n\n # Name of the UV map to be created. Using \"Overlapping\" because\n # all leaves are overlayed in UV coordinates, to allow simple \n # UV mapping of a single leaf image.\n MapName = 'Overlapping'\n\n leafUvType = settings.leafUvType\n\n \n if leafUvType == 'isosceles_triangle':\n # UV vertex locations for a isosceles triangle.\n uv_verts = [Vector((1,0)), Vector((0.5,1)), Vector((0,0))]\n \n elif leafUvType == 'square':\n # UV vertex locations for a square.\n uv_verts = [Vector((1,0)), Vector((1,1)),Vector((0,1)), Vector((0,0))]\n\n elif leafUvType == 'custom':\n\n # Initialize array.\n uv_verts = []\n\n # Copy local (x,y)-coordinates of source mesh.\n for v in UvSource.vertices:\n x = v.co[0]\n y = v.co[1]\n uv_verts.append(Vector((x,y))) \n\n else:\n # Otherwise, the selection is illegal.\n self.report({'WARNING'},'Unknown UV generation type selected. UV generation skipped.')\n fSkipUv = True\n\n # Number of vertices in input UV map.\n NVert = len(uv_verts)\n \n \n\n # Iterate over selected objects.\n for obj in leaf_objects:\n \n # Mesh of selected object.\n me = obj.data\n\n # Set material if exists.\n if mat:\n me.materials.append(mat)\n\n # UV map creation.\n if fUvGeneration and not fSkipUv: \n \n # Create new UV map.\n me.uv_textures.new(MapName)\n \n # Create a bmesh from mesh data for UV map manipulation.\n bm = bmesh.new()\n bm.from_mesh(me)\n \n # Create UV layer.\n uv_layer = bm.loops.layers.uv[0]\n \n # Initialize lookup table.\n bm.faces.ensure_lookup_table()\n \n # Number of leaves.\n NFace = len(bm.faces)\n\n # Iterate over leaves.\n for iFace in range(NFace):\n\n # Number of vertices in face.\n NFaceVert = len(bm.faces[iFace].loops)\n \n # Iterate over vertices in leaf.\n for iVert in range(NFaceVert):\n \n # Index of current vertex modulo set number of vertices.\n uv_index = iVert%NVert\n # Set UV map vertex to coordinate given by above index.\n bm.faces[iFace].loops[iVert][uv_layer].uv = uv_verts[uv_index]\n \n # Update mesh data.\n bm.to_mesh(me)\n \n # Record end time.\n end = datetime.datetime.now()\n\n # Compute duration.\n delta = end - start\n\n # Format duration as string.\n timestr = \"{:.1f}\".format(delta.total_seconds())\n\n # Print duration.\n print('Done is ' + timestr + ' seconds.')\n\n return {'FINISHED'}\n\n\nclass ImportQSM(bpy.types.Operator):\n \"\"\"Import QSM as a collection of individual cylinders\"\"\"\n\n bl_idname = \"qsm.qsm_import\"\n bl_label = \"Import\"\n\n def createQSMParent(self, scene):\n\n # Create empty parent object for the resulting object(s).\n EmptyParent = bpy.data.objects.new('TreeParent', None)\n EmptyParent.empty_draw_size = 1\n EmptyParent.empty_draw_type = 'PLAIN_AXES'\n EmptyParent.location = Vector((0.0,0.0,0.0))\n \n # Link empty to scene.\n scene.objects.link(EmptyParent)\n\n # Return parent object.\n return EmptyParent\n\n # Function to create mesh cylinder by copying the data\n # of a base object, and then transforming it.\n def addMeshCylinder(self, baseobj, cp, rot, h, r):\n\n # Copy base object and data.\n ob = baseobj.copy()\n ob.data = baseobj.data.copy()\n\n # Translate to center point.\n ob.location = cp\n\n # Scale to match cylinder radius and length.\n ob.scale = (r,r,h)\n\n # Set rotation.\n ob.rotation_euler = rot\n\n # Link new object to scene.\n bpy.context.scene.objects.link(ob)\n\n # Return new object.\n return ob\n\n # Function to import a QSM as mesh cylinders.\n def import_as_mesh_cylinders(self, context, file_path, EmptyParent, \n fBranchSeparation,\n matStem, matBranch):\n\n print('Importing QSM as mesh cylinders.')\n \n # Current scene to read properties.\n scene = context.scene\n settings = scene.qsmImportSettings\n \n # Number of imported branches.\n NBranch = 0\n # Index of last branch in input file.\n iLastBranch = 0\n\n # Flag: should the cylinder index be stored in a vertex layer.\n # Allows updating vertex colour afterwards.\n fIdColor = True\n\n # Minimum vertex count in cylinder rings.\n vmin = settings.qsmVertexCountMin\n # Maximum vertex count.\n vmax = settings.qsmVertexCountMax\n\n # Number of different vertex counts.\n vcount = vmax - vmin + 1\n\n # Initialize list of base objects that will be copied for optimization.\n baseobj = []\n\n # Generate a base object for each vertex count.\n for v in range(0,vcount):\n\n # Number of vertices in the rings of the current base object cylinder.\n nvert = vmin+v\n\n # Add a cylinder primitive with the built-in operator.\n bpy.ops.mesh.primitive_cylinder_add(vertices=nvert,\n radius=1,\n depth=1,\n end_fill_type='NGON',\n view_align=False,\n enter_editmode=False,\n location=(0.0,0.0,0.0),\n rotation=(0.0,0.0,0.0))\n\n # Get newly generated base object.\n ob = context.selected_objects[0]\n # Deselect.\n ob.select = False\n\n # Get mesh data of base object.\n me = ob.data\n\n # Translation vector.\n point = Vector((0.0,0.0,-0.5))\n\n # Move base object so that the starting point is at the origin.\n mat = Matrix.Translation(ob.matrix_world.translation - point)\n me.transform(mat)\n\n # Update mesh data.\n me.update()\n # Do opposite transition in object mode to get object origin corrected.\n ob.matrix_world.translation = point\n\n # Create bmesh object to set the shading of the envelope faces as smooth.\n bm = bmesh.new() \n bm.from_mesh(me)\n\n # Iterate over the faces other than the last two which are the caps.\n for f in bm.faces[-(nvert+2):]:\n if f.select:\n if len(f.verts) == 4:\n # Set shading smooth.\n f.smooth = True\n\n # Update mesh data and delete bmesh.\n bm.to_mesh(me)\n bm.free()\n\n # Add base object to list.\n baseobj.append(ob)\n\n # Minimum vertex count must be at least three.\n if vmin < 3:\n vmin = 3\n\n # Maximum count must be greater than the minimum.\n if vmax < vmin:\n vmax = vmin\n\n with open(file_path) as lines:\n \n # File line count.\n NLine = 0\n\n # Last displayed percentage.\n PLast = 0\n\n # Iterate through the file rows, recording the count and \n # minimum and maximum radius values.\n for line in lines:\n\n # Increase counter.\n NLine = NLine + 1\n\n # Split file line into parameter array.\n params = line.split()\n\n # Ignore rows with too few parameters.\n if len(params) < 9:\n continue\n\n # Get radius parameter of row.\n r = float(params[8])\n\n # Initialize min and max on first row.\n if NLine == 1:\n rmin = r\n rmax = r\n else:\n # Update extremes if necessary.\n if r < rmin:\n rmin = r\n if r > rmax:\n rmax = r\n \n # Number of digits to use in object naming.\n NDigit = len(str(NLine))\n \n # Return to file beginning for second iteration.\n lines.seek(0)\n\n # List of objects that are later joined to form either a branch or the full QSM.\n allobj = []\n\n # Index of current cylinder.\n iCyl = 0\n \n # Iterate over rows in input file.\n for iLine, line in enumerate(lines):\n\n # Split row into parameters.\n params = line.split()\n\n # Ignore rows with too few parameters.\n if len(params) < 9:\n continue\n\n # Increase number of cylinders.\n iCyl += 1\n \n # Print progress in the console every nth row.\n PLast = print_progress(NLine, iLine, 10, PLast)\n\n # Store parameters.\n iBranch = int(float(params[0]))\n sp = Vector((float(params[1]), float(params[2]), float(params[3])))\n ax = Vector((float(params[4]), float(params[5]), float(params[6])))\n h = float(params[7])\n r = float(params[8])\n\n # Convert axis to Euler rotation.\n rot = ax.to_track_quat('Z', 'Y').to_euler()\n\n # Select number of vertices based on linear interpolation of radius.\n nvert = vmin + (vmax-vmin)*(r-rmin)/(rmax-rmin)\n # Convert result to an integer by rounding.\n nvert = int(round(nvert))\n\n # Index of base object based on vertex count.\n iObj = nvert - vmin\n\n # Flag: file contains an additional row to use as colourmap values.\n fVertColor = False\n\n # Check if extra column for colourmap exists.\n if len(params) > 9:\n fVertColor = True\n VertColor = float(params[9])\n if len(params) > 11:\n VertColor = Vector((float(params[9]), float(params[10]), float(params[11])))\n\n # If this is the first branch, or the index differs form the previous row.\n if NBranch == 0 or iBranch != iLastBranch:\n\n # Update index of previous branch.\n iLastBranch = iBranch\n # Increase branch count.\n NBranch += 1\n\n # If multiple objects are created, use unique object and mesh names\n # by numbering them.\n if fBranchSeparation:\n meshname = \"branch_\" + str(NBranch).zfill(NDigit)\n objname = \"branch_\" + str(NBranch).zfill(NDigit)\n else:\n meshname = \"qsm_mesh\"\n objname = \"qsm\"\n\n # Start a new branch if this is the first one, or if the user\n # has selected to separate branches.\n if NBranch == 1 or fBranchSeparation:\n\n # If separate branches and not the first one.\n if NBranch > 1:\n\n # Select all objects in last branch.\n for ob in allobj:\n ob.select = True\n\n # Set one of them as active.\n scene.objects.active = allobj[0]\n # Join the objects.\n bpy.ops.object.join()\n # Apply object scale.\n bpy.ops.object.transform_apply(location=False, rotation=False, scale=True)\n # Deselect resulting object.\n allobj[0].select = False\n # Initialize array for next branch.\n allobj = []\n \n # Add current cylinder by copying and modifying the base object.\n ob = self.addMeshCylinder(baseobj[iObj],sp,rot,h,r)\n \n # Set name and parent.\n ob.name = objname\n ob.parent = EmptyParent\n\n # Get mesh data.\n me = ob.data\n # Set mesh name.\n me.name = meshname\n\n # Add stem material to object if it is the first branch,\n # or if branch material is not set (same material for all branches),\n # and if stem material is set.\n if iBranch == 1 or not matBranch:\n if matStem:\n me.materials.append(matStem)\n \n # Add branch material if material is set and it is different from the\n # stem material.\n if matBranch and (matStem != matBranch):\n me.materials.append(matBranch)\n\n # No need to change object even though the branch index changed.\n else:\n ob = self.addMeshCylinder(baseobj[iObj],sp,rot,h,r)\n\n # Still on same branch index.\n else:\n ob = self.addMeshCylinder(baseobj[iObj],sp,rot,h,r)\n\n # Add new object to list of cylinders in current branch.\n allobj.append(ob)\n\n # Get mesh data of new cylinder.\n me = ob.data\n # Create a bmesh object for colourmap creation from mesh data.\n bm = bmesh.new() \n bm.from_mesh(me)\n\n # If cylinder ID should be stored on the model.\n if fIdColor:\n # Get or create new layer for index colouring.\n layer = bm.verts.layers.int.get(\"CylinderId\")\n if not layer:\n layer = bm.verts.layers.int.new(\"CylinderId\")\n \n # Iterate over mesh vertices and set index colouring value to\n # index of the cylinder.\n for v in bm.verts:\n v[layer] = iCyl\n\n # If vertex colour information is present in the input file\n # add colour layer and assign colour for each vertex.\n if fVertColor:\n # Get or create new layer for input colourmap.\n colors = bm.loops.layers.color.get(\"Color\")\n if not colors:\n colors = bm.loops.layers.color.new(\"Color\")\n \n # Iterate over vertices and set vertex colour by\n # repeating the input value three times.\n for v in bm.verts:\n for loop in v.link_loops:\n if len(VertColor) > 1:\n loop[colors] = VertColor\n else:\n loop[colors] = [VertColor] * 3\n\n # Update mesh data and delete bmesh.\n bm.to_mesh(me)\n bm.free()\n \n # Select all cylinders of last object.\n for ob in allobj:\n ob.select = True\n\n # Set active object.\n scene.objects.active = allobj[0]\n\n # Join all cylinders in list.\n bpy.ops.object.join()\n # Apply object scale.\n bpy.ops.object.transform_apply(location=False, rotation=False, scale=True)\n\n\n # As clean-up, remove the base objects and their mesh data.\n for ob in baseobj:\n scene.objects.unlink(ob)\n bpy.data.objects.remove(ob)\n\n return {'FINISHED'}\n\n\n # Function to import a QSM as Bezier cylinders.\n def import_as_bezier_cylinders(self, context, file_path, EmptyParent, fBranchSeparation, \n matStem, matBranch, BevelObject):\n\n print('Importing QSM as Bezier cylinders.')\n \n # Current scene to read properties.\n scene = context.scene\n \n # Number of branches.\n NBranch = 0\n # Index of last branch.\n iLastBranch = 0\n\n # Length of right and left control handles.\n len_r = 0.45\n len_l = 0.45\n\n with open(file_path) as lines:\n \n # Count number of lines in file.\n NLine = sum(1 for line in lines)\n \n # Number of digits to use in object naming.\n NDigit = len(str(NLine))\n \n # If file did not have any lines.\n if NLine <= 0:\n self.report({'ERROR_INVALID_INPUT'},'Selected file is empty.')\n return {'CANCELLED'}\n\n # Return to file beginning.\n lines.seek(0)\n\n # Last displayed percentage.\n PLast = 0\n \n # Iterate over file rows.\n for iLine, line in enumerate(lines):\n\n # Split row into parameters.\n params = line.split()\n\n # Ignore rows with too few parameters.\n if len(params) < 9:\n continue\n \n # Print progress in the console every nth row.\n PLast = print_progress(NLine, iLine, 10, PLast)\n\n # Get cylinder parameters.\n iBranch = int(float(params[0]))\n sp = (float(params[1]), float(params[2]), float(params[3]))\n ax = (float(params[4]), float(params[5]), float(params[6]))\n h = float(params[7])\n r = float(params[8])\n\n # If the first branch or branch index has changed.\n if NBranch == 0 or iBranch != iLastBranch:\n\n # Update last index.\n iLastBranch = iBranch\n # Increase branch count.\n NBranch += 1\n\n # If multiple objects are created, use unique object and mesh names\n # by numbering them.\n if fBranchSeparation:\n curvename = \"branch_\" + str(NBranch).zfill(NDigit)\n objname = \"branch_\" + str(NBranch).zfill(NDigit)\n else:\n curvename = \"qsm_curve\"\n objname = \"qsm\"\n\n # Start a new branch if this is the first one, or if the user\n # has selected to separate branches.\n if NBranch == 1 or fBranchSeparation:\n\n # Create Bezier curve for new branch.\n curvedata = bpy.data.curves.new(name=curvename, type='CURVE')\n curvedata.dimensions = '3D'\n # Set bevel object.\n curvedata.bevel_object = BevelObject\n curvedata.use_fill_caps = True\n\n # Add stem material to object if it is the first branch,\n # or if branch material is not set (same material for all branches),\n # and if stem material is set.\n if iBranch == 1 or not matBranch:\n if matStem:\n curvedata.materials.append(matStem)\n \n # Add branch material if material is set and it is different from the\n # stem material.\n if matBranch and (matStem != matBranch):\n curvedata.materials.append(matBranch)\n\n # Create new object with the curve data.\n objectdata = bpy.data.objects.new(objname, curvedata)\n # Position to origin.\n objectdata.location = (0,0,0)\n # Parent to created empty.\n objectdata.parent = EmptyParent\n # Link to scene.\n bpy.context.scene.objects.link(objectdata)\n # Set as selected.\n objectdata.select = True\n\n # For each cylinder add a new Bezier spline into the curve data.\n polyline = curvedata.splines.new('BEZIER')\n # Add an extra point to have two in total.\n polyline.bezier_points.add(1)\n # Set order to one as the cylinder axis will be linear.\n polyline.resolution_u = 1\n\n # Create the starting (i == 0) and ending (i == 1) points of the spline.\n for i in (0,1):\n\n # Position on axis (0 = bottom, 1 = top).\n hf = i\n\n # Position of curve point.\n co = [sp_i+h*ax_i*hf for sp_i,ax_i in zip(sp,ax)]\n # Position of left handle.\n left = [sp_i+h*ax_i*hf-len_l*ax_i*h for sp_i,ax_i in zip(sp,ax)]\n # Position of right handle.\n right = [sp_i+h*ax_i*hf+len_r*ax_i*h for sp_i,ax_i in zip(sp,ax)]\n\n # Set Bezier curve point properties.\n polyline.bezier_points[i].co = co\n polyline.bezier_points[i].handle_left = left\n polyline.bezier_points[i].handle_right = right\n\n # Set curve point radius.\n polyline.bezier_points[i].radius = r\n\n # Assign proper materials from the splots.\n if iBranch == 1:\n if matStem:\n polyline.material_index = 0\n else:\n if matBranch or matStem:\n polyline.material_index = len(curvedata.materials)\n\n # Set curve resolution.\n polyline.resolution_u = 1\n polyline.use_endpoint_u = True\n\n\n # Function to add a branch-level Bezier spline to the given\n # curve data, from the given cylinder parameters.\n def addBezierCurve(self,curvedata, SP, AX, H, R):\n \n\n # Number of curve points = cylinder count + start point + end point.\n NPoint = len(R) + 2\n\n # Create new spline.\n polyline = curvedata.splines.new('BEZIER')\n # Add curve points to get NPoint points.\n polyline.bezier_points.add(NPoint - 1)\n\n # Iterate over cylinders in the input parameter lists.\n for i in range(0,NPoint):\n\n # Base of first cylinder.\n if i == 0:\n # Use normal radius.\n rf = 1 # Radius scaler\n # Point at the bottom of the cylinder.\n hf = 0 # Position along cylinder axis.\n j = 0 # Index of curve point.\n\n # Shorter handles as there are more than one curve point\n # \"on\" the first cylinder.\n len_r = 0.25 # Length of right handle.\n len_l = 0.25 # Length of left handle.\n\n # Tip of last cylinder.\n elif i == NPoint - 1:\n # Taper to 10% of radius.\n rf = 0.1\n # Point at the end of the cylinder.\n hf = 1\n # Last curve point.\n j = len(SP) - 1\n\n # Shorter handles as there are more than one curve point\n # \"on\" the last cylinder.\n len_l = 0.25\n len_r = 0.25\n\n # Curve points in the middle of the cylinders.\n else:\n # Normal radius.\n rf = 1\n # Point at the center of cylinder.\n hf = 0.5\n j = i - 1\n\n # Normal handle length, unless its the left handle of the\n # first cylinder, or the right handle of the last cylinder.\n len_r = 0.45\n len_l = 0.45\n\n # Middle of the first cylinder.\n if i == 1:\n len_l = 0.25\n\n # Middle of the last cylinder.\n if i == NPoint - 2:\n len_r = 0.25\n\n # Get parameters of current cylinder.\n sp = SP[j] # Start point\n ax = AX[j] # Axis\n h = H[j] # Length\n r = R[j] # Radius\n\n # Compute curve point location.\n co = [sp_i+h*ax_i*hf for sp_i,ax_i in zip(sp,ax)]\n # And handle locations.\n left = [sp_i+h*ax_i*hf-len_l*ax_i*h for sp_i,ax_i in zip(sp,ax)]\n right = [sp_i+h*ax_i*hf+len_r*ax_i*h for sp_i,ax_i in zip(sp,ax)]\n\n # Set curve point properties and radius.\n polyline.bezier_points[i].co = co\n polyline.bezier_points[i].handle_left = left\n polyline.bezier_points[i].handle_right = right\n polyline.bezier_points[i].radius = r*rf\n\n # Set curve resolution based on the number of curve points. \n # At most the resolution can be 10.\n polyline.resolution_u = min(NPoint - 2,10)\n polyline.use_endpoint_u = True\n\n # Return new spline.\n return polyline\n\n\n \n # Function to import a QSM as branch-level bevelled Bezier curves.\n def import_as_bezier_curves(self, context, file_path, EmptyParent, \n fBranchSeparation,\n matStem, matBranch, BevelObject):\n\n print('Importing QSM as Bezier curves.')\n \n # Current scene to read properties.\n scene = context.scene\n \n # Number of branches.\n NBranch = 0\n # Index of last branch.\n iLastBranch = 0\n\n # Initialize arrays to collect branch cylinder parameters.\n sp = []\n ax = []\n h = []\n r = []\n\n with open(file_path) as lines:\n \n # Count number of lines in file.\n NLine = sum(1 for line in lines)\n \n # Number of digits to use in object naming.\n NDigit = len(str(NLine))\n \n # If file had any rows.\n if NLine > 0:\n\n # If multiple objects are created, use unique object and mesh names\n # by numbering them.\n if fBranchSeparation:\n curvename = \"branch_\" + str(1).zfill(NDigit)\n objname = \"branch_\" + str(1).zfill(NDigit)\n else:\n curvename = \"qsm_curve\"\n objname = \"qsm\"\n\n # Create new curve to hold splines.\n curvedata = bpy.data.curves.new(name=curvename, type='CURVE')\n curvedata.dimensions = '3D'\n # Set bevel object and fill caps.\n curvedata.bevel_object = BevelObject\n curvedata.use_fill_caps = True\n\n # Add stem material to first object.\n if matStem:\n curvedata.materials.append(matStem)\n \n # Add branch material to first object if present.\n if matBranch and (matStem != matBranch):\n curvedata.materials.append(matBranch)\n\n # Create new object with curve data.\n objectdata = bpy.data.objects.new(objname, curvedata)\n # Set position to origin.\n objectdata.location = (0,0,0)\n # Parent to created empty.\n objectdata.parent = EmptyParent\n # Set selected.\n objectdata.select = True\n\n # Link to scene.\n scene.objects.link(objectdata)\n\n # Otherwise the file was empty.\n else:\n self.report({'ERROR_INVALID_INPUT'},'Selected file is empty.')\n return {'CANCELLED'}\n \n # Move back to file beginning.\n lines.seek(0)\n\n # Last displayed percentage.\n PLast = 0\n \n # Iterate over file rows.\n for iLine, line in enumerate(lines):\n\n # Split row into parameters.\n params = line.split()\n\n # Ignore rows with too few parameters.\n if len(params) < 9:\n continue\n\n # Print progress in the console every nth row.\n PLast = print_progress(NLine, iLine, 10, PLast)\n\n # Get cylinder parameters.\n iBranch = int(float(params[0]))\n\n # On first branch.\n if NBranch < 1:\n # Set count to one.\n NBranch = 1\n # Set last index to first index.\n iLastBranch = iBranch\n\n\n # If new branch begins, complete the last branch.\n if iBranch != iLastBranch:\n\n # Add new spline with the cylinder parameter arrays.\n polyline = self.addBezierCurve(curvedata, sp, ax, h, r)\n\n # If the branch index of the branch to complete is one,\n # assign stem material.\n if iLastBranch == 1:\n polyline.material_index = 0\n # Otherwise, assign last material slot, which is stem\n # material if its the only material and branch material\n # if it is present.\n else:\n polyline.material_index = len(curvedata.materials)\n\n # Update last branch to current value.\n iLastBranch = iBranch\n # Increase branch count.\n ++NBranch\n\n # Empty parameter arrays.\n sp[:] = []\n ax[:] = []\n h[:] = []\n r[:] = []\n\n # If branches are separated, create new object with a\n # name based on index.\n if fBranchSeparation:\n curvename = \"branch_\" + str(NBranch).zfill(NDigit)\n objname = \"branch_\" + str(NBranch).zfill(NDigit)\n\n # New curve data.\n curvedata = bpy.data.curves.new(name=curvename, type='CURVE')\n curvedata.dimensions = '3D'\n curvedata.bevel_object = BevelObject\n curvedata.use_fill_caps = True\n\n # Add branch material to rest of the objects if present.\n if matBranch:\n curvedata.materials.append(matBranch)\n # Otherwise use stem material if given.\n elif matStem:\n curvedata.materials.append(matStem)\n\n # New object with curve data.\n objectdata = bpy.data.objects.new(objname, curvedata)\n # Position to origin.\n objectdata.location = (0,0,0)\n # Parent to empty.\n objectdata.parent = EmptyParent\n # Set selected.\n objectdata.select = True\n\n # Link to current scene.\n scene.objects.link(objectdata)\n\n\n # Append cylinder parameters from current file row to parameter\n # arrays.\n sp.append( (float(params[1]), float(params[2]), float(params[3])) )\n ax.append( (float(params[4]), float(params[5]), float(params[6])) )\n h.append( float(params[7]) )\n r.append( float(params[8]) )\n\n # Complete final branch.\n polyline = self.addBezierCurve(curvedata, sp, ax, h, r)\n\n # If the branch index of the branch to complete is one,\n # assign stem material.\n if iBranch == 1:\n polyline.material_index = 0\n # Otherwise, assign last material slot, which is stem\n # material if its the only material and branch material\n # if it is present.\n else:\n polyline.material_index = len(curvedata.materials)\n\n\n # Main function of the QSM import operator. \n def execute(self,context):\n\n # Record start time to compute duration.\n start = datetime.datetime.now()\n\n # Current scene for properties.\n scene = context.scene\n settings = scene.qsmImportSettings\n\n # Path to input file.\n filestr = settings.qsm_file_path\n\n # Check for empty filepath.\n if len(filestr) == 0:\n self.report({'ERROR_INVALID_INPUT'},'Missing input file path.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Convert to absolute path.\n file_path = bpy.path.abspath(filestr)\n\n # Check that file exists.\n if not os.path.isfile(file_path):\n self.report({'ERROR_INVALID_INPUT'},'No file with given path.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Import mode: mesh / bezier\n mode = settings.qsmImportMode\n # Flag: separate objects for each branch.\n fBranchSeparation = settings.qsmSeparation\n\n # Create empty parent for QSM object(s).\n EmptyParent = self.createQSMParent(scene)\n\n # If curve-based mode, check that bevel object is given and\n # exists.\n if mode == 'bezier_cylinder' or mode == 'bezier_branch':\n\n if settings.qsmGenerateBevelObject:\n\n # Get bevel object by name. This object is set as the bevel object\n # of all the Bezier curves.\n bpy.ops.curve.primitive_bezier_circle_add(radius=1,\n view_align=False, \n enter_editmode=False, \n location=(0, 0, 0))\n\n # Selected object is the added curve.\n BevelObject = context.selected_objects[0]\n\n # Bevel object name.\n BevelObject.name = 'BevelObject'\n BevelObject.parent = EmptyParent\n BevelObject.data.resolution_u = 5\n else:\n # Bevel object name.\n bevel_object_name = settings.qsmBevelObject\n # Get bevel object by name. This object is set as the bevel object\n # of all the Bezier curves.\n BevelObject = bpy.data.objects.get(bevel_object_name)\n\n # Check that bevel object exists.\n if not BevelObject:\n self.report({'ERROR_INVALID_INPUT'},'Missing bevel object.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Check that the object is a curve object.\n if BevelObject.type != 'CURVE':\n self.report({'ERROR_INVALID_INPUT'},'Bevel object has to be a curve.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Get stem material name.\n matname = settings.qsmStemMaterial\n\n # Check that values is not empty.\n if len(matname) == 0:\n # Warn that no material set.\n print('No stem material set.')\n matStem = None\n else:\n # Try to get material with given name.\n matStem = bpy.data.materials.get(matname)\n\n # Warn if not found.\n if not matStem:\n print('Stem material not found.')\n\n # Get branch material name.\n matname = settings.qsmBranchMaterial\n\n # Check that values is not empty.\n if len(matname) == 0:\n print('No branch material set.')\n matBranch = None\n else:\n # Try to get material with given name.\n matBranch = bpy.data.materials.get(matname)\n\n # Warn if not found.\n if not matBranch:\n print('Branch material not found.')\n\n # Mesh cylinder.\n if mode == 'mesh_cylinder':\n self.import_as_mesh_cylinders(context, file_path, EmptyParent, \n fBranchSeparation,\n matStem, matBranch)\n # Cylinder-level Bezier curves.\n elif mode == 'bezier_cylinder':\n self.import_as_bezier_cylinders(context, file_path, EmptyParent, \n fBranchSeparation,\n matStem, matBranch, BevelObject)\n # Branch-level Bezier curves.\n elif mode == 'bezier_branch':\n self.import_as_bezier_curves(context, file_path, EmptyParent, \n fBranchSeparation,\n matStem, matBranch, BevelObject)\n\n # Record end time.\n end = datetime.datetime.now()\n # Compute duration.\n delta = end - start\n # Format duration as string.\n timestr = \"{:.1f}\".format(delta.total_seconds())\n\n # Display import duration in the console.\n sys.stdout.write(\"Processing finished in \" + timestr + \" sec\" + \" \"*100+\"\\n\")\n sys.stdout.flush()\n\n return {'FINISHED'}\n\n\n# Operator for updating the colourmap information of a mesh based QSM object,\n# without re-importing the geometry.\nclass UpdateMeshQSMColorMap(bpy.types.Operator):\n \"\"\"Read colourmap values from file and update selected mesh object vertex colors\"\"\"\n\n bl_idname = \"qsm.update_colourmap\"\n bl_label = \"Update colourmap\"\n\n # Main function of the colourmap update operator.\n def execute(self,context):\n\n print('Importing QSM as Bezier cylinders.')\n\n # Record start time to compute duration.\n start = datetime.datetime.now()\n \n # Current scene for properties.\n scene = context.scene\n settings = scene.qsmImportSettings\n\n # Object to update should be selected.\n ob = bpy.context.selected_objects[0]\n\n # If no selection or selected object is not a mesh.\n if not ob:\n self.report({'ERROR_INVALID_INPUT'},'No object selected.')\n return {'CANCELLED'}\n elif ob.type != 'MESH':\n self.report({'ERROR_INVALID_INPUT'},'Selected object is not a mesh.')\n return {'CANCELLED'}\n\n # Path to input file.\n file_path = bpy.path.abspath(settings.qsm_file_path)\n\n # Check that file exists.\n if not os.path.isfile(file_path):\n self.report({'ERROR_INVALID_INPUT'},'No file with given path.')\n print('Cancelled.')\n return {'CANCELLED'}\n\n # Mesh data of selected object.\n me = ob.data\n # Create bmesh object for data modification.\n bm = bmesh.new()\n bm.from_mesh(me)\n\n # Get integer layer that contains cylinder ID information,\n # crucial for updating.\n layer = bm.verts.layers.int.get(\"CylinderId\")\n\n # If layer does not exist, unable to update.\n if not layer:\n self.report({'ERROR_INVALID_INPUT'},'Selected object does not contain cylinder id info.')\n return {'CANCELLED'}\n\n with open(file_path) as lines:\n \n # Count number of lines in file.\n NLine = sum(1 for line in lines)\n \n # Number of digits to use in object naming.\n NDigit = len(str(NLine))\n \n # Return to file beginning.\n lines.seek(0)\n\n # (Vector) array to hold colourmap values of each cylinder.\n CylinderColors = []\n \n # Iterate over file rows.\n for iLine, line in enumerate(lines):\n\n # Split cylinder parameter on current row.\n params = line.split()\n\n # Ignore rows with too few parameters.\n if len(params) < 9:\n continue\n \n # Display progress every 100th row.\n if iLine == 0 or iLine%100 == 1:\n msg = \"Processing line %i of %i\" % (iLine+1, NLine)\n sys.stdout.write(msg + chr(8) * len(msg))\n sys.stdout.flush()\n\n # Append new colourmap value to array.\n if len(params) > 11:\n CylinderColors.append(Vector((float(params[9]), float(params[10]), float(params[11]))))\n else:\n CylinderColors.append(float(params[9]))\n\n # Get colour layer that holds colourmap data.\n colors = bm.loops.layers.color.get(\"Color\")\n # If layer does not exist, create new.\n if not colors:\n colors = bm.loops.layers.color.new(\"Color\")\n \n # Iterate over vertices in mesh.\n for v in bm.verts:\n\n # Cylinder index is read from CylinderId layer.\n iCyl = v[layer]\n\n # Update colour layer with new colour value.\n for loop in v.link_loops:\n if len(CylinderColors[iCyl-1]) > 1:\n loop[colors] = CylinderColors[iCyl-1]\n else:\n loop[colors] = [CylinderColors[iCyl-1]] * 3\n\n # Update mesh data.\n bm.to_mesh(me)\n # Free bmesh.\n bm.free()\n\n # Record end time.\n end = datetime.datetime.now()\n # Compute duration.\n delta = end - start\n # Format duration as string.\n timestr = \"{:.1f}\".format(delta.total_seconds())\n\n # Display import duration in the console.\n sys.stdout.write(\"Processing finished in \" + timestr + \" sec\" + \" \"*len(msg)+\"\\n\")\n sys.stdout.flush()\n\n return {'FINISHED'}\n\n\ndef min_update(self, context):\n mi = context.scene.qsmImportSettings.qsmVertexCountMin\n ma = context.scene.qsmImportSettings.qsmVertexCountMax\n\n if mi > ma:\n context.scene.qsmImportSettings.qsmVertexCountMax = mi\n\ndef max_update(self, context):\n mi = context.scene.qsmImportSettings.qsmVertexCountMin\n ma = context.scene.qsmImportSettings.qsmVertexCountMax\n\n if mi > ma:\n context.scene.qsmImportSettings.qsmVertexCountMin = ma\n\nclass QsmImportSettings(bpy.types.PropertyGroup):\n\n # Import mode variable.\n qsmImportMode = bpy.props.EnumProperty \\\n (\n name=\"QSM Import Mode\",\n description=\"Import mode determines the resulting object type\",\n items=[\n (\"mesh_cylinder\", \"Mesh cylinder\", \"Cylinder-level mesh elements\"),\n (\"bezier_cylinder\", \"Bezier cylinder\", \"Cylinder-level Bezier curves\"),\n (\"bezier_branch\", \"Bezier branch\", \"Branch-level Bezier curves\"),\n ]\n )\n\n # Flag: add new bezier circle as bevel object\n qsmGenerateBevelObject = bpy.props.BoolProperty \\\n (\n name = \"Create bevel object\",\n description = \"If checked, a new object will be created as the bevel object of all branches.\",\n default = True,\n subtype = 'NONE',\n )\n\n # Bevel object for curve based modes.\n qsmBevelObject = bpy.props.StringProperty \\\n (\n name=\"Bevel Object\",\n description=\"Object that is asigned as the bevel object of each curve\",\n )\n\n # Name of the stem material.\n qsmStemMaterial = bpy.props.StringProperty \\\n (\n name=\"Stem material\",\n description=\"Material to apply to stem after import\"\n )\n\n # Name of the branch material.\n qsmBranchMaterial = bpy.props.StringProperty \\\n (\n name=\"Branch material\",\n description=\"Material to apply to branches after import\"\n )\n\n # Flag: separate object for each branch.\n qsmSeparation = bpy.props.BoolProperty \\\n (\n name = \"Branch separation\",\n description = \"If enabled each branch results in a separate object.\",\n default = False,\n subtype = 'NONE',\n )\n\n # Path to input file with cylinder parameters.\n qsm_file_path = bpy.props.StringProperty \\\n (\n name = \"Input file\",\n default = \"\",\n description = \"TXT-file containing the cylinder parameters\",\n subtype = 'FILE_PATH'\n )\n\n # Minimum cylinder ring vertex count.\n qsmVertexCountMin = bpy.props.IntProperty \\\n (\n name = \"Vertex count minimum\",\n default = 16,\n min = 3,\n max = 50,\n subtype = 'NONE',\n description = \"Minimum number of vertices in mesh cylinders\",\n update = min_update\n )\n\n # Minimum and maximum cylinder ring vertex count.\n qsmVertexCountMax = bpy.props.IntProperty \\\n (\n name = \"Vertex count maximum\",\n default = 16,\n min = 3,\n max = 50,\n subtype = 'NONE',\n description = \"Maximum number of vertices in mesh cylinders\",\n update = max_update\n )\n\nclass LeafModelImportSettings(bpy.types.PropertyGroup):\n\n # Dropdown: UV map types.\n importType = bpy.props.EnumProperty \\\n (\n name=\"Import format\",\n description=\"Format of import data\",\n items=[\n (\"obj\", \"Wavefront OBJ\", \"Wavefront OBJ\"),\n (\"obj_ext\", \"Exteded OBJ\", \"Exteded OBJ with transition parameters\"),\n ]\n )\n\n # Path to input file with leaf model parameters.\n leaf_model_file_path = bpy.props.StringProperty \\\n (\n name = \"Input file\",\n default = \"\",\n description = \"TXT-file containing the leaf parameters\",\n subtype = 'FILE_PATH'\n )\n\n # Name of the leaf material.\n leafModelMaterial = bpy.props.StringProperty \\\n (\n name=\"Material\",\n description=\"Material to apply to object(s) after import\"\n )\n\n # Leaf model vertex count.\n leaf_model_vertex_count = bpy.props.IntProperty \\\n (\n name = \"Vertex count\",\n default = 4,\n min = 3,\n max = 4,\n description = \"Number of vertices in a single leaf\",\n subtype = 'UNSIGNED'\n )\n\n # Flag: vertex color generation.\n vertexColorGeneration = bpy.props.BoolProperty \\\n (\n name = \"Assign vertex colors\",\n description = \"Input file contains color values that should be assigned to resulting vertices\",\n default = False,\n subtype = 'NONE',\n )\n\n vertexColorMode = bpy.props.EnumProperty \\\n (\n name=\"Color source\",\n description=\"Source of vertex colors\",\n items=[\n (\"from_file\", \"From file\", \"Read from file\"),\n (\"random\", \"Randomize\", \"Randomize values\"),\n ]\n )\n\n # Flag: shape key generation for animation.\n shapekeyGeneration = bpy.props.BoolProperty \\\n (\n name = \"Generate shape keys\",\n description = \"Generate shapekeys for growth animation\",\n default = False,\n subtype = 'NONE',\n )\n\n # Leaf UV\n\n # Flag: generate UV map for leaves.\n leafUvGeneration = bpy.props.BoolProperty \\\n (\n name = \"Generate UV map\",\n description = \"Generate UV map for imported leaves.\",\n default = False,\n subtype = 'NONE',\n )\n\n # Dropdown: UV map types.\n leafUvType = bpy.props.EnumProperty \\\n (\n name=\"UV map type\",\n description=\"Leaf UV map type\",\n items=[\n (\"isosceles_triangle\", \"Isosceles triangle\", \"Isosceles triangle\"),\n (\"square\", \"Unit square\", \"Unit square\"),\n (\"custom\", \"Custom\", \"Custom vertices from object\"),\n ]\n )\n\n # Mesh to base UV map on.\n leafUvSource = bpy.props.StringProperty \\\n (\n name=\"UV mesh data\",\n description=\"Object to base the generated UV map on.\",\n )\n\ndef register():\n\n # QSM settings class.\n bpy.utils.register_class(QsmImportSettings)\n\n # Leaf model settings class.\n bpy.utils.register_class(LeafModelImportSettings)\n\n # Pointer to store all QSM import settings.\n bpy.types.Scene.qsmImportSettings = bpy.props.PointerProperty \\\n (\n type=QsmImportSettings\n )\n\n # Pointer to store all leaf model import settings.\n bpy.types.Scene.leafModelImportSettings = bpy.props.PointerProperty \\\n (\n type=LeafModelImportSettings\n )\n\n # Register classes.\n\n # Update colourmap operator.\n bpy.utils.register_class(UpdateMeshQSMColorMap)\n # QSM import operator.\n bpy.utils.register_class(ImportQSM)\n # Leaf import operator.\n bpy.utils.register_class(ImportLeafModel)\n # QSM panel\n bpy.utils.register_class(QSMPanel)\n # Leaf panel.\n bpy.utils.register_class(LeafModelPanel)\n\n\n\n\n\n\n\ndef unregister():\n # Delete custom properties from scene.\n del bpy.types.Scene.qsmImportSettings\n del bpy.types.Scene.leafModelImportSettings\n\n # Unregister classes.\n bpy.utils.unregister_class(LeafModelPanel)\n bpy.utils.unregister_class(QSMPanel)\n bpy.utils.unregister_class(ImportQSM)\n bpy.utils.unregister_class(UpdateMeshQSMColorMap)\n bpy.utils.unregister_class(ImportLeafModel)\n bpy.utils.unregister_class(QsmImportSettings)\n bpy.utils.unregister_class(LeafModelImportSettings)\n\n\n\n\n\n\nif __name__ == \"__main__\":\n register()\n","sub_path":"All_In_One/addons/qsm_leaf_import.py","file_name":"qsm_leaf_import.py","file_ext":"py","file_size_in_byte":69782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"216554839","text":"from datetime import datetime\r\nfrom django import forms\r\nfrom django.contrib.auth.models import User\r\nfrom post.models import Post, Comment, Reply, Tag\r\nfrom django.utils.html import strip_tags\r\n\r\nclass PostFullForm(forms.ModelForm):\r\n author = forms.ModelChoiceField(queryset=User.objects.all(),required=False)\r\n title = forms.CharField(min_length=6,required=True)\r\n text = forms.CharField(required=True)\r\n cover = forms.FileField(widget=forms.ClearableFileInput(attrs={'multiple': False}),required=False)\r\n status = forms.CharField(min_length=1,max_length=1,required=True)\r\n tags = forms.ModelChoiceField(queryset=User.objects.all(),required=False)\r\n notes = forms.CharField(required=False)\r\n\r\n class Meta:\r\n model = Post\r\n fields = ('title','author','text','cover','status','tags','notes')\r\n\r\nclass SlugForm(forms.Form):\r\n slug = forms.CharField(required=True)\r\n\r\nclass PostForm(forms.ModelForm):\r\n title = forms.CharField(min_length=6,required=True)\r\n text = forms.CharField(required=True)\r\n status = forms.CharField(min_length=1,max_length=1,required=True)\r\n cover = forms.FileField(widget=forms.ClearableFileInput(attrs={'multiple': False}),required=False)\r\n author = forms.ModelChoiceField(queryset=User.objects.all(),required=False)\r\n\r\n def clean_text(self):\r\n text = strip_tags(self.cleaned_data['text'])\r\n if len(text) is 0:\r\n raise forms.ValidationError(\"This field is required.\")\r\n elif len(text) < 30:\r\n raise forms.ValidationError(\"Ensure this value has at least 30 characters (it has \"+str(len(text))+\").\")\r\n return self.cleaned_data['text']\r\n\r\n class Meta:\r\n model = Post\r\n fields = ('title','text','status','cover','author','notes')\r\n\r\nclass AddCommentForm(forms.Form):\r\n text = forms.CharField(label='text')\r\n class Meta:\r\n model = Comment\r\n fields = ('text')\r\n\r\nclass AddReplyForm(forms.Form):\r\n text = forms.CharField(label='text')\r\n class Meta:\r\n model = Reply\r\n fields = ('text')\r\n\r\nclass CommentForm(forms.Form):\r\n comment_for = forms.IntegerField(widget=forms.HiddenInput())\r\n text = forms.CharField(label='text',)\r\n created_on = forms.DateField(label='created_on',)\r\n username = forms.CharField(label='username',)\r\n\r\nclass ReplyForm(forms.Form):\r\n comment_for = forms.IntegerField(widget=forms.HiddenInput())\r\n comment_by = forms.IntegerField(widget=forms.HiddenInput())\r\n text = forms.CharField(label='text',)\r\n created_on = forms.DateField(label='created_on',)\r\n username = forms.CharField(label='username',)\r\n\r\nclass SearchPostForm(forms.Form):\r\n slug = forms.CharField(label='slug',widget=forms.TextInput(attrs={'class' : 'form-control','placeholder' : 'Slug'}))\r\n\r\nclass ReportForm(forms.Form):\r\n report=forms.CharField(widget=forms.Textarea(attrs={'class' : 'form-control','placeholder':'Enter Report Description'}))\r\n","sub_path":"post/api/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"589823568","text":"from tqdm import tqdm\nfrom collections import Counter\nfrom itertools import combinations\nfrom nltk.stem import PorterStemmer\nimport joblib \nfrom sklearn.metrics import silhouette_score, davies_bouldin_score\nfrom sklearn.metrics.pairwise import euclidean_distances\n\nimport imp\nimport copy\nimport pickle\nimport multiprocessing\n\nimport numpy as np\nimport pandas as pd\nimport utils as my_utils\nimport ELJST_script_unigram as lda\nimport matplotlib.pyplot as plt\n \ngrid = [['amazon_home_20000', 'bert_attention_all', 5],\n ['amazon_home_20000', 'bert_attention_all', 25],\n ['amazon_home_20000', 'bert_attention_all', 50]]\n\n\ndef process_sampler(inp):\n \n dataset_name = inp[0]\n embedding_name = inp[1]\n n_topics = inp[2]\n \n print(dataset_name, embedding_name, \"entered\")\n\n dataset = pd.read_pickle(\"datasets/\" + dataset_name + \"_dataset\")\n \n min_df = 5\n max_df = .5\n maxIters = 20\n\n beta = .01\n gamma = 10\n lambda_param = 1.0\n n_sentiment = dataset.sentiment.unique().shape[0]\n\n alpha = 0.1/n_topics * np.ones(n_topics)\n gamma = [gamma/(n_topics*n_sentiment)]*n_sentiment\n\n similar_words = []\n \n if embedding_name == \"noembeds\":\n print(dataset_name+\"_\"+embedding_name, \"Created Embeddings\")\n similar_words = [{} for i in range(dataset.shape[0])]\n else:\n print(dataset_name+\"_\"+embedding_name, \"Loaded Embeddings\")\n similar_words = pickle.load(open(\"resources/\" + dataset_name + \"_\" + embedding_name + \".pickle\",\"rb\"))\n \n for s in similar_words:\n for i in s.keys():\n k = list(set(s[i]))\n if i in k:\n k.remove(i)\n s[i] = k\n\n sampler = lda.SentimentLDAGibbsSampler(n_topics, alpha, beta, gamma, numSentiments=n_sentiment, SentimentRange = n_sentiment, max_df = max_df, min_df = min_df, lambda_param = lambda_param)\n \n sampler._initialize_(reviews = dataset.text.tolist(), labels = dataset.sentiment.tolist())\n\n try:\n sampler.run(name=dataset_name+\"_\"+embedding_name+\"_\"+str(n_topics)+\"topics\", reviews=dataset.text.tolist(), labels=dataset.sentiment.tolist(), \n similar_words=similar_words, mrf=True, maxIters=maxIters, debug=False)\n\n joblib.dump(sampler, \"dumps/Uni_sampler_\" + dataset_name + \"_\" + embedding_name+\"_\"+str(n_topics)+\"topics\")\n print(dataset_name+\"_\"+embedding_name+\"_\"+str(n_topics)+\"topics\", \"dumped\") \n except Exception as e:\n print(e)\n print(dataset_name+\"_\"+embedding_name+\"_\"+str(n_topics)+\"topics\", \"failed\")\n \npool = multiprocessing.Pool(40)\npool.map(process_sampler, grid)\npool.close()\n\n# process_sampler(grid[0])","sub_path":"nosampler_uni.py","file_name":"nosampler_uni.py","file_ext":"py","file_size_in_byte":2705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"486992205","text":"#!/usr/bin/env python3\n'''\nSHORT DESCRIPTION\nUse the GMT 6 program earthtide to create a solid earth tide map of the area\n described by the given georeferenced file.\n\nFUTURE IMPROVEMENTS\n\nTESTING STATUS\nTested.\n'''\n\n### IMPORT MODULES ---\nimport argparse\nimport os\nimport matplotlib.pyplot as plt\nfrom IOsupport import load_gdal_dataset, load_gdal_datasets\nfrom GeoFormatting import get_raster_size, parse_transform\nfrom Viewing import raster_multiplot\n\n\n### PARSER ---\nDescription = '''Create a solid earth tide map of the area covered by a given map data set\nusing the GMT 6 program earthtide.\n\nFormat solid Earth command\ne.g., \"gmt earthtide -T2018-06-18T12:00:00 -GXXsolidtides_%s.grd -Ce,n,v -R80/90/30/40 -I15s\"\n'''\n\nExamples = ''''''\n\n\ndef createParser():\n parser = argparse.ArgumentParser(description=Description,\n formatter_class=argparse.RawTextHelpFormatter, epilog=Examples)\n\n InputArgs = parser.add_argument_group('INPUTS')\n InputArgs.add_argument('-f','--filename', dest='dsName', type=str, required=True,\n help='Filename of file for which tides are to be calculated.')\n InputArgs.add_argument('-d','--date', dest='date', type=str, required=True,\n help='Date on which the tides are to be calculated (fmt YYYYMMDD).')\n InputArgs.add_argument('-t','--time', dest='time', type=str, required=True,\n help='Time at which the tides are to be calculated (fmt HHmmSS).')\n\n OutputArgs = parser.add_argument_group('OUTPUTS')\n OutputArgs.add_argument('-o','--outName', dest='outName', type=str, default='Out', \n help='Output name.')\n OutputArgs.add_argument('-v','--verbose', dest='verbose', action='store_true', \n help='Verbose mode.')\n OutputArgs.add_argument('-p','--plot', dest='plot', action='store_true', \n help='Plot inputs and outputs.')\n return parser\n\ndef cmdParser(iargs = None):\n parser = createParser()\n return parser.parse_args(args=iargs)\n\n\n\n### TIDE MAP ---\nclass create_tide_map:\n def __init__(self, dsName, date, time, outName, verbose=False):\n '''\n Create one or more tide maps using GMT 6's earthtide functionality.\n This class \n\n Inherits\n os\n IOsupport: load_gdal_dataset, load_gdal_datasets\n GeoFormatting: get_raster_size, parse_transform\n Viewing: raster_multiplot\n '''\n # Parameters\n self.verbose = verbose\n\n # Extract spatial parameters from data set\n self.__get_spatial_data__(dsName)\n\n # Format date and time\n self.__format_date_time__(date, time)\n\n # Format outname\n self.__format_outname__(outName)\n\n # Create tide maps\n self.__create_tide_maps__()\n\n\n def __get_spatial_data__(self, dsName):\n '''\n Retrieve the necessary spatial information (bounds and resolution) from\n the specified GDAL-compatible data set.\n '''\n if self.verbose == True: print('Retrieving data set bounds and resolution')\n\n # Load GDAL data set\n DS = load_gdal_dataset(dsName)\n tnsf = DS.GetGeoTransform()\n M, N = get_raster_size(DS)\n\n # Parse geographic info\n geo = parse_transform(tnsf, M, N, verbose=inps.verbose)\n\n # Format geo string\n self.geoStr = '{left:f}/{right:f}/{bottom:f}/{top:f}'.format(**geo.__dict__)\n\n # Format resolution string\n self.resStr = '{:f}'.format(geo.dx)\n\n # Report if requested\n if self.verbose == True:\n print('--> Geo string: {:s}'.format(self.geoStr))\n print('--> Res string: {:s}'.format(self.resStr))\n\n\n def __format_date_time__(self, date, time):\n '''\n Format date into YYYY-MM-DD and time into HH:MM:SS format.\n '''\n if self.verbose == True: print('Formatting date and time')\n\n # Format date\n date = '{:s}-{:s}-{:s}'.format(date[0:4], date[4:6], date[6:8])\n\n # Format time\n time = '{:s}:{:s}:{:s}'.format(time[0:2], time[2:4], time[4:6])\n\n # Format datetime string\n self.datetimeStr = '{:s}T{:s}'.format(date, time)\n\n # Report if requested\n if self.verbose == True:\n print('date: {:s}\\ntime: {:s}'.format(date, time))\n print('--> Datetime string: {:s}'.format(self.datetimeStr))\n\n\n def __format_outname__(self, outName):\n '''\n Convert output name to GMT format.\n '''\n if self.verbose == True: print('Formatting outname')\n\n # Format outname\n self.outNameStr = '{:s}_%s.grd'.format(outName)\n\n # Report if requested\n if self.verbose == True:\n print('--> Output string: {:s}'.format(self.outNameStr))\n\n\n def __create_tide_maps__(self):\n '''\n Leverage GMT 6 to create solid Earth tide maps.\n '''\n if self.verbose == True: print('Creating solid Earth tide maps')\n \n # Format command string\n commandStr = 'gmt earthtide -T{datetimeStr:s} -G{outNameStr:s} -R{geoStr:s} -I{resStr:s} -Ce,n,v'.\\\n format(**self.__dict__)\n\n # Report if requested\n if self.verbose == True: print('full command:\\n{:s}'.format(commandStr))\n\n # Run command\n os.system(commandStr)\n\n # Report completion if requested\n if self.verbose == True: print('Tide maps generated.')\n\n\n def plot(self):\n '''\n Plot output tide maps.\n '''\n # Format tide map names\n etide = self.outNameStr.replace('%s', 'e')\n ntide = self.outNameStr.replace('%s', 'n')\n vtide = self.outNameStr.replace('%s', 'v')\n\n # Load data sets\n datasets = load_gdal_datasets([etide, ntide, vtide])\n\n # Plot data sets\n raster_multiplot(datasets.values(), ncols=3,\n cbarOrient='horizontal',\n titles=['east', 'north', 'vertical'])\n\n\n\n### MAIN ---\nif __name__ == '__main__':\n ## Inputs\n # Gather arguments\n inps = cmdParser()\n\n\n ## Tide maps\n tideMap = create_tide_map(dsName=inps.dsName,\n date=inps.date, time=inps.time,\n outName=inps.outName,\n verbose=inps.verbose)\n\n # Plot if requested\n if inps.plot == True:\n tideMap.plot()\n\n plt.show()","sub_path":"bin/compute_earthtide.py","file_name":"compute_earthtide.py","file_ext":"py","file_size_in_byte":6224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"148686498","text":"\"\"\"\n509. Fibonacci Number\n\nThe Fibonacci numbers, commonly denoted F(n) form a sequence, called the \nFibonacci sequence, such that each number is the sum of the two preceding ones,\nstarting from 0 and 1. That is,\n F(0) = 0, F(1) = 1\n F(N) = F(N - 1) + F(N - 2), for N > 1.\nGiven N, calculate F(N).\n\nExample 1:\nInput: 2\nOutput: 1\nExplanation: F(2) = F(1) + F(0) = 1 + 0 = 1.\n\nExample 2:\nInput: 3\nOutput: 2\nExplanation: F(3) = F(2) + F(1) = 1 + 1 = 2.\n\nExample 3:\nInput: 4\nOutput: 3\nExplanation: F(4) = F(3) + F(2) = 2 + 1 = 3.\n \nNote:\n0 ≤ N ≤ 30.\n\nSubmission Info:\n Runtime: 32ms, faster than 94.66%\n Memory Usage: 13.1MB, less than 73.69%\n\"\"\"\n\nclass Solution:\n def fib(self, N: int) -> int:\n fibonacci = [1, 1, 0]\n \n if N <= 2:\n return fibonacci[N - 1]\n \n for i in range(2, N):\n fibonacci[2] = fibonacci[0] + fibonacci[1]\n fibonacci[0] = fibonacci[1]\n fibonacci[1] = fibonacci[2]\n return fibonacci[-1]","sub_path":"0509. Fibonacci Number.py","file_name":"0509. Fibonacci Number.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"617043465","text":"name = input('Enter your name:')\nfor i in name:\n print(i.upper())\nsum = ''\ncondition = True\nwhile condition:\n string = input('Please enter any string: ')\n if string!='quit':\n sum+=string\n \n elif string=='quit':\n print(sum)\n break\n","sub_path":"loops/example4.py","file_name":"example4.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"370334579","text":"import adv_test\nimport adv\nfrom slot.a import *\n\ndef module():\n return Rodrigo\n\nclass Rodrigo(adv.Adv):\n a1 = ('a',0.08,'hp70')\n conf ={}\n conf['slot.a'] = TSO()+BN()\n\n\nif __name__ == '__main__':\n conf = {}\n conf['acl'] = \"\"\"\n `s1\n `s2\n `fs, seq=3 and cancel\n \"\"\"\n adv_test.test(module(), conf, verbose=0)\n\n","sub_path":"adv/rodrigo.py","file_name":"rodrigo.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"12157813","text":"from flask import Flask, jsonify, request\nfrom database import db_session, Produto, Tag\nfrom os import getenv\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = getenv('SECRET_KEY')\n\n\n@app.route('/find')\ndef find():\n from clarifai.rest import ClarifaiApp\n\n url = request.args['imageUrl']\n app = ClarifaiApp(api_key=getenv('CLARIFAI_KEY'))\n\n model = app.models.get(model_id='produtos')\n response = model.predict_by_url(url=url)\n concept = response['outputs'][0]['data']['concepts']\n concept = concept[0]\n\n if concept['value'] < 0.4:\n return jsonify({\n \"redirect_to_blocks\": [\"return\"]\n })\n\n obj = db_session.query(Produto).filter_by(clarifai_id=concept['name']).first()\n resp = {\n \"messages\": obj.message\n }\n return jsonify(resp)\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"553335377","text":"\nimport sqlite3\nfrom os.path import join, split\n\ndef dictionary_factory(cursor, row):\n \"\"\"\n Create a dictionary from rows in a cursor result.\n The keys will be the column names.\n :param cursor: A cursor from which a query row has just been fetched\n :param row: The query row that was fetched\n :return: A dictionary associating column names to values\n \"\"\"\n col_names = [d[0].lower() for d in cursor.description]\n return dict(zip(col_names, row))\n\ndef get_connection():\n dirname = split(__file__)[0]\n filename = join(dirname, \"measures.sqlite\")\n conn = sqlite3.connect(filename)\n conn.row_factory = dictionary_factory # note: no parentheses\n return conn\n\n\n\ndef do_command(cmd, args=[]):\n conn = get_connection()\n try:\n crs = conn.cursor()\n crs.execute(cmd, args)\n rtval = crs.fetchall()\n conn.commit()\n return rtval\n finally:\n conn.close()\n\ndef do_insert(cmd, args=[]):\n conn = get_connection()\n try:\n crs = conn.cursor()\n crs.execute(cmd, args)\n rtval = crs.lastrowid\n conn.commit()\n return rtval\n finally:\n conn.close()\n\n# def do_command_no_return(cmd, args=[]):\n# conn = get_connection()\n# try:\n# crs = conn.cursor()\n# crs.execute(cmd, args)\n# conn.commit()\n# finally:\n# conn.close()\n","sub_path":"Assignment 6/Resources/assign06_test/db_util/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"400831046","text":"import os\nimport numpy as np\nimport pandas as pd\nimport lightgbm as lgb\nfrom .model import Model\nfrom .util import Util\n\n# LightGBM\nclass ModelLGB(Model):\n\n def train(self, tr_x, tr_y, va_x=None, va_y=None):\n # データのセット\n isvalid = va_x is not None\n lgb_train = lgb.Dataset(tr_x, tr_y)\n if isvalid:\n lgb_valid = lgb.Dataset(va_x, va_y)\n\n # ハイパーパラメータの設定\n params = dict(self.params)\n # num_round = params.pop('num_round')\n\n # 学習\n if isvalid:\n self.model = lgb.train(params, lgb_train, valid_sets=lgb_valid)\n else:\n self.model = lgb.train(params, lgb_train)\n\n # if isvalid:\n # early_stopping_rounds = params.pop('early_stopping_rounds')\n # watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n # self.model = xgb.train(params, dtrain, num_round, evals=watchlist,\n # early_stopping_rounds=early_stopping_rounds)\n # else:\n # watchlist = [(dtrain, 'train')]\n # self.model = xgb.train(params, dtrain, num_round, evals=watchlist)\n\n\n def predict(self, te_x):\n return self.model.predict(te_x, num_iteration=self.model.best_iteration)\n\n\n def save_model(self):\n model_path = os.path.join('../models/lgb', f'{self.run_fold_name}.model')\n os.makedirs(os.path.dirname(model_path), exist_ok=True)\n Util.dump(self.model, model_path)\n\n\n def load_model(self):\n model_path = os.path.join('../models/lgb', f'{self.run_fold_name}.model')\n self.model = Util.load(model_path)","sub_path":"scripts/sample/models/model_lgb.py","file_name":"model_lgb.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"148832015","text":"import os\n\ndef file_reader():\n file_open = open(\"file.txt\",\"r\")\n files = file_open.read().splitlines()\n file_open.close()\n del files[-1]\n return files\n\ndef file_writer():\n os.system('ls /tmp> file.txt')\nfile_writer()\nfor i in file_reader():\n print(i)\n","sub_path":"list_file.py","file_name":"list_file.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"435632354","text":"#########################################################################\n# #\n# HexGrid Constructor (Python Version) #\n# This constructor script calculates and generates the hexgrid #\n# temperature layer on top of the map. #\n# Due to performance and function duration, the original function #\n# is divided into smaller parts. Easier to monitor and add logs. #\n# #\n#########################################################################\nimport turfpy.measurement as turf\nfrom turfpy.transformation import union\nfrom geojson import Feature, Polygon, FeatureCollection, Point\nimport datetime as dte\nimport json, math\n\n\n# get default hexgrid\ndef get_hexgrid(filename):\n\n # filepath = 'gsod/posts/blanks/' + filename\n # filepath = 'BatchCompute/data' + filename\n with open(filename, 'r') as readfile:\n hexgrid = json.load(readfile)\n\n return hexgrid\n\n\n# get the default hexgrid, compute the polygons and centroids\ndef hexgrid_constructor(bbox, cellSide, stations, levels, mid_lat):\n\n # set variables, declare hexGrid\n bbox = ','.join([str(b) for b in bbox]).replace(',', '_')\n filename = 'blank_HexGrid' + bbox + 'r' + str(cellSide) + '.json'\n hexGrid = get_hexgrid(filename)\n centroids = []\n hexGridDict = {}\n tempDict = {}\n\n # loop thru hexgrid to get centroids\n s1 = dte.datetime.now()\n for hex in hexGrid['features']:\n centroid_id = 'P' + ','.join([str(round(c, 6)) for c in hex['centroid']['geometry']['coordinates']])\n centroid_id = centroid_id.replace('P-', 'N').replace(',', '_').replace('-', 'n')\n # print(centroid_id)\n hexGridDict[centroid_id] = {\n 'station': {'0': 0},\n 'rings': [],\n }\n tempDict[centroid_id] = {\"temperature\": -1}\n centroids.append(Feature(geometry=Point(hex['centroid']['geometry']['coordinates'])))\n centroid_set = FeatureCollection(centroids)\n e1 = dte.datetime.now()\n\n # loop through stations and assign it to the hexGrid\n station_centroids = []\n print(\"Set Hexagon Tiles:\", str((e1 - s1).total_seconds()), \"seconds\")\n s2 = dte.datetime.now()\n print(\"Number of Stations:\", len(stations))\n for idx, station in enumerate(stations):\n station_coord = Feature(geometry=Point(stations[idx]['geometry']['coordinates']))\n\n # print something every 100 to show it is still running\n if idx % 100 == 0:\n print(\"Centroids: \", len(station_centroids), \", idx: \", idx)\n\n lat = stations[idx]['geometry']['coordinates'][1]\n cellSide_convert = convert_distance(cellSide, lat, mid_lat) * 1.05 # 5% error\n closest_hex = find_closest_polygon(station_coord, centroid_set, cellSide_convert)\n if closest_hex['properties']['distanceToPoint'] > (cellSide * 2):\n pass\n else:\n # assign that hex the station\n coord = 'P' + ','.join([str(round(c, 6)) for c in closest_hex['geometry']['coordinates']])\n coord = coord.replace('P-', 'N').replace(',', '_').replace('-', 'n')\n # print(hexGridDict[coord])\n hexGridDict[coord]['station'] = station\n tempDict[coord]['temperature'] = stations[idx]['properties']['TMAX'] # TMAX USED HERE #############!!!!!!\n station_centroids.append(Feature(geometry=Point(closest_hex['geometry']['coordinates'])))\n\n e2 = dte.datetime.now()\n print(\"Assign Stations:\", str((e2 - s2).total_seconds()), \"seconds\")\n\n # add rings\n s1 = dte.datetime.now()\n for idx, hex in enumerate(hexGridDict):\n stations_set = FeatureCollection(station_centroids.copy())\n\n centroid_coord = hex.replace('N', '-').replace('P', '').replace('_', ',').replace('n', '-')\n centroid_coord = [float(c) for c in centroid_coord.split(',')]\n\n # print something every 1000 to show it is still running\n if idx % 1000 == 0:\n print(\"Ring Searching: \", idx)\n\n # get all the '0's and ignore the stations\n if '0' in hexGridDict[hex]['station']:\n # get closest stations in recursive function\n rings = get_closest_stations(centroid_coord, stations_set, tempDict, cellSide, mid_lat, 0, 1, levels)\n # print(len(stations_set['features']), idx)\n # if idx == 0:\n # print(\"First Coord:\", centroid_coord)\n if not rings:\n # no results\n # if idx == 12417:\n # print('No Results', rings, centroid_coord)\n pass\n else:\n hexGridDict[hex]['rings'] = rings\n # print('Rings', idx, centroid_coord, hexGridDict[hex]['rings']) # , stations_set)\n e1 = dte.datetime.now()\n print(\"Adding Rings:\", str((e1 - s1).total_seconds()), \"seconds\")\n\n # re - calculate temps and deploy the rings, then stations\n # heat_check = 0\n for idx, hex in enumerate(hexGridDict):\n if ('0' in hexGridDict[hex]['station']) and (len(hexGridDict[hex]['rings']) > 0):\n\n # get \"highest\" ring_level, e.g. level 1 is highest\n ring_level = hexGridDict[hex]['rings'][0]['ring_level']\n acc_temp = 0\n total_acc = 0\n\n # average out the overlaps\n for ring in hexGridDict[hex]['rings']:\n if ring['ring_level'] == ring_level:\n acc_temp += ring['temperature']\n total_acc += 1\n avg_temp = acc_temp / total_acc\n\n hexGrid['features'][idx]['properties'] = {\n 'temperature': avg_temp\n }\n elif not ('0' in hexGridDict[hex]['station']):\n # this is a weather station\n hexGrid['features'][idx]['properties'] = hexGridDict[hex]['station']['properties'].copy()\n hexGrid['features'][idx]['properties']['temperature'] = \\\n (hexGrid['features'][idx]['properties']['TMAX'] + 40) / 80\n else:\n # this is not weather station and outside all rings\n '''\n if heat_check == 0:\n heat_check += 1\n print(\"Heat Check\")\n '''\n hexGrid['features'][idx]['properties'] = {\n 'temperature': -1\n }\n e2 = dte.datetime.now()\n print(\"Deploying Temperatures:\", str((e2 - e1).total_seconds()), \"seconds\")\n\n return hexGrid\n\n\n# ACTUAL nearest point -- edited the source code from turfpy library\ndef actual_nearest_point(target_point: Feature, points: FeatureCollection) -> Feature:\n\n if not target_point:\n raise Exception(\"target_point is required\")\n\n if not points:\n raise Exception(\"points is required\")\n\n min_dist = float(\"inf\")\n best_feature_index = 0\n\n def _callback_feature_each(pt, feature_index):\n nonlocal min_dist, best_feature_index\n distance_to_point = turf.distance(target_point, pt)\n # print(distance_to_point)\n if float(distance_to_point) < min_dist:\n best_feature_index = feature_index\n min_dist = distance_to_point\n # print(min_dist)\n return True\n\n actual_feature_each(points, _callback_feature_each)\n\n nearest = points[\"features\"][best_feature_index]\n nearest[\"properties\"][\"featureIndex\"] = best_feature_index\n nearest[\"properties\"][\"distanceToPoint\"] = min_dist\n return nearest\n\n\n# ACTUAL feature each loop -- edited the source code from turfpy library\ndef actual_feature_each(geojson, callback):\n if geojson[\"type\"] == \"Feature\":\n callback(geojson, 0)\n elif geojson[\"type\"] == \"FeatureCollection\":\n for i in range(0, len(geojson[\"features\"])):\n # print(callback(geojson[\"features\"][i], i))\n if not callback(geojson[\"features\"][i], i):\n break\n\n\n# adated from actual_nearest_point -- find the first polygon that it is in\ndef find_closest_polygon(target_point: Feature, points: FeatureCollection, cellSide) -> Feature:\n\n if not target_point:\n raise Exception(\"target_point is required\")\n\n if not points:\n raise Exception(\"points is required\")\n\n min_dist = 10000000 # cellSide * 1.05\n best_feature_index = 0\n\n def _callback_feature_each(pt, feature_index):\n nonlocal min_dist, best_feature_index\n distance_to_point = turf.distance(target_point, pt)\n # print(distance_to_point)\n if float(distance_to_point) <= cellSide:\n best_feature_index = feature_index\n min_dist = distance_to_point\n # print(min_dist)\n return False # return False will break the loop once inside a polygon\n return True\n\n actual_feature_each(points, _callback_feature_each)\n\n nearest = points[\"features\"][best_feature_index]\n nearest[\"properties\"][\"featureIndex\"] = best_feature_index\n nearest[\"properties\"][\"distanceToPoint\"] = min_dist\n return nearest\n\n\n# adated from actual_nearest_point -- find the next closest ring polygon\ndef find_next_ring(target_point: Feature, points: FeatureCollection, min_dist, max_dist) -> Feature:\n\n if not target_point:\n raise Exception(\"target_point is required\")\n\n if not points:\n raise Exception(\"points is required\")\n\n found_dist = 10000000 # something super big, float(\"inf\")\n best_feature_index = 0\n\n def _callback_feature_each(pt, feature_index):\n nonlocal found_dist, best_feature_index\n distance_to_point = turf.distance(target_point, pt)\n # print(distance_to_point)\n if (float(distance_to_point) >= min_dist) and (float(distance_to_point) < max_dist):\n best_feature_index = feature_index\n found_dist = distance_to_point\n # print(min_dist)\n return False # return False will break the loop once inside a polygon\n return True\n\n actual_feature_each(points, _callback_feature_each)\n\n nearest = points[\"features\"][best_feature_index]\n nearest[\"properties\"][\"featureIndex\"] = best_feature_index\n nearest[\"properties\"][\"distanceToPoint\"] = found_dist\n return nearest\n\n\n# Get Closest Weather Stations -- recursive\ndef get_closest_stations(coord, the_stations, tempDict, cellSide, mid_lat, loops, level, max_level):\n\n '''\n if level == 1:\n adj_min_dist = convert_distance(cellSide, coord[1], mid_lat)\n adj_max_dist = convert_distance(cellSide * math.sqrt(3) * level, coord[1], mid_lat)\n else:\n '''\n # above or below mid_lat\n hyp_dist = cellSide * math.sqrt(3) * level\n if coord[1] >= mid_lat:\n # above mid_lat -- convert shortest distance\n adj_max_dist = max(hyp_dist, convert_distance(hyp_dist, coord[1], mid_lat)) # hyp_dist\n adj_min_dist = min(cellSide * level, convert_distance(cellSide * level, coord[1], mid_lat))\n else:\n adj_max_dist = max(hyp_dist, convert_distance(hyp_dist, coord[1], mid_lat))\n adj_min_dist = convert_distance(cellSide * level, coord[1], mid_lat)\n\n # account for 2% error\n get_min_dist = float(round_decimals_down(min(adj_max_dist, adj_min_dist), 6)) * 0.98\n get_max_dist = float(round(max(adj_max_dist, adj_min_dist), 6)) * 1.02\n\n # find next ring\n closest_station = find_next_ring(coord, the_stations, get_min_dist, get_max_dist)\n # closest_station = actual_nearest_point(coord, the_stations)\n\n # get feature index and distance\n feature_index = closest_station['properties']['featureIndex']\n distance = closest_station['properties']['distanceToPoint']\n new_stations = the_stations.copy()\n\n # DEBUG:\n '''\n if (coord[0] == -125.773062) and (coord[1] == 24.149234):\n print(closest_station, distance, level, get_min_dist, get_max_dist, len(new_stations['features']))\n if (coord[0] == -123.710315) and (coord[1] == 44.009509):\n f1 = Feature(geometry=Point((-123.710315, 44.009509)))\n f2 = Feature(geometry=Point((-123.710315, 43.775858)))\n get_distance = turf.distance(f1, f2)\n print(feature_index, get_distance, level, get_min_dist, get_max_dist, len(new_stations['features']))\n \n if (coord[0] == -96.894608) and (coord[1] == 33.728896):\n f1 = Feature(geometry=Point((-96.636765, 33.845721)))\n f2 = Feature(geometry=Point((-96.894608, 33.728896)))\n get_distance = turf.distance(f1, f2)\n print(feature_index, get_distance, level, get_min_dist, get_max_dist, len(new_stations['features']))\n '''\n\n # get station coord to assign temperature\n station_coord = 'P' + ','.join([str(round(c, 6)) for c in closest_station['geometry']['coordinates']])\n station_coord = station_coord.replace('P-', 'N').replace(',', '_').replace('-', 'n')\n\n if (loops > 7) or (level > max_level):\n return False\n elif (float(round(distance, 6)) < get_max_dist) and \\\n (float(round(distance, 6)) >= get_min_dist) and (len(new_stations['features']) > 1):\n # remove that from list\n new_stations['features'].pop(feature_index)\n\n # recursive call to get next closest station\n next_closest = get_closest_stations(coord, new_stations, tempDict, cellSide, mid_lat, loops+1, level, max_level)\n coord_dict = [{\n 'ring_level': level,\n 'temperature': (tempDict[station_coord]['temperature'] + 40 + (-1 * level)) / 80\n }]\n # print(coord_dict, coord, distance, get_min_dist, get_max_dist)\n\n if not next_closest:\n pass\n else:\n coord_dict.extend(next_closest)\n return coord_dict\n elif loops <= 7:\n # else if loops less than 7 but not satisfy above -- increase the level\n next_closest = get_closest_stations(coord, new_stations, tempDict, cellSide,\n mid_lat, loops + 1, level + 1, max_level)\n # don't process coord_dict; just next_closest if not false\n if not next_closest:\n return False\n else:\n return next_closest\n else:\n return False\n\n\n# convert the distance based on the latitude and reference latitude\ndef convert_distance(distance, lat, middle_lat):\n\n # find pixel distance of the distance at the middle latitude\n \n km_per_pixel = 40075 * math.cos(middle_lat * math.pi / 180) / math.pow(2, 12)\n pixel_distance = distance / km_per_pixel\n\n # now use pixel distance to convert it to the distance at the needed latitude\n km_per_pixel = 40075 * math.cos(lat * math.pi / 180) / math.pow(2, 12)\n return pixel_distance * km_per_pixel\n\n\n# round decimals down\ndef round_decimals_down(number:float, decimals:int=2):\n \"\"\"\n Returns a value rounded down to a specific number of decimal places.\n \"\"\"\n if not isinstance(decimals, int):\n raise TypeError(\"decimal places must be an integer\")\n elif decimals < 0:\n raise ValueError(\"decimal places has to be 0 or more\")\n elif decimals == 0:\n return math.ceil(number)\n\n factor = 10 ** decimals\n return math.floor(number * factor) / factor\n","sub_path":"BatchCompute/data/hexgrid_constructor.py","file_name":"hexgrid_constructor.py","file_ext":"py","file_size_in_byte":15132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"243569673","text":"\"\"\"\n计算各城市商家运营数据情况\n\"\"\"\nimport pandas as pd\nfrom datetime import datetime\ntimes = datetime.now().strftime('%Y-%m-%d')\n\nfilename = '沙县各商家运营数据.xlsx'\nsheet_name = pd.ExcelFile(filename).sheet_names # print(sheet_name) # ['沙县', '安陆', '丹江口'] 返回值是列表\nb_name = input('请输入您需要查询的商家名称:')\nfor sheet in sheet_name:\n if sheet == '12月':\n gmv = 2257444.8\n order = 55475\n agent_money = 219801.25\n different_rate = 0.0099\n df01 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df01.ix[df01['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df01['非顾客原因异常订单率'] = df01['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df01 = df01.drop(['是否有双证'], axis=1) # 删除列\n df01 = df01.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df01 = df01.drop(['一级品类'], axis=1) # 删除列\n df01 = df01.drop(['二级品类'], axis=1) # 删除列\n df01 = df01.drop(['代理商名称'], axis=1) # 删除列\n df01 = df01.drop(['商家ID'], axis=1) # 删除列\n df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df01 = df01.drop(['实际��付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df01['原价交易额贡献占比'] = 0 # 可以增加新的列\n df01['原价交易额贡献占比'] = df01.apply(lambda x: df01['原价交易额'] / gmv)\n df01['原价交易额贡献占比'] = df01['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df01['订单数贡献占比'] = 0\n df01['订单数贡献占比'] = df01.apply(lambda x: df01['订单数'] / order)\n df01['订单数贡献占比'] = df01['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df01['代补金额贡献占比'] = 0\n df01['代补金额贡献占比'] = df01.apply(lambda x: df01['代理商补贴金额'] / agent_money)\n df01['代补金额贡献占比'] = df01['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df01['非异贡献占比'] = 0\n df01['非异贡献占比'] = df01.apply(lambda x: df01['非顾客原因异常订单率'] / different_rate)\n df01['非异贡献占比'] = df01['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df01[\"订单数\"] = df01[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df01.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n # df01 = df01.head(30) # 取前30行数据\n df01.set_index(['外卖组织结构'], inplace=True)\n data01 = df01[df01['商家名称'] == b_name] # 魏记骨汤麻辣烫\n print(data01)\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n if sheet == '11月':\n gmv = 2160776.78\n order = 54250\n agent_money = 182178.55\n different_rate = 0.0107\n df02 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df02.ix[df02['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df02['非顾客原因异常订单率'] = df02['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df02 = df02.drop(['是否有双证'], axis=1) # 删除列\n df02 = df02.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df02 = df02.drop(['一级品类'], axis=1) # 删除列\n df02 = df02.drop(['二级品类'], axis=1) # 删除列\n df02 = df02.drop(['代理商名称'], axis=1) # 删除列\n df02 = df02.drop(['商家ID'], axis=1) # 删除列\n df02 = df02.drop(['配送方式'], axis=1) # 删除列\n df02 = df02.drop(['实际支付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df02['原价交易额贡献占比'] = 0 # 可以增加新的列\n df02['原价交易额贡献占比'] = df02.apply(lambda x: df02['原价交易额']/gmv)\n df02['原价交易额贡献占比'] = df02['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df02['订单数贡献占比'] = 0\n df02['订单数贡献占比'] = df02.apply(lambda x: df02['订单数'] / order)\n df02['订单数贡献占比'] = df02['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df02['代补金额贡献占比'] = 0\n df02['代补金额贡献占比'] = df02.apply(lambda x: df02['代理商补贴金额'] / agent_money)\n df02['代补金额贡献占比'] = df02['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df02['非异贡献占比'] = 0\n df02['非异贡献占比'] = df02.apply(lambda x: df02['非顾客原因异常订单率'] / different_rate)\n df02['非异贡献占比'] = df02['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df02[\"订单数\"] = df02[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df02.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n df02 = df02.head(30)\n df02.set_index(['外卖组织结构'], inplace=True)\n data02 = df02[df02['商家名称'] == b_name] # 魏记骨汤麻辣烫\n print(data02)\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n\n if sheet == '10月':\n gmv = 2300995.2\n order = 56908\n agent_money = 191680.21\n different_rate = 0.0094\n df03 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df03.ix[df03['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df03['非顾客原因异常订单率'] = df03['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df03 = df03.drop(['是否有双证'], axis=1) # 删除列\n df03 = df03.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df03 = df03.drop(['一级品类'], axis=1) # 删除列\n df03 = df03.drop(['二级品类'], axis=1) # 删除列\n df03 = df03.drop(['代理商名称'], axis=1) # 删除列\n df03 = df03.drop(['商家ID'], axis=1) # 删除列\n df03 = df03.drop(['配送方式'], axis=1) # 删除列\n df03 = df03.drop(['实际支付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df03['原价交易额贡献占比'] = 0 # 可以增加新的列\n df03['原价交易额贡献占比'] = df03.apply(lambda x: df03['原价交易额'] / gmv)\n df03['原价交易额贡献占比'] = df03['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df03['订单数贡献占比'] = 0\n df03['订单数贡献占比'] = df03.apply(lambda x: df03['订单数'] / order)\n df03['订单数贡献占比'] = df03['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df03['代补金额贡献占比'] = 0\n df03['代补金额贡献占比'] = df03.apply(lambda x: df03['代理商补贴金额'] / agent_money)\n df03['代补金额贡献占比'] = df03['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df03['非异贡献占比'] = 0\n df03['非异贡献占比'] = df03.apply(lambda x: df03['非顾客原因异常订单率'] / different_rate)\n df03['非异贡献占比'] = df03['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df03[\"订单数\"] = df03[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df03.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n df03 = df03.head(30)\n df03.set_index(['外卖组织结构'], inplace=True)\n data03 = df03[df03['商家名称'] == b_name] # 魏记骨汤麻辣烫\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n if sheet == '9月':\n gmv = 2055899.15\n order = 50911\n agent_money = 174015.28\n different_rate = 0.0132\n df04 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df04.ix[df04['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df04['非顾客原因异常订单率'] = df04['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df04 = df04.drop(['是否有双证'], axis=1) # 删除列\n df04 = df04.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df04 = df04.drop(['一级品类'], axis=1) # 删除列\n df04 = df04.drop(['二级品类'], axis=1) # 删除列\n df04 = df04.drop(['代理商名称'], axis=1) # 删除列\n df04 = df04.drop(['商家ID'], axis=1) # 删除列\n df04 = df04.drop(['配送方式'], axis=1) # 删除列\n df04 = df04.drop(['实际支付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df04['原价交易额贡献占比'] = 0 # 可以增加新的列\n df04['原价交易额贡献占比'] = df04.apply(lambda x: df04['原价交易额'] / gmv)\n df04['原价交易额贡献占比'] = df04['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df04['订单数贡献占比'] = 0\n df04['订单数贡献占比'] = df04.apply(lambda x: df04['订单数'] / order)\n df04['订单数贡献占比'] = df04['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df04['代补金额贡献占比'] = 0\n df04['代补金额贡献占比'] = df04.apply(lambda x: df04['代理商补贴金额'] / agent_money)\n df04['代补金额贡献占比'] = df04['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df04['非异贡献占比'] = 0\n df04['非异贡献占比'] = df04.apply(lambda x: df04['非顾客原因异常订单率'] / different_rate)\n df04['非异贡献占比'] = df04['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df04[\"订单数\"] = df04[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df04.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n df04 = df04.head(30)\n df04.set_index(['外卖组织结构'], inplace=True)\n data04 = df04[df04['商家名称'] == b_name] # 魏记骨汤麻辣烫\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n if sheet == '8月':\n gmv = 2692068.51\n order = 65329\n agent_money = 219326.55\n different_rate = 0.0156\n df05 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df05.ix[df05['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df05['非顾客原因异常订单率'] = df05['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df05 = df05.drop(['是否有双证'], axis=1) # 删除列\n df05 = df05.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df05 = df05.drop(['一级品类'], axis=1) # 删除列\n df05 = df05.drop(['二级品类'], axis=1) # 删除列\n df05 = df05.drop(['代理商名称'], axis=1) # 删除列\n df05 = df05.drop(['商家ID'], axis=1) # 删除列\n df05 = df05.drop(['配送方式'], axis=1) # 删除列\n df05 = df05.drop(['实际支付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df05['原价交易额贡献占比'] = 0 # 可以增加新的列\n df05['原价交易额贡献占比'] = df05.apply(lambda x: df05['原价交易额'] / gmv)\n df05['原价交易额贡献占比'] = df05['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df05['订单数贡献占比'] = 0\n df05['订单数贡献占比'] = df05.apply(lambda x: df05['订单数'] / order)\n df05['订单数贡献占比'] = df05['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df05['代补金额贡献占比'] = 0\n df05['代补金额贡献占比'] = df05.apply(lambda x: df05['代理商补贴金额'] / agent_money)\n df05['代补金额贡献占比'] = df05['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df05['非异贡献占比'] = 0\n df05['非异贡献占比'] = df05.apply(lambda x: df05['非顾客原因异常订单率'] / different_rate)\n df05['非异贡献占比'] = df05['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df05[\"订单数\"] = df05[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df05.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n df05 = df05.head(30)\n df05.set_index(['外卖组织结构'], inplace=True)\n data05 = df05[df05['商家名称'] == b_name] # 魏记骨汤麻辣烫\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n if sheet == '7月':\n gmv = 2627850.44\n order = 63877\n agent_money = 221700.17\n different_rate = 0.0184\n df06 = pd.read_excel(filename, sheet)\n # df[['col2', 'col3']] = df[['col2', 'col3']].apply(pd.to_numeric)\n # 将百分数转化为小数\n # df01 = df01.where(df01.notnull(), 0) # 把所有为空的列的值改为None\n df06.ix[df06['非顾客原因异常订单率'] == '-', '非顾客原因异常订单率'] = 0 # 刷选\n df06['非顾客原因异常订单率'] = df06['非顾客原因异常订单率'].str.strip('%').astype(float) / 100\n df06 = df06.drop(['是否有双证'], axis=1) # 删除列\n df06 = df06.drop(['是否签署SD合作协议'], axis=1) # 删除列\n df06 = df06.drop(['一级品类'], axis=1) # 删除列\n df06 = df06.drop(['二级品类'], axis=1) # 删除列\n df06 = df06.drop(['代理商名称'], axis=1) # 删除列\n df06 = df06.drop(['商家ID'], axis=1) # 删除列\n df06 = df06.drop(['配送方式'], axis=1) # 删除列\n df06 = df06.drop(['实际支付交易额'], axis=1) # 删除列\n # df01 = df01.drop(['配送方式'], axis=1) # 删除列\n df06['原价交易额贡献占比'] = 0 # 可以增加新的列\n df06['原价交易额贡献占比'] = df06.apply(lambda x: df06['原价交易额'] / gmv)\n df06['原价交易额贡献占比'] = df06['原价交易额贡献占比'].apply(lambda x: format(x, '.2%'))\n df06['订单数贡献占比'] = 0\n df06['订单数贡献占比'] = df06.apply(lambda x: df06['订单数'] / order)\n df06['订单数贡献占比'] = df06['订单数贡献占比'].apply(lambda x: format(x, '.2%'))\n df06['代补金额贡献占比'] = 0\n df06['代补金额贡献占比'] = df06.apply(lambda x: df06['代理商补贴金额'] / agent_money)\n df06['代补金额贡献占比'] = df06['代补金额贡献占比'].apply(lambda x: format(x, '.2%'))\n df06['非异贡献占比'] = 0\n df06['非异贡献占比'] = df06.apply(lambda x: df06['非顾客原因异常订单率'] / different_rate)\n df06['非异贡献占比'] = df06['非异贡献占比'].apply(lambda x: format(x, '.2%'))\n # 排序 一定要记得先将数据转化成 int类型\n # df01.sort_values([\"原价交易额贡献占比\", \"订单数贡献占比\", '代补金额贡献占比', '非异贡献占比'], ascending=False)\n df06[\"订单数\"] = df06[\"订单数\"].astype(\"int\") # 强制转化类型\n # inplace表示再排序的时候是否生成一个新的dataframe 结构\n df06.sort_values([\"原价交易额贡献占比\"], inplace=True, ascending=False)\n df06 = df06.head(30)\n df06.set_index(['外卖组织结构'], inplace=True)\n data06 = df06[df06['商家名称'] == b_name] # 魏记骨汤麻辣烫\n # url = 'C:/Users/王颖/Desktop/'\n # df01.to_excel(url + '沙县11月各商家数据明细.xlsx')\n\n# 将多个dataframe合并成一个dataframe, 这两条数据的列名称是完全一样\nurl = 'C:/Users/王颖/Desktop/'\ndf = pd.concat([data01, data02, data03, data04, data05, data06])\ndf.to_excel(url + '沙县11月各商家数据明细.xlsx')\n# 将多个dataframe数据写入同一个Excel的不同sheet中\n# url = 'C:/Users/王颖/Desktop/'\n# with pd.ExcelWriter(url+\"沙县各商家运营情况.xlsx\") as writer:\n# df01.to_excel(writer, sheet_name='沙县12月')\n# df02.to_excel(writer, sheet_name='沙县11月')\n# df03.to_excel(writer, sheet_name='沙县10月')\n# df04.to_excel(writer, sheet_name='沙县9月')\n# df05.to_excel(writer, sheet_name='沙县8月')\n# df06.to_excel(writer, sheet_name='沙县7月')\n\n\n\n","sub_path":"execl_计算历史数据单个商家的营业详情.py","file_name":"execl_计算历史数据单个商家的营业详情.py","file_ext":"py","file_size_in_byte":19265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"312610424","text":"import math\nimport numpy as np\nimport random as rnd\n\nstr_bold = '\\033[1m'\nstr_reset = '\\033[0m'\nstr_blue_bg = '\\033[44m'\nstr_white_font = '\\033[37m'\n\n\ndef L2(a, b):\n s = a.size\n if s != b.size:\n raise ValueError(\"Arrays must be of equal size.\")\n q = 0\n for i in range(s):\n q += (a[i] - b[i])**2\n return math.sqrt(q)\n\n\ndef L1(a, b):\n s = a.size\n if s != b.size:\n raise ValueError(\"Arrays must be of equal size.\")\n q = 0\n for i in range(s):\n q += abs(a[i] - b[i])\n return q\n\n\ndef findMinMax(data):\n rows, columns = data.shape\n minima = np.full(columns, -1, dtype=float)\n maxima = np.full(columns, -1, dtype=float)\n for i in range(rows):\n for j in range(columns):\n if minima[j] > data[i][j] or minima[j] < 0:\n minima[j] = data[i][j]\n if maxima[j] < data[i][j] or maxima[j] < 0:\n maxima[j] = data[i][j]\n return minima, maxima\n\n\ndef kmeans(data, k=1, update_threshold=0, maximum_iterations=-1, distance_func=L2, verbose=False):\n column_count = len(data.columns)\n record_count = len(data.values)\n k_means = []\n # cluster indices for every record\n c_indices = np.zeros(record_count, dtype=np.uint8)\n # distance to cluster for every record\n c_distances = np.full(record_count, -1, dtype=float)\n update_count = 1\n\n # initialize clusters\n for _ in range(k):\n new_k = np.zeros(column_count)\n for j in range(column_count):\n new_k[j] = rnd.random()\n k_means.append(new_k)\n cluster_count = len(k_means)\n # begin grouping process\n clusters_were_updated = True\n updated_list = [False for i in range(len(k_means))]\n while clusters_were_updated and (update_count < maximum_iterations if maximum_iterations > 0 else True):\n # print cluster values\n if verbose == True:\n print(\"K = %i, update %i\" % (k, update_count))\n for c in range(len(k_means)):\n if updated_list[c]:\n print(str_bold + str_blue_bg + str_white_font, end='')\n print(\"C%i : \" % (c+1), end='')\n for i in range(k_means[c].size):\n print(\"%7.2f\" % (k_means[c][i]), end='')\n print(str_reset, end='')\n print()\n # measure distances, update group if necessary\n update_count += 1\n clusters_were_updated = False\n for record_index in range(record_count):\n for cluster_index in range(cluster_count):\n distance = distance_func(\n data.values[record_index], k_means[cluster_index])\n if distance < c_distances[record_index] or c_distances[record_index] < 0:\n c_distances[record_index] = distance\n c_indices[record_index] = cluster_index\n # update clusters\n updated_list = [False for i in range(len(k_means))]\n for i in range(cluster_count):\n updated_cluster = np.zeros(column_count, dtype=float)\n item_count = 0\n for j in range(record_count):\n if c_indices[j] == i:\n item_count += 1\n for m in range(column_count):\n updated_cluster[m] += data.iloc[j, m]\n if item_count > 0:\n for m in range(column_count):\n updated_cluster[m] /= item_count\n if distance_func(updated_cluster, k_means[i]) > update_threshold:\n k_means[i] = updated_cluster\n clusters_were_updated = True\n updated_list[i] = True\n return c_indices, k_means\n","sub_path":"Patrones/Final/clustering.py","file_name":"clustering.py","file_ext":"py","file_size_in_byte":3679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"445993744","text":"import torch\nimport pytorch_lightning as pl\nfrom torch import nn, mean, optim\nfrom copy import deepcopy\n\nfrom typing import Any, Dict\nfrom torch.optim.optimizer import Optimizer\nimport torchmetrics\nimport models\nfrom models.loss_functions import EVM\nfrom models.metrics import BERMetric, QFactor\nfrom data.transform_1d_2d import transform_to_1d\nfrom math import sqrt\n\n# import plotly.express as px\n# from plotly.offline import plot\nimport matplotlib.pyplot as plt\n\nclass ConvNet_regressor(pl.LightningModule):\n def __init__(\n self, \n seq_len: int,\n pulse_width: float,\n z_end: float,\n dim_t: int,\n decision_level: float,\n \n in_features: int,\n bias = False,\n\n optimizer:str = 'Adam',\n optimizer_kwargs: Dict[str, Any] = {'lr':1e-4},\n scheduler: str = 'StepLR',\n scheduler_kwargs: Dict[str, Any] = {'step_size':10},\n criterion: str = 'EVM',\n ):\n '''\n in_features (int) - number of input features in model\n bias (bool) - whether to use bias in linear layers or not.\n \n optimizer (str) - name of optimizer (ex. \"Adam\", \"SGD\")\n optimizer_kwargs (dict) - parameters of optimizer (ex. {'lr':1e-4})\n scheduler (str) - name of scheduler that will be used\n scheduler_kwargs (dict) - parameters of scheduler\n criterion (str) - Loss function that will be used for training. \"MSE\" or \"EVM\"\n '''\n \n\n super().__init__()\n\n self.optimizer = optimizer\n self.optimizer_kwargs = optimizer_kwargs\n self.scheduler = scheduler\n self.scheduler_kwargs = scheduler_kwargs\n\n self.net = ConvNet(in_features, bias)\n \n self.loss_type = criterion\n \n if criterion == 'MSE':\n self.criterion = nn.MSELoss()\n elif criterion == 'EVM':\n self.criterion = EVM()\n \n self.seq_len = seq_len\n self.pulse_width = pulse_width\n self.z_end = z_end\n self.dim_t = dim_t\n \n self.t_end = ((seq_len - 1)//2 + 1) * pulse_width \n tMax = self.t_end + 4*sqrt(2*(1 + z_end**2))\n tMin = -tMax\n dt = (tMax - tMin) / dim_t\n self.t = torch.linspace(tMin, tMax-dt, dim_t)\n self.t_window = [torch.abs(self.t + self.t_end).argmin(),\n torch.abs(self.t - self.t_end).argmin()]\n \n self.ber = BERMetric(decision_level=decision_level,\n pulse_number=seq_len,\n pulse_width=pulse_width,\n t=self.t + self.t_end + 0.5*pulse_width,\n t_window=self.t_window)\n\n\n def forward(self, x):\n if len(x.shape) == 4 and x.shape[0] == 1:\n x = x.squeeze(dim=0)\n x = x.permute(2, 0, 1)\n real = x.real\n imag = x.imag\n real, imag = self.net(real, imag)\n return (real + 1j*imag).unsqueeze(1).permute(1, 2, 0)\n\n\n def training_step(self, batch, batch_idx):\n data, target = batch\n preds = self.forward(data)\n \n preds = transform_to_1d(preds)\n target = transform_to_1d(target)\n \n if self.loss_type == 'MSE':\n loss_real = self.criterion(preds.real, target.real)\n loss_imag = self.criterion(preds.imag, target.imag)\n loss = loss_real + loss_imag\n elif self.loss_type == 'EVM':\n loss = self.criterion(preds, target)\n \n self.log(\"loss_train\", loss, prog_bar = False, logger = True)\n return {\"loss\": loss, \"preds\": preds, \"target\": target}\n\n\n def validation_step(self, batch, batch_idx):\n data, target = batch\n preds = self.forward(data)\n\n preds = transform_to_1d(preds)\n target = transform_to_1d(target)\n \n if self.loss_type == 'MSE':\n loss_real = self.criterion(preds.real, target.real)\n loss_imag = self.criterion(preds.imag, target.imag)\n loss = loss_real + loss_imag\n elif self.loss_type == 'EVM':\n loss = self.criterion(preds, target)\n \n self.log(\"loss_val\", loss, prog_bar = False, logger = True)\n return {'preds':preds , 'target': target}\n\n\n def configure_optimizers(self):\n OptimizerClass = getattr(optim, self.optimizer)\n SchedulerClass = getattr(optim.lr_scheduler, self.scheduler)\n opt = OptimizerClass(self.parameters(), **self.optimizer_kwargs)\n sch = SchedulerClass(opt, **self.scheduler_kwargs)\n\n return [opt], [sch]\n\n\n def training_epoch_end(self, outputs):\n preds = torch.cat([r['preds'] for r in outputs], dim=0)\n target = torch.cat([r['target'] for r in outputs], dim=0)\n\n self.ber.update(preds.squeeze(1), target.squeeze(1))\n ber_value = self.ber.compute()\n q_factor = QFactor(ber_value)\n self.log('Q_factor_train', q_factor, prog_bar = True, logger = True)\n \n # nt1 = torch.abs(self.t - 0.5 * self.pulse_width).argmin()\n # nt2 = torch.abs(self.t - 8.5 * self.pulse_width).argmin()\n # fig, ax = plt.subplots(1, 1)\n # ax.plot(self.t[nt1:nt2], target[0,0,nt1:nt2].cpu().real, label='Target')\n # ax.plot(self.t[nt1:nt2], preds[0,0,nt1:nt2].detach().cpu().real, label='Predicted')\n # ax.set_xlabel('Time')\n # ax.set_ylabel('Re(E)')\n # self.logger.experiment.add_figure('prediction_train', fig, global_step=self.current_epoch)\n\n def validation_epoch_end(self, outputs):\n preds = torch.cat([r['preds'] for r in outputs], dim=0)\n target = torch.cat([r['target'] for r in outputs], dim=0)\n\n self.ber.update(preds.squeeze(1), target.squeeze(1))\n ber_value = self.ber.compute()\n q_factor = QFactor(ber_value)\n self.log('Q_factor_val', q_factor, prog_bar = True, logger = True)\n \n nt1 = torch.abs(self.t - 0.5 * self.pulse_width).argmin()\n nt2 = torch.abs(self.t - 8.5 * self.pulse_width).argmin()\n fig, ax = plt.subplots(1, 1, dpi=150)\n ax.plot(self.t[nt1:nt2], target[0,0,nt1:nt2].cpu().real, label='Target')\n ax.plot(self.t[nt1:nt2], preds[0,0,nt1:nt2].detach().cpu().real, label='Predicted')\n ax.set_xlabel('Time')\n ax.set_ylabel('Re(E)')\n self.logger.experiment.add_figure('prediction_val', fig, global_step=self.current_epoch)\n # fig = px.line(x=self.t[nt1:nt2], y=target[0,0,nt1:nt2].real, title='Target', labels={'x':'Time', 'y':'real E'})\n # plot(fig)\n # fig = px.line(x=self.t[nt1:nt2], y=preds[0,0,nt1:nt2].detach().real, title='Predicted', labels={'x':'Time', 'y':'real E'})\n # plot(fig)\n\n def get_configuration(self):\n '''\n Returns dict of str with current configuration of the model.\n Can be used for Logger.\n '''\n configuration = {\n 'activation': self.net.activation_name, \n 'criterion': str(self.criterion.__repr__())[:-2],\n 'optimizer': self.optimizer,\n 'optimizer_param': str(self.optimizer_kwargs)[1:-1], \n 'scheduler': self.scheduler,\n 'scheduler_param': str(self.scheduler_kwargs)[1:-1]\n }\n return configuration\n\n\nclass ConvNet(nn.Module):\n '''\n Model with 1D-CNN architecture\n Inspired from https://discuss.pytorch.org/t/cnn-architecture-for-short-time-series-data/99814\n '''\n \n def __init__(self, in_features: int, bias=False, activation='ReLU'):\n '''\n @num_classes - number of features in input vector\n @bias - whether to use bias for convolutional layers or not\n @activation - the activation function used after each conv layer\n '''\n super(ConvNet, self).__init__()\n \n # Activation function\n self.activation_name = activation\n ActivationClass = getattr(nn, activation)\n \n self.real_model = nn.Sequential(\n nn.Conv1d(1, 3, kernel_size=3, stride=1, padding=1, bias=bias),\n nn.BatchNorm1d(3),\n ActivationClass(),\n nn.MaxPool1d(kernel_size=3, stride=1, padding=1),\n nn.Dropout(0.3),\n \n nn.Conv1d(3, 5, kernel_size=3, stride=1, padding=1, bias=bias),\n nn.BatchNorm1d(5),\n ActivationClass(),\n nn.Dropout(0.3),\n \n nn.Conv1d(5, 5, kernel_size=3, stride=1, padding=1, bias=bias),\n nn.BatchNorm1d(5),\n ActivationClass(),\n \n nn.Dropout(0.3)\n )\n \n self.imag_model = deepcopy(self.real_model)\n self.real_fc = nn.Linear(in_features, in_features)\n self.imag_fc = nn.Linear(in_features, in_features)\n \n \n def forward(self, real, imag):\n real = self.real_model(real)\n real = mean(real, dim = 1)\n real = self.real_fc(real)\n \n imag = self.imag_model(imag)\n imag = mean(imag, dim = 1)\n imag = self.imag_fc(imag)\n \n return real, imag","sub_path":"models/convnet.py","file_name":"convnet.py","file_ext":"py","file_size_in_byte":9146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"320764617","text":"\"\"\"\nKavita Amin \n12/1/2017\n\nAssignment: Build a mini game that helps a ninja make some money! \nWhen you start the game, your ninja should have 0 gold. \nThe ninja can go to different places (farm, cave, house, casino) and earn different amounts of gold. \nIn the case of a casino, your ninja can earn or LOSE up to 50 golds. \n\n\"\"\"\n\nfrom flask import Flask, render_template, session, redirect, request\nimport random\nfrom datetime import datetime\n\napp = Flask(__name__)\napp.secret_key = \"key\"\n\n@app.route('/')\ndef index(): \n\n if \"gold\" not in session: \n session[\"gold\"] = 0\n\n if \"activities\" not in session: \n session[\"activities\"] = [] \n\n return render_template(\"index.html\")\n\n\n\n@app.route('/process_money', methods=[\"POST\"])\ndef process_money(): \n \n time = str(datetime.now())\n\n if request.form[\"building\"] == \"farm\": \n farmEarnings = random.randint(10, 20)\n session[\"gold\"] += farmEarnings\n session[\"activities\"].append(\"Earned {} golds from the farm! ({}) \".format(farmEarnings,time))\n elif request.form[\"building\"] == \"cave\":\n caveEarnings = random.randint(5, 10)\n session[\"gold\"] += caveEarnings\n session[\"activities\"].append(\"Earned {} golds from the cave! ({}) \".format(caveEarnings,time))\n elif request.form[\"building\"] == \"house\": \n houseEarnings = random.randint(2, 5)\n session[\"gold\"] += houseEarnings\n session[\"activities\"].append(\"Earned {} golds from the house! ({}) \".format(houseEarnings,time))\n elif request.form[\"building\"] == \"casino\": \n winOrLose = random.randint(0,1)\n if winOrLose == 0: \n casinoLoss = random.randint(0,50)\n session[\"gold\"]-= casinoLoss\n session[\"activities\"].append(\"Entered a casino and lost {} golds. ({}) \".format(casinoLoss, time))\n elif winOrLose == 1: \n casinoWin = random.randint(0, 50)\n session[\"gold\"] += casinoWin\n session[\"activities\"].append(\"Entered a casino and won {} golds. ({}) \".format(casinoWin, time))\n\n return redirect('/')\n\napp.run(debug=True)\n","sub_path":"ninja_gold/ninja_gold.py","file_name":"ninja_gold.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57437507","text":"\r\nimport pyglet\r\nfrom pyglet.gl import *\r\nfrom pyglet.window import key\r\nfrom math import *\r\n\r\n\r\nwindow = pyglet.window.Window()\r\n\r\n'''\r\ntext_label = pyglet.text.Label('OTHELLO',\r\n font_name='Comic Sans',\r\n font_size=50,\r\n x=window.width // 2, y=window.height // 2 + 200,\r\n anchor_x='center', anchor_y='center')\r\n'''\r\n\r\n\r\n@window.event\r\ndef on_draw():\r\n\r\n window.clear()\r\n\r\n #text_label.draw()\r\n\r\n posx, posy = 0, 0\r\n sides = 32\r\n radius = 15\r\n\r\n\r\n for a in range(0,280,35):\r\n for b in range(0,280,35):\r\n spelar_position = [190+a, 370-b]\r\n glBegin(GL_POLYGON)\r\n for i in range(100):\r\n cosine = radius * cos(i * 2 * pi / sides) + posx\r\n sine = radius * sin(i * 2 * pi / sides) + posy\r\n glVertex2f(spelar_position[0]+cosine, spelar_position[1]+sine)\r\n glEnd()\r\n\r\n\r\n\r\n\r\npyglet.app.run()","sub_path":"othello.py","file_name":"othello.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"549853013","text":"import os\nfrom PIL import Image\nimport pickle\nimport numpy as np\nimport tensorflow as tf\nfrom keras.datasets import mnist\nfrom keras.utils import to_categorical\nfrom keras import optimizers, regularizers\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\nwith open('C:\\\\Users\\\\Nina\\\\PycharmProjects\\\\test\\\\venv\\\\X_Train2.data', 'rb') as filehandle:\n # read the data as binary data stream\n X_Train = pickle.load(filehandle)\n\nwith open('C:\\\\Users\\\\Nina\\\\PycharmProjects\\\\test\\\\venv\\\\Y_Train2.data', 'rb') as filehandle:\n # read the data as binary data stream\n Y_Train = pickle.load(filehandle)\n\nwith open('C:\\\\Users\\\\Nina\\\\PycharmProjects\\\\test\\\\venv\\\\Meta2.data', 'rb') as filehandle:\n # read the data as binary data stream\n Meta = pickle.load(filehandle)\n\nX_Train = X_Train.reshape(X_Train.shape[0], X_Train.shape[1], X_Train.shape[2], 1)\n\nX_Train = X_Train/255\nY_Train = to_categorical(Y_Train)\n\nmodel = Sequential()\n\nmodel.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), activation='relu',\n kernel_regularizer=regularizers.l2(0.001),\n input_shape=(X_Train.shape[1],\n X_Train.shape[2], 1),\n padding=\"same\"))\nmodel.add(MaxPooling2D(pool_size=(2, 2), strides=(1, 1)))\n\nmodel.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.001),\n padding=\"same\"))\nmodel.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.001),\n padding=\"same\"))\n# model.add(Conv2D(32, kernel_size=(3, 3), strides=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.001),\n# padding=\"same\"))\n\n#model.add(MaxPooling2D(pool_size=(2, 2), strides=(1, 1)))\n\n#model.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.001),\n# padding=\"same\"))\n#model.add(Conv2D(16, kernel_size=(3, 3), strides=(1, 1), activation='relu', kernel_regularizer=regularizers.l2(0.001),\n# padding=\"same\"))\n\n# model.add(Conv2D(16, kernel_size=3, activation='relu'))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(50, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(2, activation='softmax'))\n\nsgd = optimizers.SGD(lr=0.01, momentum=0.0001, nesterov=True)\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n# model.compile(optimizer=sgd, loss='mean_squared_error', metrics=['accuracy'])\n\n# model.fit(X_Train, Y_Train, validation_data=(X_Train, Y_Train), epochs=3)\nX_test = X_Train[0:291]\nX_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)\ny_test = Y_Train[0:291]\nmodel.fit(X_Train, Y_Train,validation_data=(X_Train, Y_Train), epochs=30, batch_size=10, shuffle=True)\n\nmodel.save('my_model.h5')\n","sub_path":"2020_02 Quantifying Thin-film Defects using Machine Vision/DeepThin model/train_cnn_2.py","file_name":"train_cnn_2.py","file_ext":"py","file_size_in_byte":3000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"43667372","text":"#!/usr/bin/env python\n# coding=utf-8\n# Author: bloke\n# Project: 地名分级显示\n\nimport requests\nimport re\nimport sys\n\n\ndef get_place(url, place_re): #获取数据\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) \\\n Maxthon/5.1.1.1000 Chrome/55.0.2883.75 Safari/537.36'\n }\n response = requests.get(url, headers=headers)\n try:\n data = place_re.findall(response.text)[0]\n return(data)\n except IndexError as e:\n return(None)\n\n\nurl = 'https://www.douban.com/note/290487992/'\nplace_re = re.compile(r'', re.S)\ndata = get_place(url, place_re)\nif not data: sys.exit()\nthe_data = re.findall(r'(直辖市)：(.*?)

(\\S{1,2}地区)
(.*?)

(\\S{1,2}地区)
(.*?)

(\\S{1,2}地区)
(.*?)

(\\S{1,2}地区)
(.*?)

(\\S{1,2}地区)
(.*?)

(\\S{1,2}地区)
(.*?)
港澳台', data, re.S)\nprovinces = the_data[0]\nprovinces_dict = {}\n\nfor num in range(2, len(provinces), 2): # 返回 地区、省、市 字典\n child_dict = {}\n child_data = provinces[num+1].split('
')\n if len(child_data) >= 1:\n for item in child_data:\n city, district = item.split('：')\n child_dict[city] = district\n else:\n provinces_dict[provinces[num]] = child_dict\n else:\n provinces_dict[provinces[num]] = provinces[num+1]\n\nprovince_list = list(provinces_dict.keys())\nwhile 1:\n for num, province in enumerate(province_list): # 列出地区\n print(str(num) + ' : ' + province)\n else:\n print('-- Usage: q to exit; num to join. --')\n select_province = input('Input Prevince: ').strip()\n if not select_province:\n continue\n if select_province == 'q':\n sys.exit(0)\n try:\n select_province = int(select_province)\n if select_province in range(len(province_list)):\n city_dict = provinces_dict[province_list[select_province]]\n if isinstance(city_dict, dict):\n city_list = list(city_dict.keys())\n while 1:\n for city_num, city_name in enumerate(city_list): # 列出省份\n print(str(city_num) + ' : ' + city_name)\n else:\n print('-- Usage: q to exit; b to go up; num to join. --')\n select_district = input('Input City: ').strip()\n if select_district == 'q':\n sys.exit(0)\n elif select_district == 'b':\n break\n try:\n select_district = int(select_district)\n if select_district in range(len(city_list)):\n for the_district in city_dict[city_list[select_district]].split(): # 列出市区\n print(the_district)\n else:\n print('-- Usage: q to exit; b to go up. --')\n while 1:\n the_select = input('Input: ').strip()\n if the_select == 'q':\n sys.exit(0)\n elif the_select == 'b':\n break\n else:\n continue\n except SystemExit as e: # 捕获 sys.exit 抛出的 SystemExit 异常\n sys.exit(0)\n except:\n continue\n except ValueError as e:\n continue\n","sub_path":"module1/place_name.py","file_name":"place_name.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"40507454","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport copy\n\nimport httpretty\nimport mock\n\nfrom openstack import exceptions\nfrom openstack import resource\nfrom openstack import session\nfrom openstack.tests import base\nfrom openstack.tests import fakes\nfrom openstack import transport\n\nfake_name = 'rey'\nfake_id = 99\nfake_attr1 = 'lana'\nfake_attr2 = 'del'\n\nfake_resource = 'fake'\nfake_resources = 'fakes'\nfake_arguments = {'name': 'rey'}\nfake_base_path = '/fakes/%(name)s/data'\nfake_path = '/fakes/rey/data'\n\nfake_data = {'id': fake_id,\n 'name': fake_name,\n 'attr1': fake_attr1,\n 'attr2': fake_attr2}\nfake_body = {fake_resource: fake_data}\n\n\nclass FakeResource(resource.Resource):\n\n resource_key = fake_resource\n resources_key = fake_resources\n base_path = fake_base_path\n\n allow_create = allow_retrieve = allow_update = True\n allow_delete = allow_list = allow_head = True\n\n name = resource.prop('name')\n first = resource.prop('attr1')\n second = resource.prop('attr2')\n\n\nclass ResourceTests(base.TestTransportBase):\n\n TEST_URL = fakes.FakeAuthenticator.ENDPOINT\n\n def setUp(self):\n super(ResourceTests, self).setUp()\n self.transport = transport.Transport(accept=transport.JSON)\n self.auth = fakes.FakeAuthenticator()\n self.session = session.Session(self.transport, self.auth)\n\n @httpretty.activate\n def test_empty_id(self):\n self.stub_url(httpretty.GET, path=[fake_path], json=fake_body)\n obj = FakeResource.new(**fake_arguments)\n obj.get(self.session)\n\n self.assertEqual(fake_id, obj.id)\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_attr1, obj['attr1'])\n self.assertEqual(fake_attr2, obj['attr2'])\n\n self.assertEqual(fake_name, obj.name)\n self.assertEqual(fake_attr1, obj.first)\n self.assertEqual(fake_attr2, obj.second)\n\n @httpretty.activate\n def test_create(self):\n self.stub_url(httpretty.POST, path=fake_path, json=fake_body)\n\n obj = FakeResource.new(name=fake_name,\n attr1=fake_attr1,\n attr2=fake_attr2)\n\n obj.create(self.session)\n self.assertFalse(obj.is_dirty)\n\n last_req = httpretty.last_request().parsed_body[fake_resource]\n\n self.assertEqual(3, len(last_req))\n self.assertEqual(fake_name, last_req['name'])\n self.assertEqual(fake_attr1, last_req['attr1'])\n self.assertEqual(fake_attr2, last_req['attr2'])\n\n self.assertEqual(fake_id, obj.id)\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_attr1, obj['attr1'])\n self.assertEqual(fake_attr2, obj['attr2'])\n\n self.assertEqual(fake_name, obj.name)\n self.assertEqual(fake_attr1, obj.first)\n self.assertEqual(fake_attr2, obj.second)\n\n @httpretty.activate\n def test_get(self):\n self.stub_url(httpretty.GET, path=[fake_path, fake_id], json=fake_body)\n obj = FakeResource.get_by_id(self.session, fake_id,\n path_args=fake_arguments)\n\n self.assertEqual(fake_id, obj.id)\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_attr1, obj['attr1'])\n self.assertEqual(fake_attr2, obj['attr2'])\n\n self.assertEqual(fake_name, obj.name)\n self.assertEqual(fake_attr1, obj.first)\n self.assertEqual(fake_attr2, obj.second)\n\n @httpretty.activate\n def test_head(self):\n self.stub_url(httpretty.HEAD, path=[fake_path, fake_id],\n name=fake_name,\n attr1=fake_attr1,\n attr2=fake_attr2)\n obj = FakeResource.head_by_id(self.session, fake_id,\n path_args=fake_arguments)\n\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_attr1, obj['attr1'])\n self.assertEqual(fake_attr2, obj['attr2'])\n\n self.assertEqual(fake_name, obj.name)\n self.assertEqual(fake_attr1, obj.first)\n self.assertEqual(fake_attr2, obj.second)\n\n @httpretty.activate\n def test_update(self):\n new_attr1 = 'attr5'\n new_attr2 = 'attr6'\n fake_body1 = copy.deepcopy(fake_body)\n fake_body1[fake_resource]['attr1'] = new_attr1\n\n self.stub_url(httpretty.POST, path=fake_path, json=fake_body1)\n self.stub_url(httpretty.PATCH,\n path=[fake_path, fake_id],\n json=fake_body)\n\n obj = FakeResource.new(name=fake_name,\n attr1=new_attr1,\n attr2=new_attr2)\n obj.create(self.session)\n self.assertFalse(obj.is_dirty)\n self.assertEqual(new_attr1, obj['attr1'])\n\n obj['attr1'] = fake_attr1\n obj.second = fake_attr2\n self.assertTrue(obj.is_dirty)\n\n obj.update(self.session)\n self.assertFalse(obj.is_dirty)\n\n last_req = httpretty.last_request().parsed_body[fake_resource]\n self.assertEqual(1, len(last_req))\n self.assertEqual(fake_attr1, last_req['attr1'])\n\n self.assertEqual(fake_id, obj.id)\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_attr1, obj['attr1'])\n self.assertEqual(fake_attr2, obj['attr2'])\n\n self.assertEqual(fake_name, obj.name)\n self.assertEqual(fake_attr1, obj.first)\n self.assertEqual(fake_attr2, obj.second)\n\n @httpretty.activate\n def test_delete(self):\n self.stub_url(httpretty.GET, path=[fake_path, fake_id], json=fake_body)\n self.stub_url(httpretty.DELETE, [fake_path, fake_id])\n obj = FakeResource.get_by_id(self.session, fake_id,\n path_args=fake_arguments)\n\n obj.delete(self.session)\n\n last_req = httpretty.last_request()\n self.assertEqual('DELETE', last_req.method)\n self.assertEqual('/endpoint/fakes/rey/data/99', last_req.path)\n\n @httpretty.activate\n def test_list(self):\n results = [fake_data.copy(), fake_data.copy(), fake_data.copy()]\n for i in range(len(results)):\n results[i]['id'] = fake_id + i\n\n self.stub_url(httpretty.GET,\n path=[fake_path],\n json={fake_resources: results})\n\n objs = FakeResource.list(self.session, marker='x',\n path_args=fake_arguments)\n\n self.assertIn('marker=x', httpretty.last_request().path)\n self.assertEqual(3, len(objs))\n\n for obj in objs:\n self.assertIn(obj.id, range(fake_id, fake_id + 3))\n self.assertEqual(fake_name, obj['name'])\n self.assertEqual(fake_name, obj.name)\n self.assertIsInstance(obj, FakeResource)\n\n def test_attrs(self):\n obj = FakeResource()\n\n try:\n obj.name\n except AttributeError:\n pass\n else:\n self.fail(\"Didn't raise attribute error\")\n\n try:\n del obj.name\n except AttributeError:\n pass\n else:\n self.fail(\"Didn't raise attribute error\")\n\n\nclass FakeResponse:\n def __init__(self, response):\n self.body = response\n\n\nclass TestFind(base.TestCase):\n NAME = 'matrix'\n ID = 'Fishburne'\n\n def setUp(self):\n super(TestFind, self).setUp()\n self.mock_session = mock.Mock()\n self.mock_get = mock.Mock()\n self.mock_session.get = self.mock_get\n self.matrix = {'id': self.ID}\n\n def test_name(self):\n self.mock_get.side_effect = [\n FakeResponse({FakeResource.resources_key: []}),\n FakeResponse({FakeResource.resources_key: [self.matrix]})\n ]\n\n result = FakeResource.find(self.mock_session, self.NAME,\n path_args=fake_arguments)\n\n self.assertEqual(self.ID, result.id)\n p = {'fields': 'id', 'name': self.NAME}\n self.mock_get.assert_called_with(fake_path, params=p, service=None)\n\n def test_id(self):\n resp = FakeResponse({FakeResource.resources_key: [self.matrix]})\n self.mock_get.return_value = resp\n\n result = FakeResource.find(self.mock_session, self.ID,\n path_args=fake_arguments)\n\n self.assertEqual(self.ID, result.id)\n p = {'fields': 'id', 'id': self.ID}\n self.mock_get.assert_called_with(fake_path, params=p, service=None)\n\n def test_nameo(self):\n self.mock_get.side_effect = [\n FakeResponse({FakeResource.resources_key: []}),\n FakeResponse({FakeResource.resources_key: [self.matrix]})\n ]\n FakeResource.name_attribute = 'nameo'\n\n result = FakeResource.find(self.mock_session, self.NAME,\n path_args=fake_arguments)\n\n FakeResource.name_attribute = 'name'\n self.assertEqual(self.ID, result.id)\n p = {'fields': 'id', 'nameo': self.NAME}\n self.mock_get.assert_called_with(fake_path, params=p, service=None)\n\n def test_dups(self):\n dup = {'id': 'Larry'}\n resp = FakeResponse({FakeResource.resources_key: [self.matrix, dup]})\n self.mock_get.return_value = resp\n\n self.assertRaises(exceptions.DuplicateResource, FakeResource.find,\n self.mock_session, self.NAME)\n\n def test_nada(self):\n resp = FakeResponse({FakeResource.resources_key: []})\n self.mock_get.return_value = resp\n\n self.assertRaises(exceptions.ResourceNotFound, FakeResource.find,\n self.mock_session, self.NAME)\n\n def test_no_name(self):\n self.mock_get.side_effect = [\n FakeResponse({FakeResource.resources_key: []}),\n FakeResponse({FakeResource.resources_key: [self.matrix]})\n ]\n FakeResource.name_attribute = None\n\n self.assertRaises(exceptions.ResourceNotFound, FakeResource.find,\n self.mock_session, self.NAME)\n\n def test_repr_name(self):\n FakeResource.resource_name = 'foo'\n self.assertEqual('foo: {}', repr(FakeResource()))\n FakeResource.resource_name = None\n FakeResource.resource_key = None\n self.assertEqual('FakeResource: {}', repr(FakeResource()))\n FakeResource.resource_key = fake_resource\n self.assertEqual(fake_resource + ': {}', repr(FakeResource()))\n\n def test_id_attribute(self):\n faker = FakeResource(fake_data)\n self.assertEqual(fake_id, faker.id)\n faker.id_attribute = 'name'\n self.assertEqual(fake_name, faker.id)\n faker.id_attribute = 'attr1'\n self.assertEqual(fake_attr1, faker.id)\n faker.id_attribute = 'attr2'\n self.assertEqual(fake_attr2, faker.id)\n faker.id_attribute = 'id'\n self.assertEqual(fake_id, faker.id)\n","sub_path":"openstack/tests/test_resource.py","file_name":"test_resource.py","file_ext":"py","file_size_in_byte":11363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"557102482","text":"#!/usr/bin/python3\n\"\"\"a class Square that manages: size\n\"\"\"\n\n\nclass Node:\n \"\"\"a class Node\n \"\"\"\n def __init__(self, data, next_node=None):\n self.data = data\n self.next_node = next_node\n\n @property\n def data(self):\n return self.__data\n\n @data.setter\n def data(self, value):\n if value is None:\n self.__data = value\n else:\n if not isinstance(value, int):\n raise TypeError('data must be an integer')\n else:\n self.__data = value\n\n @property\n def next_node(self):\n return self.__next_node\n\n @next_node.setter\n def next_node(self, value):\n if not value:\n self.__next_node = value\n else:\n if not isinstance(value, Node):\n raise TypeError('next_node must be a Node object')\n else:\n self.__next_node = value\n\n\nclass SinglyLinkedList:\n \"\"\"a class SinglyLinkedList\n \"\"\"\n def __init__(self):\n self.__head = Node(None)\n\n def __str__(self):\n ss = \"\"\n cur = self.__head\n if cur.data is not None:\n while cur.next_node is not None:\n ss += str(cur.data)\n ss += '\\n'\n cur = cur.next_node\n ss += str(cur.data)\n ss += '\\n'\n return ss[:-1]\n\n def sorted_insert(self, value):\n new = Node(value)\n cur = self.__head\n if cur is None:\n self.__head = new\n elif cur.data is None:\n cur.data = value\n else:\n if value <= cur.data:\n new.next_node = self.__head\n self.__head = new\n else:\n while cur.next_node is not None:\n if value < cur.next_node.data:\n break\n cur = cur.next_node\n new.next_node = cur.next_node\n cur.next_node = new\n","sub_path":"0x06-python-classes/100-singly_linked_list.py","file_name":"100-singly_linked_list.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"502537964","text":"from math import sin, cos\n\n\nclass log():\n def __init__(self, fileName):\n self.fileName = fileName\n self._file = open(fileName, 'w+')\n self._file.write(\n 'Time(s) pitch(rad) yaw(rad) roll(rad) pitchspeed(rad/s) yawspeed(rad/s) rollspeed(rad/s) pn(m) pe(m) alt[MSL](m) vnorth[NED](m/s) veast[NED](m/s) vdown[NED](m/s) xacc(m/s^2) yacc(m/s^2) zacc(m/s^2) IAS(m/s) AOA() Sideslip() RCch1() RCch2() RCch3() RCch4()\\n')\n\n def addEntry(self, state, delta, sensors, time):\n text = '{time} {theta} {psi} {phi} {q} {r} {p} {pn} {pe} {h} {vNorth} {vEast} {vDown} {xacc} {yacc} {zacc} {Va} {alpha} {beta} {delta_a} {delta_e} {delta_t} {delta_r}'\n text = text.format(time=time, theta=state.theta, psi=state.psi, phi=state.phi, q=state.q, r=state.r, p=state.p, pn=state.pn,\n pe=state.pe, h=state.h, vNorth=state.Vg * cos(state.chi) * cos(state.gamma), vEast=state.Vg * sin(state.chi) * cos(state.gamma),\n vDown=-state.Vg * sin(state.gamma), xacc=sensors.accel_x, yacc=sensors.accel_y, zacc=sensors.accel_z, Va=state.Va, alpha=state.alpha, beta=state.beta, delta_a=delta.aileron,\n delta_e=delta.elevator, delta_t=delta.throttle, delta_r=delta.rudder)\n self._file.write(text + '\\n')\n\n def closeLog(self):\n self._file.close()\n","sub_path":"UAV-P2/Raspberry_Pi/tools/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"77982805","text":"\"\"\"Plot Sverdrup transport meridional section computed from wind stress\"\"\"\nfrom __future__ import print_function\nimport numpy as np\nimport matplotlib.pyplot as plt ; plt.close('all')\nimport os\n\nimport poppy.grid\nimport phdpy.sverdrup\n\nfrom phdpy import settings\n\n\ndef _lon0str(lon0):\n return '{:.0f}W'.format(-lon0) if lon0<0 else '{:.0f}E'.format(lon0)\n\ndef plot_sverdrup_transport_section(dsids,region,lon0,tautimes=-1,\n plotmap=False,savefig=False):\n \"\"\"Plot Sverdrup Transport Meridional Section\"\"\"\n fig,ax = plt.subplots()\n\n title = 'Sverdrup transport meridional section at {}'.format(_lon0str(lon0))\n ax.set_title(title)\n\n for dsid in dsids:\n # check whether the data of one dsid should be \n # interpolated to the grid of another\n dsidfull = dsid\n dsid = dsidfull.split('_int_')[0]\n try:\n inttofname = settings.datafiles[dsidfull.split('_int_')[1]]\n except IndexError:\n inttofname = None\n except KeyError:\n raise\n\n # get sverdrup transport\n lon0_rewrap = settings.region_lon0[region]\n lon,lat,psi = phdpy.sverdrup.get_psi(settings.datafiles[dsid],\n region=region,tautimes=tautimes,lon0_rewrap=lon0_rewrap,\n interpolate_to_grid_from_file=inttofname)\n\n # get landmask\n # mask also everything north of 60N, where the grid becomes funny\n landmask_regular = psi.mask | (lat>60)\n\n # find section\n meanlon = np.mean(np.ma.masked_where(landmask_regular,lon),axis=0)\n i0 = np.argmin(np.abs(np.mod(meanlon,360)-np.mod(lon0,360)))\n lon0_comp = meanlon[i0]\n latax = lat[:,i0]\n\n psi_section = psi[:,i0]\n if np.all(psi_section.mask): raise ValueError('All values in psi_section are masked!')\n\n # add a map of the section\n if plotmap and dsid == dsids[0]:\n subax = _add_subplot_axes(ax,[0.6, 0.02, 0.3, 0.3])\n subax.pcolormesh(psi)\n subax.axvline(i0,0.1,0.9,color='0.5',ls=settings.lineplotfmt[dsid]['ls'])\n subax.autoscale(tight=True)\n subax.axis('off')\n #subax.pcolormesh(np.mod(meanlon,360),latax,psi)\n #subax.axvline(lon0_comp,**settings.lineplotfmt[dsid])\n #subax.yaxis.set_visible(False)\n #subax.tick_params(axis='both', direction='in')\n #subax.get_xaxis().tick_bottom()\n\n # plot psi section\n ax.plot(latax,psi_section,label=dsidfull,**settings.lineplotfmt[dsid])\n\n\n ax.set_ylabel('Sverdrup Transport (Sv)')\n ax.grid(True)\n #ax.set_xlim(-80,60)\n ax.legend(loc=0)\n ax.set_xlabel('latitude')\n ax.set_xlim(settings.lataxlim[region])\n\n if savefig:\n figname = os.path.join(settings.figdir,'sverdrup_transport',\n 'sverdrup_transport_section_{}_at_{}_{}'.format(region,_lon0str(lon0),'_'.join(dsids)))\n fig.savefig(figname+'.png',dpi=300)\n else:\n plt.show()\n\n\ndef _add_subplot_axes(ax,rect,axisbg='w'):\n fig = ax.figure\n box = ax.get_position()\n width = box.width\n height = box.height\n inax_position = ax.transAxes.transform(rect[0:2])\n transFigure = fig.transFigure.inverted()\n infig_position = transFigure.transform(inax_position) \n x = infig_position[0]\n y = infig_position[1]\n width *= rect[2]\n height *= rect[3] \n subax = fig.add_axes([x,y,width,height],axisbg=axisbg)\n x_labelsize = subax.get_xticklabels()[0].get_size()\n y_labelsize = subax.get_yticklabels()[0].get_size()\n x_labelsize *= rect[2]**0.5\n y_labelsize *= rect[3]**0.5\n subax.xaxis.set_tick_params(labelsize=x_labelsize)\n subax.yaxis.set_tick_params(labelsize=y_labelsize)\n return subax\n\nif __name__ == '__main__':\n \n defaults = dict( \n dsids = ['x3','x1','x010'],\n region = 'Indo-Pacific',\n lon0 = 180,\n tautimes = -1,\n )\n\n import argparse\n parser = argparse.ArgumentParser(description=\"Plot Sverdrup transport computed from wind stress\")\n parser.add_argument('-d','--dsids',type=(lambda s: s.split(',')),help='dataset IDs, separated by comma')\n parser.add_argument('-r','--region',type=str,help='region')\n parser.add_argument('-l','--lon0',type=float,help='Longitude at which to draw the meridional section')\n parser.add_argument('--tautimes',type=int,choices=[-1,1],help='factor to multiply tau by')\n parser.add_argument('-m',dest='plotmap',action='store_true',help='set this to plot a map of the section')\n parser.add_argument('-s',dest='savefig',action='store_true',help='set this to save the figure')\n\n parser.set_defaults(**defaults)\n args = parser.parse_args()\n \n plot_sverdrup_transport_section(**vars(args))\n","sub_path":"wind_driven_circulation/sverdrup_transport_sections.py","file_name":"sverdrup_transport_sections.py","file_ext":"py","file_size_in_byte":4738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"191441953","text":"from fastapi import FastAPI\nfrom starlette.middleware.cors import CORSMiddleware\n\nfrom identify.api.api_v1.api import api_router\n\napp = FastAPI()\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=['*'],\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\napp.include_router(api_router)\n\n\nif __name__ == '__main__':\n import uvicorn\n uvicorn.run(app, host='0.0.0.0', port=9009)\n","sub_path":"identify/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"456908730","text":"# -*- coding: utf-8 -*-\nimport sys\n\ndef main():\n #ここに関数記述\n f = open(sys.argv[1],'r')\n f1 = open('make/col1.txt','w')\n f2 = open('make/col2.txt','w')\n for line in f:\n line = line.split('\\t')\n f1.write(line[0] + '\\n')\n f2.write(line[1])\n f.close()\n f1.close()\n f2.close()\n \nif(__name__=='__main__'):\n main()\n","sub_path":"003.py","file_name":"003.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"264175031","text":"import pygame\r\nimport sys\r\nimport Jogo\r\nimport Print\r\nimport InterfaceInicial\r\nimport InterfaceDificuldade\r\nimport InterfaceFinal\r\nimport InterfaceDefenições\r\nimport InterfaceCores\r\nimport InterfaceSelectCores\r\nimport files\r\nimport InterfaceResultados\r\nimport Results\r\n\r\n# Ecra para definir a fase que o jogo representra\r\n# INICIO = 1\r\n# DEFENIÇÕES = 2\r\n# SKILL = 3\r\n# JOGO = 4\r\n# FINAL = 5\r\n# CORES = 6\r\n# SELECT_CORES = 7\r\necra = 1\r\n\r\n# Tamanho do ecra\r\nTAMANHO = 880\r\npygame.init()\r\nscreen = pygame.display.set_mode((TAMANHO, TAMANHO))\r\ngame_over = False\r\npygame.display.set_caption(\"Definitely Not Sudoku\")\r\nicon = pygame.image.load('Images/Logo.png')\r\npygame.display.set_icon(icon)\r\n\r\n# Variaveis de inicialização\r\ndificuldade = 35\r\nn_select = False\r\ntimes = 1\r\nstrart_time = -times\r\nbotoes_jogo = [False, False, False, False]\r\nbotoes_inicio = [False, False, False, False, False, False]\r\nbotoes_defenicao = [False, True, True, True, False]\r\nbotoes_dificuldade = [False, False, False, False, False, False]\r\nbotoes_cores = [False, False, False, False, False, False, False]\r\nbotoes_select_cores = [False, False, False, False, False, False, False, False, False, False, False]\r\nbotoes_fim = [False, False]\r\nbotoes_resultados = [False, False]\r\ninicio = True\r\nresult = \"Erro\"\r\nswitch_white = False\r\n\r\n# Variaveis de jogo\r\nerrados = []\r\nmat = []\r\nalterados = []\r\njogo = []\r\ncoluna = 0\r\nlinha = 0\r\ntempo = 0\r\nbest_results = Results.ler_resultados()\r\n\r\n# Cores\r\nfirst = 1\r\nsecond = 0\r\nthird = 4\r\nfourth = 7\r\n\r\ndefenicoes = files.gera_def(first, second, third, fourth, switch_white, dificuldade, botoes_defenicao[1], botoes_defenicao[2], botoes_defenicao[3])\r\ndefenicoes = files.ler_def(defenicoes)\r\nfirst, second, third, fourth, switch_white, dificuldade, botoes_defenicao[1], botoes_defenicao[2], botoes_defenicao[3] = files.act_def(defenicoes)\r\n\r\nwrong = botoes_defenicao[1]\r\nhelps = botoes_defenicao[2]\r\nfix = botoes_defenicao[3]\r\n\r\n# Carrega corres botoes\r\nbase, alternativo, small_base, small_alternativo, mini_base, mini_alternativo, small_third, mini_third, small_fourth, \\\r\n mini_fourth = Print.carrega_cores(first, second, third, fourth)\r\ncolour = mini_base\r\ntela_cor = 0\r\n\r\n# Ciclo do jogo\r\nwhile not game_over:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n sys.exit()\r\n\r\n base, alternativo, small_base, small_alternativo, mini_base, mini_alternativo, small_third, mini_third,\\\r\n small_fourth, mini_fourth = Print.carrega_cores(first, second, third, fourth)\r\n cor_letras = Print.print_fundo(screen, switch_white)\r\n\r\n if ecra == 1:\r\n # Ecra inicial do jogo\r\n inicio = True\r\n ecra, game_over = InterfaceInicial.strart(screen, botoes_inicio, ecra, base, alternativo, game_over, first, second,\r\n third, fourth, switch_white, dificuldade, wrong, helps, fix)\r\n elif ecra == 2:\r\n # Menu das defenições\r\n wrong, helps, fix, ecra = InterfaceDefenições.defenicoes(screen, botoes_defenicao, ecra, dificuldade, base,\r\n alternativo, small_base, small_alternativo,\r\n switch_white)\r\n elif ecra == 3:\r\n # Menu das dificuldades\r\n dificuldade, ecra = InterfaceDificuldade.imprime_dificuldade(screen, dificuldade, botoes_dificuldade, base,\r\n alternativo, switch_white)\r\n elif ecra == 4:\r\n # Jogo\r\n mat, n_select, colour, jogo, linha, coluna, botoes_jogo, errados, alterados, strart_time, times, \\\r\n inicio, ecra, tempo, result, best_results = Jogo.sudoku(screen, event, n_select, colour, linha, coluna, alterados,\r\n errados, mat, jogo, botoes_jogo, strart_time, times, wrong,\r\n helps, fix, dificuldade, inicio, ecra, tempo, small_base,\r\n small_alternativo, mini_base, mini_alternativo, mini_third,\r\n mini_fourth, switch_white, cor_letras, best_results)\r\n elif ecra == 5:\r\n # Ecra no fim do jogo\r\n ecra, game_over = InterfaceFinal.final(screen, botoes_fim, ecra, result, tempo, game_over, base,\r\n alternativo, switch_white, cor_letras)\r\n elif ecra == 6:\r\n # Ecra de escolha de cores\r\n ecra, tela_cor, switch_white = InterfaceCores.i_cores(screen, botoes_cores, ecra, base, alternativo, tela_cor, small_base,\r\n small_alternativo, small_third, small_fourth, switch_white)\r\n elif ecra == 7:\r\n # Ecra de escolha de uma cor\r\n ecra, tela_cor, first, second, third, fourth = InterfaceSelectCores.i_select_cores(screen,\r\n botoes_select_cores,\r\n ecra, base, alternativo,\r\n tela_cor, first, second,\r\n third, fourth,\r\n switch_white)\r\n elif ecra == 8:\r\n ecra, best_results = InterfaceResultados.resultados(screen, botoes_resultados, ecra, base, alternativo, switch_white, best_results, cor_letras)\r\n\r\n pygame.display.update()\r\n","sub_path":"Pyton/Sudoku/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"256249335","text":"import sys\nimport os\nsys.path.append(\".\")\nfrom pokemon import pokemon\n\ndef get_pokemon_from_file(filename):\n pokemon_list = []\n file = open(filename,\"r\")\n text = file.read()\n file.close()\n lines = text.split(\"\\n\")\n for line in lines:\n new_poke = pokemon()\n if new_poke.get_from_string(line):\n pokemon_list.append(new_poke)\n return pokemon_list\n\npoke_list = \"../Additional_files/pokemon_list.txt\"\nsprites_dir = \"../Additional_files/pokemon_sprites/\"\nshiny_sprites_dir = \"../Additional_files/shiny_pokemon_sprites/\"\npokemon_list = get_pokemon_from_file(poke_list)\nexceptions = {}\nexceptions[\"083\"] = \"farfetchd\"\nexceptions[\"083G\"] = \"farfetchd-galar\"\nexceptions[\"029\"] = \"nidoran-f\"\nexceptions[\"032\"] = \"nidoran-m\"\nexceptions[\"122\"] = \"mr-mime\"\nexceptions[\"122G\"] = \"mr-mime-galar\"\nexceptions[\"439\"] = \"mime-jr\"\nexceptions[\"669\"] = \"flabebe\"\nexceptions[\"772\"] = \"type-null\"\nexceptions[\"865\"] = \"sirfetchd\"\nexceptions[\"866\"] = \"mr-rime\"\nexceptions[\"785\"] = \"tapu-koko\"\nexceptions[\"786\"] = \"tapu-lele\"\nexceptions[\"787\"] = \"tapu-bulu\"\nexceptions[\"788\"] = \"tapu-fini\"\nekeys = exceptions.keys()\nfor poke in pokemon_list:\n region = \"\"\n old_name = None\n for key in ekeys:\n if key == poke.national_number:\n old_name = exceptions[key]\n break\n if old_name == None:\n old_name = poke.name.lower()\n if poke.regional_variant():\n region = \"-\" + poke.region.lower()\n old_name = old_name + region\n try:\n os.rename(sprites_dir+old_name+\".png\", sprites_dir+poke.national_number+\".png\")\n except FileNotFoundError:\n pass\n try:\n os.rename(shiny_sprites_dir+old_name+\".png\", shiny_sprites_dir+poke.national_number+\".png\")\n except FileNotFoundError:\n pass","sub_path":"python_scripts/rename_iamges.py","file_name":"rename_iamges.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"113803207","text":"import requests,os,hashlib,time,pandas\nfrom propertiesUtil import Properties\nfrom params import Params\nfrom lxml import html\nfrom urllib import parse\n\nproxy = {'http:':'127.0.0.1:8888'}\nlogin_postUrl = 'http://10.2.3.58:808/webadmin/user_admlogin.do'\nlogout_postUrl = 'http://10.2.3.58:808/webadmin/user_admlogout.do'\nlogin_qaptchaUrl = 'http://10.2.3.58:808/front/ajax_getMathValidCode.do'\ndomain_url = 'http://10.2.3.58:808/webadmin/user_adm.do'\naddinfo_url = 'http://10.2.3.58:808/webadmin/article_addinfo.do'\nupload_url = 'http://10.2.3.58:808/webadmin/article/upload.jsp'\nsaveupload_url= 'http://10.2.3.58:808/webadmin/article_addinfo.do'\nsearch_url= 'http://10.2.3.58:808/webadmin/article.do?id=6406407&prevContact=&contact=&groupId=0&siteId=0'\nprops = Properties('upload.properties').getProperties()\nBETA=props.get('beta','')\nerrors=[]\n\n\ndef log(msg):\n print('%s>: --------->%s...........'%(time.strftime('%Y-%m-%d %H:%M:%S',time.localtime()),msg))\n#登录\ndef login(s):\n # 验证码\n s.post(login_qaptchaUrl, data=Params.QAPTCHA())\n # 登录请求\n r = s.post(login_postUrl,data=Params.LOGINDATA(props['user'],props['password']))\n return r.text=='login'\n\n#根节点id\ndef getnodeids(s,title):\n r = s.post(search_url,data=Params.SEARCHDATA(title))\n #print(r.content)\n root = html.etree.HTML(r.content)\n nodeids = root.xpath('//td/input[@name=\"id\"]/@value')\n return nodeids\n\ndef uploadexcel(s):\n rowdatas = pandas.read_excel(os.path.join(props['dirpath'], props['excelfile']))\n uploads=[]\n for vin, fdjcj, clxh, fdjxh, xxgkbh, fdjbh in zip(rowdatas[' VIN[Title]'], rowdatas['发动机厂家[Name]'],\n rowdatas['车型[Single1]'], rowdatas['发动机型号[Single2]'],\n rowdatas['信息公开编号[Single3]'], rowdatas['发动机编号[Single4]']):\n nodeids = getnodeids(s, vin)\n if len(nodeids)>0:\n errors.append('%s已存在%s条记录,插入失败'%(vin,len(nodeids)))\n continue\n rowdata = Params.CREATEROW(vin, fdjcj, clxh, fdjxh, xxgkbh, fdjbh)\n r = s.post(addinfo_url,data=rowdata,allow_redirects=False)\n if(r.headers['Location'] == 'article.do?id=6406407&groupId=0&siteId=0'):\n nodeids = getnodeids(s,vin)\n if len(nodeids) == 1:\n uploads.append((str(vin),nodeids[0],clxh))\n continue\n errors.append('%s数据插入失败'%(vin))\n return uploads\n\ndef uploadwordpdf(s,uploads):\n success=[]\n for vin,nodeid,clxh in uploads:\n lbjpath=os.path.join(props['hbgjlbjpath'],clxh)\n wordfile1=os.path.join(lbjpath,'环保关键零部件.doc')\n wordfile2=os.path.join(lbjpath,'环保关键零部件.docx')\n pdffile=os.path.join(props['dirpath'],'PDF/%s.pdf'%(vin))\n wordfile=wordfile2\n if os.path.exists(wordfile2):\n wordfile = wordfile2\n elif os.path.exists(wordfile1):\n wordfile = wordfile1\n else:\n errors.append('%s丢失Word文件!' % wordfile)\n continue\n if os.path.exists(pdffile) == False:\n errors.append('%s丢失Pdf文件' % pdffile)\n continue\n\n # 上传pdf文件\n with open(pdffile, 'rb') as f:\n filename = '%s.pdf' % (vin)\n files = {'myfile': (filename, f, 'application/octet-stream')}\n r = s.post(upload_url, data=Params.UPFILEDATA(filename), files=files)\n filepath = r.text\n if filepath.find('/photos/') != -1:\n # pdf文件绑定\n r = s.post(saveupload_url, data=Params.UPFILESAVE('%s%s'%(BETA,vin), filepath, nodeid),allow_redirects=False)\n if r.status_code != 302:\n errors.append('%s pdf上传失败!' % (vin))\n else:\n success.append(vin)\n else:\n errors.append('%s pdf上传失败!' % (vin))\n\n # 上传word文件\n with open(wordfile, 'rb') as f:\n if wordfile == wordfile1:\n filename = '环保关键零部件.doc'\n else:\n filename = '环保关键零部件.docx'\n files = {'myfile': (parse.quote(filename), f, 'application/octet-stream')}\n r = s.post(upload_url, data=Params.UPFILEDATA(filename), files=files)\n filepath = r.text.strip()\n if filepath.find('/photos/') != -1:\n # word文件绑定\n r = s.post(saveupload_url, data=Params.UPFILESAVE('%s环保关键零部件' % (BETA), filepath, nodeid),\n allow_redirects=False)\n if r.status_code != 302:\n errors.append('%s word上传失败!' % (vin))\n else:\n success.append(vin)\n else:\n errors.append('%s word上传失败!' % (vin))\n success = set(filter(lambda x:success.count(x)==2,success))\n return success\n\ndef uploadWormWork():\n s = requests.session()\n s.headers.update({'User-Agent': 'User-Agent:Mozilla/5.0(WindowsNT6.1;rv:2.0.1)Gecko/20100101Firefox/4.0.1'})\n s.proxies = proxy\n try:\n #step1 用户登录\n log('%s开始登录' % props['user'])\n if login(s)==False:\n raise Exception('用户/密码名错误')\n log('%s登录成功' % props['user'])\n #step2 导入数据\n log('开始导入excel数据')\n uploads = uploadexcel(s)\n log('excel导入结束')\n #step3 上传文件\n log('开始上传文件')\n success = uploadwordpdf(s,uploads)\n log('文件上传结束')\n #step4 打印结果\n log('处理结果:处理成功%s条数据'%(len(success)))\n if len(errors)==0:\n log('数据全部处理成功！')\n else:\n for error in errors:\n log(error)\n except Exception as e:\n log('发生异常,提前结束:')\n log(e)\n\n finally:\n s.get(logout_postUrl,allow_redirects=False)\n log('%s登出'%props['user'])\n s.close()\n\nif __name__ == '__main__':\n uploadWormWork()","sub_path":"python/epAutoUpload.py","file_name":"epAutoUpload.py","file_ext":"py","file_size_in_byte":6195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"248480526","text":"from builtins import object\nimport cgi\nimport cherrypy\nimport logging\nimport splunk.util\nfrom decorator import decorator\n\nlogger = logging.getLogger('splunk.appserver.mrsparkle.lib.message')\n\nQUEUE_SESSION_KEY = 'queue'\nQUEUE_INFO_LEVEL = 'info'\nQUEUE_ERROR_LEVEL = 'error'\n\n\n\ndef get_session_queue():\n \"\"\"\n Creates or returns une pickled session Queue object with a key of QUEUE_SESSION_KEY\n \"\"\"\n sess = cherrypy.session\n if QUEUE_SESSION_KEY in sess:\n return sess.get(QUEUE_SESSION_KEY)\n else:\n sess[QUEUE_SESSION_KEY] = SessionQueue()\n return sess[QUEUE_SESSION_KEY]\n\n@decorator\ndef save_to_session(fn, self, *a, **kw):\n '''Simple decorator that ensures cherrypy's session gets re-written.'''\n ret_val = fn(self, *a, **kw)\n cherrypy._test_session_has_changed = True\n if (hasattr(cherrypy, 'session')):\n if self.isChanged():\n cherrypy.session.acquire_lock()\n if hasattr(cherrypy.session, 'changed'):\n cherrypy.session.changed = True\n cherrypy.session[QUEUE_SESSION_KEY] = self\n \n return ret_val\n\n\ndef send_client_message(level, msg):\n '''Mechanism for sending a message to the client from the server.'''\n cherrypy.response.headers['X-Splunk-Messages-Available'] = 1\n get_session_queue().add(level, msg)\n\n\nclass Queue(object):\n \"\"\"\n A dead simple container for storing temporary system messages categorized by level.\n \"\"\"\n\n def __init__(self):\n self.queue = []\n self.changed = False\n\n def isChanged(self):\n return self.changed\n\n def add(self, level, message):\n \"\"\"\n Add a message to a list with a specified level.\n Order is perserved.\n Args:\n level: The level marker for the message.\n message: The message string to store.\n \"\"\"\n logger.debug('adding level:%s, message:%s' % (level, message))\n self.changed = True\n self.queue.append({'message': message, 'time': splunk.util.getISOTime(), 'level': level})\n\n def get_level(self, level, delete=True):\n \"\"\"\n Retrieve a list of messages based on a specified level.\n Args:\n level: The level cagegory for a list of messages.\n delete: Delete the list of messages from this level after retrieval.\n \"\"\" \n matches = []\n items = self.queue[:]\n for item in items:\n if item['level'] is level:\n matches.append(item)\n if delete:\n self.queue.pop(self.queue.index(item))\n if matches:\n self.changed = delete\n else:\n self.changed = False\n return matches \n \n def get_levels(self):\n \"\"\"\n Retrieve a sorted list of distinct message levels stored in the queue.\n \"\"\" \n levels = []\n for item in self.queue:\n levels.append(item['level'])\n uniques = sorted(set(levels))\n return uniques\n \n def get_len(self, level=None):\n \"\"\"\n Retrieve the length of messages based on a specified level or the length of all messages combined.\n Args:\n level: The level cagegory for a list of messages.\n \"\"\" \n if level is None:\n return len(self.queue)\n else:\n return len(self.get_level(level, delete=False))\n \n def get_all(self, delete=True):\n \"\"\"\n Retrieve the entire message list.\n Args:\n delete: Delete the entire message list entries after retrieval.\n \"\"\" \n self.changed = False\n queue = self.queue[:]\n if delete and self.queue:\n self.changed = True\n self.queue = []\n return queue\n\n def fifo(self, delete=True):\n \"\"\"\n First in first out (fifo) - retrieve the first message in the list.\n Args:\n delete: Delete the message list entry after retrieval.\n \"\"\"\n if len(self.queue) is 0:\n self.changed = False\n return None\n if delete:\n self.changed = True\n queue = self.queue\n else:\n self.changed = False\n queue = self.queue[:]\n return queue.pop(0)\n \n def lifo(self, delete=True): \n \"\"\"\n Last in first out (lifo) - retrieve the last message in the list.\n Args:\n delete: Delete the message list entry after retrieval.\n \"\"\"\n if len(self.queue) is 0:\n self.changed = False\n return None\n if delete:\n self.changed = True\n queue = self.queue\n else:\n self.changed = False\n queue = self.queue[:]\n return queue.pop()\n\n\n\nclass SessionQueue(Queue):\n '''\n A mirror of the Queue object that ensures if it's stored in a modified\n Cherrypy session, the session is properly rewritten when necessary.\n '''\n\n def __init__(self):\n Queue.__init__(self)\n\n @save_to_session\n def add(self, level, message):\n super(SessionQueue, self).add(level, message)\n\n @save_to_session\n def get_level(self, level, delete=True):\n return super(SessionQueue, self).get_level(level, delete)\n \n @save_to_session\n def get_all(self, delete=True):\n return super(SessionQueue, self).get_all(delete)\n\n @save_to_session\n def fifo(self, delete=True):\n return super(SessionQueue, self).fifo(delete)\n \n @save_to_session \n def lifo(self, delete=True): \n return super(SessionQueue, self).lifo(delete)\n\n\nif __name__ == '__main__':\n\n import unittest\n \n class QueueTests(unittest.TestCase):\n\n def testQueue(self):\n queue = Queue()\n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n\n self.assert_(len(queue.get_levels()) is 1)\n self.assert_(queue.get_levels()[0] is \"notice\")\n self.assert_(queue.get_len(level=\"notice\") is 3)\n self.assert_(queue.get_len() is 3)\n self.assert_(len(queue.get_level(\"notice\")) is 3)\n self.assert_(len(queue.get_level(\"notice\")) is 0)\n self.assert_(queue.get_len(level=\"notice\") is 0)\n self.assert_(queue.get_len() is 0)\n \n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n\n self.assert_(len(queue.get_level(\"notice\", delete=False)) is 3)\n self.assert_(len(queue.get_level(\"notice\")) is 3)\n self.assert_(len(queue.get_level(\"notice\")) is 0)\n\n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n queue.add(\"message\", \"message string1\")\n queue.add(\"message\", \"message string2\")\n queue.add(\"message\", \"message string3\")\n queue.add(\"message\", \"message string4\")\n\n self.assert_(len(queue.get_levels()) is 2)\n self.assert_(queue.get_levels().index(\"notice\") is 1)\n self.assert_(queue.get_levels().index(\"message\") is 0)\n self.assert_(queue.get_len(level=\"notice\") is 3)\n self.assert_(queue.get_len(level=\"message\") is 4)\n self.assert_(queue.get_len() is 7)\n\n messages = queue.get_all()\n\n self.assert_(queue.get_len(level=\"notice\") is 0)\n self.assert_(queue.get_len(level=\"message\") is 0)\n self.assert_(queue.get_len() is 0)\n self.assert_(len(messages) is 7)\n self.assert_(len(queue.get_level(\"notice\")) is 0)\n self.assert_(len(queue.get_level(\"message\")) is 0)\n\n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n queue.add(\"message\", \"message string1\")\n queue.add(\"message\", \"message string2\")\n queue.add(\"message\", \"message string3\")\n queue.add(\"message\", \"message string4\")\n\n self.assert_(queue.get_len(level=\"notice\") is 3)\n self.assert_(queue.get_len(level=\"message\") is 4)\n self.assert_(queue.get_len() is 7)\n\n messages = queue.get_all(delete=False)\n\n self.assert_(queue.get_len(level=\"notice\") is 3)\n self.assert_(queue.get_len(level=\"message\") is 4)\n self.assert_(queue.get_len() is 7)\n self.assert_(len(messages) is 7)\n self.assert_(len(queue.get_level(\"notice\")) is 3)\n self.assert_(len(queue.get_level(\"message\")) is 4)\n\n queue = Queue()\n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n\n self.assert_(queue.fifo(delete=False)['message'] is \"notice string1\")\n self.assert_(queue.fifo(delete=True)['message'] is \"notice string1\")\n self.assert_(queue.fifo(delete=True)['message'] is \"notice string2\")\n self.assert_(queue.fifo(delete=True)['message'] is \"notice string3\")\n self.assert_(queue.fifo(delete=True) is None)\n\n queue = Queue()\n queue.add(\"notice\", \"notice string1\")\n queue.add(\"notice\", \"notice string2\")\n queue.add(\"notice\", \"notice string3\")\n\n self.assert_(queue.lifo(delete=False)['message'] is \"notice string3\")\n self.assert_(queue.lifo(delete=True)['message'] is \"notice string3\")\n self.assert_(queue.lifo(delete=True)['message'] is \"notice string2\")\n self.assert_(queue.lifo(delete=True)['message'] is \"notice string1\")\n self.assert_(queue.lifo(delete=True) is None)\n\n class QueueTestsChanged(unittest.TestCase):\n \n def setUp(self):\n self.queue = Queue()\n self.assert_(self.queue.isChanged() is False)\n\n self.queue.add(\"notice\", \"notice string1\")\n self.assert_(self.queue.isChanged() is True)\n\n def testQueueChangedGetLevel(self):\n #get_level\n self.queue.get_level(\"notice\", False)\n self.assert_(self.queue.isChanged() is False)\n self.queue.get_level(\"notice\", True)\n self.assert_(self.queue.isChanged() is True)\n self.queue.get_level(\"notice\")\n self.assert_(self.queue.isChanged() is False)\n\n self.queue.add(\"notice\", \"notice string1\")\n self.assert_(self.queue.isChanged() is True)\n self.queue.get_level(\"message\", True)\n self.assert_(self.queue.isChanged() is False)\n self.queue.get_level(\"notice\", True)\n self.assert_(self.queue.isChanged() is True)\n\n def testQueueChangedGetAll(self):\n #get_level\n self.queue.get_all(False)\n self.assert_(self.queue.isChanged() is False)\n self.queue.get_all(True)\n self.assert_(self.queue.isChanged() is True)\n self.queue.get_all()\n self.assert_(self.queue.isChanged() is False)\n\n def testQueueChangedFifo(self):\n #get_level\n self.queue.add(\"notice\", \"notice string2\")\n self.queue.add(\"notice\", \"notice string3\")\n\n self.assert_(self.queue.fifo(delete=False)['message'] == \"notice string1\")\n self.assert_(self.queue.isChanged() is False)\n self.assert_(self.queue.fifo(delete=True)['message'] == \"notice string1\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.fifo(delete=True)['message'] == \"notice string2\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.fifo(delete=True)['message'] == \"notice string3\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.fifo(delete=True) is None)\n self.assert_(self.queue.isChanged() is False)\n\n def testQueueChangedLifo(self):\n #get_level\n self.queue.add(\"notice\", \"notice string2\")\n self.queue.add(\"notice\", \"notice string3\")\n\n self.assert_(self.queue.lifo(delete=False)['message'] == \"notice string3\")\n self.assert_(self.queue.isChanged() is False)\n self.assert_(self.queue.lifo(delete=True)['message'] == \"notice string3\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.lifo(delete=True)['message'] == \"notice string2\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.lifo(delete=True)['message'] == \"notice string1\")\n self.assert_(self.queue.isChanged() is True)\n self.assert_(self.queue.lifo(delete=True) is None)\n self.assert_(self.queue.isChanged() is False)\n\n\n class SessionQueueTests(unittest.TestCase):\n \n def setUp(self):\n self.queue = SessionQueue()\n\n cherrypy._test_session_has_changed = False\n\n def tearDown(self):\n self.assert_(cherrypy._test_session_has_changed is True)\n self.queue = None\n\n def testSessionQueueAdding(self):\n self.queue.add('error', 'foo')\n\n def testSessionQueueGetLevel(self):\n self.queue.get_level('error')\n\n def testSessionQueueGetAll(self):\n self.queue.get_all()\n\n def testSessionQueueFifo(self):\n self.queue.fifo()\n \n def testSessionQueueLifo(self):\n self.queue.lifo()\n\n\n loader = unittest.TestLoader()\n suites = []\n suites.append(loader.loadTestsFromTestCase(QueueTests))\n suites.append(loader.loadTestsFromTestCase(QueueTestsChanged))\n suites.append(loader.loadTestsFromTestCase(SessionQueueTests))\n unittest.TextTestRunner(verbosity=2).run(unittest.TestSuite(suites))\n\n","sub_path":"appserver/mrsparkle/lib/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":13980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"358793360","text":"import os\nimport sys\nimport logging\nimport threading\nimport queue\nimport json\nfrom datetime import datetime\nfrom contextlib import contextmanager\n\nimport fire\n\nimport fluxx\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.INFO)\n\nDEFAULT_LOG_DIR = './logs'\nDEFAULT_THREAD_COUNT = 5\nDEFAULT_PER_PAGE = 100\n\n\n@contextmanager\ndef write_operation(instance, model, threads):\n \"Initialize queue, read input, start and end threads.\"\n json_data = sys.stdin.read()\n records = json.loads(json_data)\n\n if 'records' in records:\n records = records['records']\n\n yield records\n\n q = queue.Queue()\n for i, record in enumerate(records):\n item = {\n 'index': i,\n 'model': model,\n 'record': record\n }\n q.put(item)\n\n for _ in range(threads):\n worker = FluxxThread(q, instance)\n worker.daemon = True\n worker.start()\n\n q.join()\n\n\nclass FluxxThread(threading.Thread):\n\n \"\"\"Spawns a new thread performing Fluxx API\n create and update requests.\"\"\"\n\n def __init__(self, queue, instance):\n self.q = queue\n self.client = fluxx.FluxxClient.from_env(instance)\n\n super().__init__()\n\n def run(self):\n while True:\n item = self.q.get()\n\n index = item.get('index')\n model = item.get('model').lower()\n record = item.get('record')\n method = record.get('method').upper()\n\n try:\n record_id = record.pop('id', None)\n log_msg = 'Input line {}: {}d record '.format(index, method.title())\n\n if method == 'CREATE':\n created = self.client.create(model, record)\n log.info(log_msg + str(created['id']))\n\n elif method == 'UPDATE':\n updated = self.client.update(model, record_id, record)\n log.info(log_msg + str(updated['id']))\n\n elif method == 'DELETE':\n deleted = self.client.delete(model, record_id)\n log.info(log_msg + str(deleted['id']))\n else:\n log.info(log_msg + 'Method not specified')\n\n except NotImplementedError:\n log.error('Process method not implemented.')\n\n except fluxx.FluxxError as error:\n log.error(error)\n\n finally:\n self.q.task_done()\n\n\nclass FluxxCLI(object):\n\n \"\"\"Command line interface to this API wrapper, reads and writes JSON.\"\"\"\n\n def __init__(self, instance, log_dir=DEFAULT_LOG_DIR):\n self.instance = instance\n\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n\n log_file = '{}_{}.log'.format(instance, datetime.now().strftime('%x %X').replace('/', '-'))\n log_path = os.path.join(log_dir, log_file)\n\n # add file handler to module level logger\n handler = logging.FileHandler(log_path, delay=True)\n log.addHandler(handler)\n\n def list(self, model, cols, page=1, per_page=DEFAULT_PER_PAGE):\n \"\"\"Return a list of records according to the Page and PerPage\n settings. Page must be greater than 0.\n\n :model: The Fluxx ModelObject you wish to query\n :page: Section of the total list to retrieve, must be greater than 0.\n :per_page: Number of records to return per page.\n :returns: None\n\n \"\"\"\n\n client = fluxx.FluxxClient.from_env(self.instance)\n records = client.list(model, cols=list(cols), page=page, per_page=per_page)\n\n sys.stdout.write(str(json.dumps(records)))\n\n def create(self, model, threads=DEFAULT_THREAD_COUNT):\n \"\"\"Creates each record provided in the list.\n\n :model: The Fluxx Model Object you wish to create.\n :returns: None\n\n \"\"\"\n\n with write_operation(self.instance, model, threads) as records:\n for record in records:\n record['method'] = 'CREATE'\n\n def update(self, model, threads=DEFAULT_THREAD_COUNT):\n \"\"\"Updates each record provided in the list.\n Each record must have an id.\n\n :model: The Fluxx Model Object you wish to update.\n :returns: None\n\n \"\"\"\n\n with write_operation(self.instance, model, threads) as records:\n for i, record in enumerate(records):\n record['method'] = 'UPDATE'\n\n def delete(self, model, threads=DEFAULT_THREAD_COUNT):\n \"\"\"Deletes each record provided in the list.\n Each record must have an id.\n\n :model: The Fluxx Model Object you wish to update.\n :returns: None\n\n \"\"\"\n\n with write_operation(self.instance, model, threads) as records:\n for i, record in enumerate(records):\n record['method'] = 'DELETE'\n\n def upsert(self, model, threads=DEFAULT_THREAD_COUNT):\n \"\"\"Creates or updates a each record provided in the list.\n The non-null status of the 'id' attribute of every record determines\n whether it will be created or updated, with None value IDs defaulting\n to creation.\n\n :model: The Fluxx ModelObject you wish to create.\n :returns: None\n\n \"\"\"\n\n with write_operation(self.instance, model, threads) as records:\n for i, record in enumerate(records):\n if 'id' in record:\n record['method'] = 'UPDATE'\n else:\n record['method'] = 'CREATE'\n\n\ndef main():\n fire.Fire(FluxxCLI)\n","sub_path":"fluxx/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"126547161","text":"from typing import List, Tuple, Any\nfrom contextlib import suppress\nfrom nboost.types import Request, Response, Choice\nfrom nboost.helpers import load_json, dump_json\nfrom nboost.exceptions import MissingQuery\nfrom nboost.codex.base import BaseCodex\n\n\nclass ESCodex(BaseCodex):\n \"\"\"Elasticsearch Codex\"\"\"\n SEARCH_PATH = '/.*/_search'\n\n def parse_query(self, request: Request) -> Tuple[Any, bytes]:\n \"\"\"try to get query from query params, then body\"\"\"\n body = load_json(request.body)\n\n # search for query in body\n if body:\n with suppress(KeyError):\n field, query = body['query']['match'].popitem()\n if isinstance(query, dict):\n query = query['query']\n return field, query.encode()\n\n # search for query in url\n with suppress(KeyError):\n field, *query = request.url.query['q'].split(':')\n return field, ':'.join(query).encode()\n\n raise MissingQuery\n\n def multiply_request(self, request: Request) -> Tuple[int, List[str]]:\n \"\"\"Multiply size of Elasticsearch query\"\"\"\n body = load_json(request.body)\n\n topk = request.url.query.pop('size', None)\n correct_cids = request.url.query.pop('nboost', None)\n\n # search for topk in body\n if body:\n correct_cids = body.pop('nboost', correct_cids)\n topk = body.pop('size', topk)\n\n topk = 10 if topk is None else int(topk)\n\n if body:\n body['size'] = topk * self.multiplier\n request.body = dump_json(body)\n else:\n request.url.query['size'] = str(topk * self.multiplier)\n\n correct_cids = correct_cids.split(',') if correct_cids else None\n return topk, correct_cids\n\n def parse_choices(self, response: Response, field: str) -> List[Choice]:\n \"\"\"Parse out Elasticsearch hits\"\"\"\n body = load_json(response.body)\n hits = body.get('hits', {'hits': []})['hits']\n return [Choice(\n hit['_id'], # cid\n hit['_source'][field].encode() # body\n ) for hit in hits]\n\n def reorder_response(self, request, response, ranks):\n \"\"\"Reorder Elasticsearch hits\"\"\"\n body = load_json(response.body)\n body['_nboost'] = '⚡NBOOST'\n\n body['hits']['hits'] = [body['hits']['hits'][rank] for rank in ranks]\n\n jkwargs = {'ensure_ascii': False}\n if 'pretty' in request.url.query:\n jkwargs.update({'indent': 2})\n\n response.body = dump_json(body, **jkwargs)\n","sub_path":"nboost/codex/es.py","file_name":"es.py","file_ext":"py","file_size_in_byte":2569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"639817213","text":"# ---------------------------------------------------------------------\n# Cisco.IOS.get_ipv6_neighbor\n# ---------------------------------------------------------------------\n# Copyright (C) 2007-2012 The NOC Project\n# See LICENSE for details\n# ---------------------------------------------------------------------\n\n# Python modules\nimport re\n\n# NOC modules\nfrom noc.core.script.base import BaseScript\nfrom noc.sa.interfaces.igetipv6neighbor import IGetIPv6Neighbor\n\n\nclass Script(BaseScript):\n name = \"Cisco.IOS.get_ipv6_neighbor\"\n interface = IGetIPv6Neighbor\n\n rx_line = re.compile(\n r\"^(?P[0-9a-fA-F:\\.]+)\\s+\"\n r\"\\d+\\s+\"\n r\"(?P[0-9a-f]{4}\\.[0-9a-f]{4}\\.[0-9a-f]{4})\\s+\"\n r\"(?P\\S+)\\s+\"\n r\"(?P\\S+)\\s*$\"\n )\n\n s_map = {\n \"INCMP\": \"incomplete\",\n \"REACH\": \"reachable\",\n \"STALE\": \"stale\",\n \"DELAY\": \"delay\",\n \"PROBE\": \"probe\",\n }\n\n def execute_cli(self, vrf=None, **kwargs):\n # Get states\n cmd = \"show ipv6 neighbor\"\n r = self.cli(cmd, list_re=self.rx_line)\n # Remap states\n for n in r:\n n[\"state\"] = self.s_map[n[\"state\"]]\n return r\n","sub_path":"sa/profiles/Cisco/IOS/get_ipv6_neighbor.py","file_name":"get_ipv6_neighbor.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"604928313","text":"#\n# Copyright (c) 2016 Intel Corporation\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\nfrom retry import retry\n\nfrom modules.constants import TapComponent as TAP\nfrom modules.markers import components\nfrom modules.tap_logger import step\nfrom modules.tap_object_model import PlatformSnapshot\n\nlogged_components = (TAP.platform_snapshot,)\npytestmark = [components.platform_snapshot]\n\n\nclass TestSnapshot:\n\n @retry(AssertionError, tries=10, delay=3)\n def _get_new_snapshot(self, snapshots_before):\n step(\"Get new snapshot after triggering\")\n snapshots_after = PlatformSnapshot.api_get_snapshots()\n assert len(snapshots_after) > len(snapshots_before)\n return snapshots_after[0]\n\n def test_compare_snapshot_and_version(self):\n step(\"Get snapshots\")\n snapshots = PlatformSnapshot.api_get_snapshots()\n step(\"Get version\")\n version = PlatformSnapshot.api_get_version()\n assert snapshots[0] == version\n\n def test_trigger_snapshot(self):\n step(\"Get snapshots\")\n snapshots_before = PlatformSnapshot.api_get_snapshots()\n step(\"Get version\")\n version_before = PlatformSnapshot.api_get_version()\n step(\"Trigger new snapshot\")\n PlatformSnapshot.api_trigger_snapshots()\n new_snapshot = self._get_new_snapshot(snapshots_before=snapshots_before)\n step(\"Get new versions\")\n version_after = PlatformSnapshot.api_get_version()\n assert version_before != version_after\n assert new_snapshot == version_after\n","sub_path":"project/tests/test_functional/platform_snapshot/test_platform_snapshot.py","file_name":"test_platform_snapshot.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"527226575","text":"from flask import Blueprint\nfrom flask import render_template\nfrom models import User, Text\nfrom Txt.forms import EditTextForm\nfrom flask import request\nfrom app import db\n\ntxt = Blueprint('Txt', __name__, template_folder='templates', static_folder='static')\n\n@txt.route('/User-')\ndef txt_main(email):\n user = User.query.filter(User.email==email).first()\n print(user) \n texts = user.texts\n return render_template('texts.html', texts = texts)\n\n@txt.route('/Text-', methods = ['POST', 'GET'])\ndef text(id):\n t_object = Text.query.filter(Text.id==id).first()\n if request.method == 'POST':\n form = EditTextForm(formdata = request.form, obj = t_object)\n form.populate_obj(t_object)\n db.session.commit()\n form = EditTextForm(obj=t_object)\n name = form.name\n text = form.text\n return render_template('edit_text.html', form = form, t_object = t_object, text = text, name = name)","sub_path":"Txt/blueprint.py","file_name":"blueprint.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"572235918","text":"import requests\r\nimport urllib.parse\r\nimport re\r\n\r\nrequestedResults = 0\r\n\r\ndef bingSearch(query):\r\n headers = {\r\n \"Host\":\"www.bing.com\",\r\n \"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36\",\r\n \"Accept\":\"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\"\r\n \r\n }\r\n r = requests.get(\"https://www.bing.com/search?q=\" + urllib.parse.quote(f\"{query}\"), headers=headers, stream=True)\r\n return str(r.content)\r\n\r\ndef findQuizletMatches(webResponse):\r\n matches = re.findall(r\"

  • = len(matches):\r\n for i in matches:\r\n print(i)\r\n else:\r\n i = 0\r\n while i < requestedResults:\r\n print(matches[i])\r\n i = i + 1\r\n\r\n\r\ndef start():\r\n try:\r\n global requestedResults\r\n searchQuery = input(\"Search Query: \")\r\n requestedResults = int(input(\"Reqested Results: \"))\r\n findQuizletMatches(bingSearch(searchQuery))\r\n except:\r\n start()\r\n\r\nstart()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"452423259","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.linspace(0, 2*np.pi)\noffsets = np.linspace(0, 2*np.pi, 4, endpoint=False)\n#print(offsets)\n\nyy = np.transpose([np.sin(x+phi) for phi in offsets])\n#print(yy)\n\nplt.rc('lines', linewidth=4)\nfig, (ax0, ax1) = plt.subplots(nrows=2)\nfig = plt.figure(figsize=(12, 10))\nplt.rc('axes', color_cycle=['r', 'g', 'b', 'y'])\nax0.plot(yy)\nax0.set_title('Set default color cycle to rgby')\n\nax1.set_color_cycle(['c','m', 'y', 'k'])\nax1.plot(yy)\nax1.set_title('Set axes color cycle to cmyk')\nplt.subplots_adjust(hspace=3, wspace=2)\nplt.show()","sub_path":"matplotlib/sin_cos.py","file_name":"sin_cos.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"248031547","text":"\"\"\"\nA queue is a data structure whose primary purpose is to store and\nreturn elements in First In First Out order. \n\n1. Implement the Queue class using an array as the underlying storage structure.\n Make sure the Queue tests pass.\n2. Re-implement the Queue class, this time using the linked list implementation\n as the underlying storage structure.\n Make sure the Queue tests pass.\n3. What is the difference between using an array vs. a linked list when \n implementing a Queue?\nStretch: What if you could only use instances of your Stack class to implement the Queue?\n What would that look like? How many Stacks would you need? Try it!\n\"\"\"\n\nclass Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n\nclass Queue:\n def __init__(self):\n # Array Implementation\n # self.storage = []\n\n \n self.size = 0\n self.head = None\n self.tail = None\n \n def __len__(self):\n # Array Implementation\n # return len(self.storage)\n # Linked List Implementation\n return self.size\n\n def enqueue(self, value):\n \n # self.storage.append(value)\n new_node = Node(value)\n # If the list is empty, set the head node and its .next value to be the value\n if self.head is None:\n self.head = new_node\n self.head.next = new_node\n self.tail = new_node\n # Otherwise, if there are nodes, change the current tail's .next value from None to the value\n else:\n self.tail.next = new_node\n self.tail = new_node\n self.size += 1\n\n def dequeue(self):\n \n # if len(self.storage) == 0:\n # return None\n # else:\n # removed = self.storage.pop(0)\n # return removed\n \n if self.head is None:\n return None\n elif self.head.next is None:\n removed = self.head\n self.head = None\n self.size = 0\n return removed.data\n else:\n removed = self.head\n self.head = self.head.next\n self.size -= 1\n return removed.data","sub_path":"queue/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"55863040","text":"import numpy as np\n\ndef arnoldi_iteration(A, b, n):\n\n \"\"\"\n\n Computes a basis of the (n+1)-Krylov subspace of A: the space\n\n spanned by {b, Ab, ..., A^n b}.\n\n\n Input\n\n A: mxm array\n\n b: initial vector (length m)\n\n n: dimension of Krylov subspace, must be >=1\n\n \n\n Returns Q, h\n\n Q: mx(n+1) array, the columns are an orthonormal basis of the\n\n Krylov subspace.\n\n h: (n+1)xn array, A on basis Q. It is upper Hessenberg. \n\n \"\"\"\n\n m = A.shape[0] \n\n\n h = np.zeros((n+1, n)) \n\n Q = np.zeros((m, n+1)) \n\n\n q = b/np.linalg.norm(b) # Normalize the input vector\n\n Q[:, 0] = q # Use it as the first Krylov vector\n\n \n\n for k in range(n): \n\n v = A.dot(q) # Generate a new candidate vector \n\n for j in range(k+1): # Subtract the projections on previous vectors\n\n h[j, k] = np.dot(Q[:, j].conj(),v)\n\n v = v - h[j, k]*Q[:, j] \n\n\n h[k+1, k] = np.linalg.norm(v)\n\n eps = 1e-12 # If v is shorter than this threshold it is the zero vector\n\n if h[k+1,k] > eps: # Add the produced vector to the list, unless\n\n q = v / h[k+1, k] # the zero vector is produced.\n\n Q[:, k+1] = q\n\n else: # If that happens, stop iterating.\n\n return Q,h\n\n return Q,h","sub_path":"optimaztion/arnoldi.py","file_name":"arnoldi.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"578642480","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/12/17 0017 14:19\n# @Site : \n# @File : currencyApi.py\n# @Software: PyCharm\nfrom flask import jsonify, json, request\nfrom flask_jwt_extended import jwt_required\n\nfrom common.FormatStr import dictRemoveNone\nfrom common.OperationOfDB import findById\nfrom common.ReturnMessage import returnMsg, returnErrorMsg, errorCode\nfrom models.Boss.OrderAidance import OrderAidance\nfrom models.Data.Aidance import Aidance\nfrom models.Data.Item import Item\nfrom models.Member.MemberBases import MemberBases\nfrom models.Order.OrderService import OrderService\nfrom models.Order.UserOrder import UserOrder\nfrom version.v3.bossConfig import app\nfrom common.getAreaCode import getAreaCode\n\n\n@app.route(\"/getOrderInfo\", methods=[\"POST\"])\n@jwt_required\ndef getOrderInfo():\n jsonData = request.get_data()\n dataDict = json.loads(jsonData)\n orderNo = dataDict.get(\"orderNo\", \"\")\n if not orderNo:\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n orderInfo = findById(UserOrder, \"order_no\", orderNo, isStrcheck=True)\n if orderInfo:\n _orderDict = {\n \"orderId\": orderInfo.order_id,\n \"orderNo\": orderInfo.order_no,\n \"orderType\": orderInfo.order_type,\n \"orderFrom\": orderInfo.order_from,\n \"orderStatus\": orderInfo.order_status,\n \"declareStatus\": orderInfo.declare_status,\n \"orderAddIp\": orderInfo.order_add_ip,\n \"orderAddTime\": orderInfo.order_add_time,\n \"serviceId\": orderInfo.service_id,\n \"contactPerson\": orderInfo.contact_person,\n \"contactPhone\": orderInfo.contact_phone,\n \"contactEmail\": orderInfo.contact_email,\n \"orderMessage\": orderInfo.order_message,\n \"orderAmount\": str(orderInfo.order_amount),\n \"payableAmount\": str(orderInfo.payable_amount),\n \"realAmount\": str(orderInfo.real_amount),\n \"orderPoint\": orderInfo.order_point,\n \"isCoupon\": orderInfo.is_coupon,\n \"isInvoice\": orderInfo.is_invoice,\n \"isComment\": orderInfo.is_comment,\n \"userId\": orderInfo.user_id,\n }\n _orderDict = dictRemoveNone(_orderDict)\n resultDict = returnMsg(_orderDict)\n else:\n resultDict = returnErrorMsg(errorCode[\"query_fail\"])\n return jsonify(resultDict)\n\n\n@app.route(\"/getItemInfo\", methods=[\"POST\"])\n@jwt_required\ndef getItemInfo():\n jsonData = request.get_data()\n dataDict = json.loads(jsonData)\n itemId = dataDict.get(\"itemId\", \"\")\n orderNo = dataDict.get(\"orderNo\", \"\")\n if not (itemId or orderNo):\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n if itemId:\n itemInfo = findById(Item, 'item_id', \"itemId\")\n else:\n serviceInfo = findById(OrderService, \"order_no\", orderNo, isStrcheck=True)\n if serviceInfo:\n itemInfo = findById(Item, \"item_id\", serviceInfo.item_id, isStrcheck=True)\n else:\n itemInfo = None\n if itemInfo:\n _orderDict = {\n \"itemId\": itemInfo.item_id,\n \"deptName\": itemInfo.dept_name,\n \"levelCode\": itemInfo.level_code,\n \"categoryName\": itemInfo.category_name,\n \"areaCode\": itemInfo.area_code,\n \"itemUrl\": itemInfo.item_url,\n \"itemTitle\": itemInfo.item_title,\n \"itemImgurl\": itemInfo.item_imgurl,\n \"itemPulishdate\": itemInfo.item_pulishdate,\n \"itemType\": itemInfo.item_type,\n \"itemSort\": itemInfo.item_sort,\n \"isTop\": itemInfo.is_top,\n \"isLock\": itemInfo.is_lock,\n \"isService\": itemInfo.is_service,\n \"isContentJson\": itemInfo.is_content_json,\n # \"createTime\": itemInfo.create_time,\n \"isClose\": itemInfo.is_close,\n \"itemDeadline\": itemInfo.item_deadline,\n \"isSecular\": itemInfo.is_secular,\n \"mediaType\": itemInfo.media_type,\n \"mediaUrl\": itemInfo.media_url\n }\n _orderDict = dictRemoveNone(_orderDict)\n resultDict = returnMsg(_orderDict)\n else:\n resultDict = returnErrorMsg(errorCode[\"query_fail\"])\n return jsonify(resultDict)\n\n\n@app.route(\"/getOrderServiceInfo\", methods=[\"POST\"])\n@jwt_required\ndef getOrderServiceInfo():\n jsonData = request.get_data()\n dataDict = json.loads(jsonData)\n id = dataDict.get(\"id\", \"\")\n orderNo = dataDict.get(\"orderNo\", \"\")\n if not (id or orderNo):\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n if id:\n serviceInfo = findById(OrderService, \"id\", id)\n else:\n serviceInfo = findById(OrderService, \"order_no\", orderNo, isStrcheck=True)\n if serviceInfo:\n _serviceDict = {\n \"id\": serviceInfo.id,\n # \"orderNo\": serviceInfo.order_no,\n # \"itemId\": serviceInfo.item_id,\n \"serviceName\": serviceInfo.service_name,\n \"policySource\": serviceInfo.policy_source,\n \"servicePrice\": str(serviceInfo.service_price),\n \"serviceStarttime\": serviceInfo.service_starttime,\n \"serviceDeadline\": serviceInfo.service_deadline,\n \"directionName\": serviceInfo.direction_name,\n \"serviceContent\": serviceInfo.service_content,\n \"sheetContent\": serviceInfo.sheet_content,\n \"materialList\": serviceInfo.material_list,\n \"forecastPath\": serviceInfo.forecast_path,\n \"serviceContactPerson\": serviceInfo.service_contact_person,\n \"serviceContactPhone\": serviceInfo.service_contact_phone,\n # \"isSecular\": serviceInfo.is_secular,\n \"isEvaluate\": serviceInfo.is_evaluate,\n \"declareList\": serviceInfo.declare_list,\n \"createTime\": serviceInfo.create_time,\n \"categoryType\": serviceInfo.category_type,\n \"servciceProcess\": serviceInfo.servcice_process,\n }\n _serviceDict = dictRemoveNone(_serviceDict)\n resultDict = returnMsg(_serviceDict)\n else:\n resultDict = returnErrorMsg(errorCode[\"query_fail\"])\n return jsonify(resultDict)\n\n\n@app.route(\"/getMemberInfo\", methods=[\"POST\"])\n@jwt_required\ndef getMemberInfo():\n jsonData = request.get_data()\n dataDict = json.loads(jsonData)\n orderNo = dataDict.get(\"orderNo\", \"\")\n userId = dataDict.get(\"userId\", \"\")\n if not (userId or orderNo):\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n if userId:\n userInfo = findById(MemberBases, \"user_id\", userId)\n else:\n orderInfo = findById(UserOrder, \"order_no\", orderNo, isStrcheck=True)\n if orderInfo:\n userInfo = findById(MemberBases, \"user_id\", orderInfo.user_id)\n else:\n userInfo = None\n if userInfo:\n areaName = \"\"\n if userInfo.area_code:\n provinceName, cityName, districtName = getAreaCode(userInfo.area_code)\n areaName = provinceName + cityName + districtName\n _memberInfo = {\n \"userId\": userInfo.user_id,\n \"memberName\": userInfo.member_name,\n \"memberType\": userInfo.member_type,\n \"memberContactEmail\": userInfo.member_contact_email,\n \"memberContactPerson\": userInfo.member_contact_person,\n \"memberContactPhone\": userInfo.member_contact_phone,\n \"areaCode\": userInfo.area_code,\n \"areaName\": areaName,\n \"memberCreditCode\": userInfo.member_credit_code,\n }\n _memberInfo = dictRemoveNone(_memberInfo)\n resultDict = returnMsg(_memberInfo)\n else:\n resultDict = returnErrorMsg(errorCode[\"query_fail\"])\n return jsonify(resultDict)\n\n\n# 订单项目 结合\n@app.route(\"/getOrderItemInfo\", methods=[\"POST\"])\n@jwt_required\ndef getOrderItemInfo():\n jsonData = request.get_data()\n dataDict = json.loads(jsonData)\n orderNo = dataDict.get(\"orderNo\", \"\")\n if not orderNo:\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n orderInfo = findById(UserOrder, \"order_no\", orderNo, isStrcheck=True)\n if not orderInfo:\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n serviceInfo = findById(OrderService, \"order_no\", orderNo, isStrcheck=True)\n if serviceInfo:\n itemInfo = findById(Item, \"item_id\", serviceInfo.item_id, isStrcheck=True)\n else:\n resultDict = returnErrorMsg(errorCode[\"param_error\"])\n return jsonify(resultDict)\n _itemDict = {}\n _orderDict = {\n \"orderId\": orderInfo.order_id,\n \"orderNo\": orderInfo.order_no,\n \"orderType\": orderInfo.order_type,\n \"orderFrom\": orderInfo.order_from,\n \"orderStatus\": orderInfo.order_status,\n \"declareStatus\": orderInfo.declare_status,\n \"orderAddIp\": orderInfo.order_add_ip,\n \"orderAddTime\": orderInfo.order_add_time,\n \"serviceId\": orderInfo.service_id,\n \"contactPerson\": orderInfo.contact_person,\n \"contactPhone\": orderInfo.contact_phone,\n \"contactEmail\": orderInfo.contact_email,\n \"orderMessage\": orderInfo.order_message,\n \"orderAmount\": str(orderInfo.order_amount),\n \"payableAmount\": str(orderInfo.payable_amount),\n \"realAmount\": str(orderInfo.real_amount),\n \"orderPoint\": orderInfo.order_point,\n \"isCoupon\": orderInfo.is_coupon,\n \"isInvoice\": orderInfo.is_invoice,\n \"isComment\": orderInfo.is_comment,\n \"userId\": orderInfo.user_id,\n }\n if itemInfo:\n _itemDict = {\n \"itemId\": itemInfo.item_id,\n \"deptName\": itemInfo.dept_name,\n \"levelCode\": itemInfo.level_code,\n \"categoryName\": itemInfo.category_name,\n \"areaCode\": itemInfo.area_code,\n \"itemUrl\": itemInfo.item_url,\n \"itemTitle\": itemInfo.item_title,\n \"itemImgurl\": itemInfo.item_imgurl,\n \"itemPulishdate\": itemInfo.item_pulishdate,\n \"itemType\": itemInfo.item_type,\n \"itemSort\": itemInfo.item_sort,\n \"isTop\": itemInfo.is_top,\n \"isLock\": itemInfo.is_lock,\n \"isService\": itemInfo.is_service,\n \"isContentJson\": itemInfo.is_content_json,\n # \"createTime\": itemInfo.create_time,\n \"isClose\": itemInfo.is_close,\n \"itemDeadline\": itemInfo.item_deadline,\n \"isSecular\": itemInfo.is_secular,\n \"mediaType\": itemInfo.media_type,\n \"mediaUrl\": itemInfo.media_url\n }\n infoDict = dict(_orderDict, **_itemDict)\n infoDict = dictRemoveNone(infoDict)\n resultDict = returnMsg(infoDict)\n return jsonify(resultDict)\n","sub_path":"boss_service/controllers/currencyApi.py","file_name":"currencyApi.py","file_ext":"py","file_size_in_byte":10824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"165804305","text":"def get(setn,no):\n print('Enter the elements in set {}'.format(no))\n setn.update([int(e) for e in input().split()])\n\ndef check(set1,set2,function):\n set3 = set()\n func = function.replace('x','{}')\n func = func.replace('^','**')\n for e in set1:\n set3.add(int(eval(func.format(e))))\n if(len(set1) == len(set3) and set3.issubset(set2)):\n print('It is one-to-one function')\n else:\n print('It is not one-to-one function')\n\nset1 = set()\nset2 = set()\nget(set1,1)\nget(set2,2)\nfunction = input('Enter the function use \\'x\\' as placeholder:')\ncheck(set1,set2,function)\n","sub_path":"December-09/d9.py","file_name":"d9.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"252341264","text":"import threading\nimport time\n\n\nclass ServerThread(threading.Thread):\n def __init__(self, threadID, name, counter, *args):\n threading.Thread.__init__(self)\n self.threadID = threadID\n self.name = name\n self.counter = counter\n self.args = args\n\n def run(self):\n print(\"printing args\")\n print(self.args)\n client(self, self.args[0], self.args[1])\n return\n\n\ndef client(self, client_socket, addr):\n print(\"Client thread started\")\n run = True\n while run:\n data = client_socket.recv(1024)\n if not data:\n run = False\n break\n client_socket.send(data)\n client_socket.close()\n\n","sub_path":"Threaded/Server/Server_Thread.py","file_name":"Server_Thread.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"67841649","text":"import pygame, sys #imports the pygame and sys modules\nfrom pygame.locals import * #pygame.locals has the constants like QUIT, MOUSEMOTION, AND K_ESCAPE.\n\npygame.init()#This must be called before any other pygame code.\nfpsClock = pygame.time.Clock() #The Clock object makes sure our program runs (at most) at a certain FPS.\n\nscreen = pygame.display.set_mode((640,480)) #set_mode() creates the window. Param is (width, height) in pixels. The Surface object returned is drawn to the screen when pygame.display.update() is called.\npygame.display.set_caption('Pygame helper caption')\n\nred = pygame.Color(255,0,0)\ngreen = pygame.Color(0,255,0) #Params to the Color objects are for Red, Green, Blue. 0 is none. 255 is max\nblue = pygame.Color(0,0,255)\nwhite = pygame.Color(255,255,255)\nblack = pygame.Color(0,0,0)\nmousex, mousey = 0,0\n\nfontObj = pygame.font.Font('freesansbold.ttf', 24) #Create a Font object with a font of size 32 points\nmsg = ' '\n\nclass fish():\n def __init__(self, x, y, vx, vy, ax, ay, size, red, green, blue):\n self.x = x\n self.y = y\n self.vx = vx\n self.vy = vy\n self.ax = ax\n self.ay = ay\n\n self.size = size\n self.r = red\n self.g = green\n self.b = blue\n self.srf = pygame.Surface((int(2*self.size),int(2*self.size)))\n \n def create_picture(self):\n self.img = pygame.draw.circle(self.srf,\n (self.r, self.g, self.b),\n (int(self.size), int(self.size)),\n self.size)\n\nfishes = []\ntest1 = fish(50,50,0,0,0,0,20,250,0,0)\nfishes.append(test1)\nfor fish in fishes:\n fish.create_picture()\n \nwhile True:\n screen.fill(black) #This will fill the Surface object with black\n\n msgBox = fontObj.render(msg, False, green) #render() creates a surface object with the text drawn on it in the specified font and color. You can blit() this Surface object to the window's Surface object.\n screen.blit(msgBox, (0,0))\n \n for fish in fishes:\n screen.blit(fish.srf, (fish.x-fish.size, fish.y-fish.size))\n \n for event in pygame.event.get(): #pygame.event.get() returns a list of all Event objects that happened since the last time get() was called\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == MOUSEMOTION: #The event object has type, pos, key and other attributes depending on the type of event it is.\n mousex, mousey = event.pos\n temp = ('X: %.3f, Y: %.3f' %(mousex, mousey))\n msg = temp\n elif event.type == MOUSEBUTTONUP:\n mousex, mousey = event.pos\n if event.button == 1:\n msg = 'Left mouse click'\n elif event.button == 2:\n msg = 'Middle mouse click'\n elif event.button == 3:\n msg = 'Right mouse click'\n elif event.button == 4:\n msg = 'Mouse scrolled up.'\n elif event.button == 5:\n msg = 'Mouse scrolled down.'\n elif event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n pygame.event.post(pygame.event.Event(QUIT))\n\n pygame.display.update() #The window is not drawn to the actual screen until pygame.display.update is called.\n fpsClock.tick(30) #wait long enough to run at 30 frames per second. (Call this after pygame.display.update().)\n \n\n \n \n","sub_path":"Dell precision backup/fish.py","file_name":"fish.py","file_ext":"py","file_size_in_byte":3480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"93006258","text":"import random, time\r\n\r\nfrom Term import *\r\nfrom Clause import *\r\n\r\n\r\ndef randomAssignment(assignments):\r\n ''' Randomly assign True or False to each element in the list\r\n assignments. '''\r\n for i in range(len(assignments)):\r\n if random.randint(1,2) == 1:\r\n assignments[i] = False\r\n else:\r\n assignments[i] = True\r\n\r\ndef hillClimb(clauses, guess):\r\n ''' Finds a new \"guess\" that is better than the input \"guess\". Each possible\r\n \"new guess\" is made by flipping one variable in \"guess\" from True to False (or\r\n vice versa).\r\n Returns False if it can't find a better guess. '''\r\n\r\n # assume the minimum is the current guess\r\n minValue = [(Clause.countTrue(clauses, guess), guess)]\r\n\r\n # if the current guess solves the problem, don't try any others\r\n if Clause.countTrue(clauses, guess) == len(clauses):\r\n return guess\r\n\r\n foundBetter = False\r\n for i in range(0, len(guess)):\r\n newGuess = guess.copy()\r\n\r\n # the next statement flips the True/False value at index i\r\n newGuess[i] = True if guess[i] == False else False\r\n count = Clause.countTrue(clauses, newGuess) # see how good the new guess is\r\n\r\n if count == len(clauses): # perfect. No need to try anything else.\r\n return newGuess\r\n\r\n if count > minValue[0][0]: # better than the best so far\r\n minValue = [(count, newGuess)]\r\n foundBetter = True\r\n elif count == minValue[0][0] and foundBetter: # equal to the best so far\r\n minValue.append((count, newGuess))\r\n\r\n if not foundBetter:\r\n return False\r\n\r\n # pick one at random\r\n index = random.randint(0, len(minValue) - 1)\r\n return minValue[index][1]\r\n\r\ndef genetic(clauses,guess):\r\n pop = clauses\r\n genCount = 1\r\n generate = True\r\n while generate:\r\n newPop = []\r\n children = []\r\n newPop = sorted(pop, key = lambda x: Clause.countTrue(pop,guess))\r\n newPop = newPop[50:]\r\n for i in range(len(newPop)//2):\r\n if i == len(newPop)//2 - 1:\r\n c1t1 = newPop[i].terms[0]\r\n c2t1 = newPop[0].terms[0]\r\n c1t3 = newPop[0].terms[2]\r\n c2t3 = newPop[i].terms[2]\r\n if random.randint(1, 2) == 1:\r\n c1t2 = newPop[i].terms[1]\r\n else:\r\n c1t2 = newPop[0].terms[1]\r\n if random.randint(1, 2) == 1:\r\n c2t2 = newPop[i].terms[1]\r\n else:\r\n c2t2 = newPop[0].terms[1]\r\n else:\r\n c1t1 = newPop[i].terms[0]\r\n c2t1 = newPop[i+1].terms[0]\r\n c1t3 = newPop[i+1].terms[2]\r\n c2t3 = newPop[i].terms[2]\r\n if random.randint(1,2) == 1:\r\n c1t2 = newPop[i].terms[1]\r\n else:\r\n c1t2 = newPop[i+1].terms[1]\r\n if random.randint(1,2) == 1:\r\n c2t2 = newPop[i].terms[1]\r\n else:\r\n c2t2 = newPop[i+1].terms[1]\r\n c1 = Clause(c1t1,c1t2,c1t3)\r\n c2 = Clause(c2t1,c2t2,c2t3)\r\n children.append(c1)\r\n children.append(c2)\r\n # for i in range(len(children)):\r\n # if random.randint(1,2) == 1:\r\n # children[i] = children[i].terms.reverse()\r\n newPop = newPop + children\r\n genCount += 1\r\n newPop = sorted(newPop,key= lambda x: Clause.countTrue(newPop, guess))\r\n newPop = newPop[:99]\r\n for i in range(100):\r\n if Clause.allClausesTrue(newPop,guess) == True:\r\n generate = False\r\n return newPop,genCount\r\n pop = newPop\r\n genCount += 1\r\n\r\n\r\n\r\n# let's play\r\n#n = int(input('How many variables? '))\r\nprint(\"n\\taverage generation count\")\r\nfor n in range(10,100,5):\r\n assignments = n*[True]\r\n randomAssignment(assignments)\r\n #print('Random assignments: ', assignments)\r\n\r\n m = 100 #int(input('How many random clauses?'))\r\n allClauses = []\r\n for i in range(m):\r\n allClauses.append(Clause.randomClauseThatIsSatisfiable(n, assignments))\r\n\r\n totalgens = 0\r\n for i in range(10):\r\n randomGuess = [True if random.randint(1, 2) == 1 else False for _ in range(n)]\r\n\r\n answer, gens = genetic(allClauses,randomGuess)\r\n totalgens += gens\r\n print(n, \"\\t\", totalgens/10)\r\n\r\n","sub_path":"HW7Part3-Miranda Stevens Brunk/3SAT_Gen_part1.py","file_name":"3SAT_Gen_part1.py","file_ext":"py","file_size_in_byte":4460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"257594175","text":"from django.conf.urls import url, include\r\nfrom rest_framework.urlpatterns import format_suffix_patterns\r\n\r\nfrom . import views\r\n\r\napp_name = 'myapp'\r\nurlpatterns = [\r\n url(r'^$', views.IndexView.as_view(), name='index'),\r\n url(r'^create/$', views.CreateAd, name = 'create'),\r\n url(r'^view/$', views.ViewAd.as_view(), name = 'view'),\r\n url(r'^api/all/$', views.AdvertisementsList.as_view(), name = 'api'),\r\n # url(r'^api/(?P[a-zA-Z]+)+(?P[a-zA-Z]+(?:[\\s-][a-zA-Z]+)*)+(?P[0-9]+)+(?P
    [a-zA-Z]+(?:[\\s-][a-zA-Z]+)*)+(?P[a-zA-Z]+)'\r\n url(r'^api/(?P[a-zA-Z]+(?:[\\s-][a-zA-Z]+)*)/$', views.CityList.as_view()),\r\n\r\n\r\n]\r\n\r\nurlpatterns = format_suffix_patterns(urlpatterns)\r\n","sub_path":"mysite/myapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"205090415","text":"from rest_framework import serializers\nfrom .models import Persona\nfrom .models import Negocio\nfrom .models import Publicidad\nfrom .models import EncuestaPregunta\nfrom .models import EncuestaRespuesta\nfrom .models import CategoriaNegocios\nfrom .models import Galeria\nfrom .models import Cheking\nfrom servicios.serializers import PlanSerializer\nfrom servicios.serializers import CategoriaSerializer\nfrom servicios.serializers import SubcategoriaSerializer\nfrom usuarios.serializers import UsuarioSerializer\n\nclass PersonaSerializer(serializers.HyperlinkedModelSerializer):\n\tnombre = serializers.CharField(max_length=50)\n\tsexo = serializers.CharField(max_length=1)\n\tcorreo = serializers.CharField(max_length=50)\n\tclass Meta():\n\t\tmodel = Persona\n\t\tfields = ('nombre','sexo','correo' ,)\n\nclass NegocioSerializer(serializers.HyperlinkedModelSerializer):\n\tidUsuario = PersonaSerializer()\n\tidPlan = PlanSerializer()\n\tnombre = serializers.CharField(max_length=50)\n\tdescripcion = serializers.CharField(max_length=50)\n\temocion = serializers.CharField(max_length=50)\n\tnum_tel = serializers.CharField(max_length=50)\n\tfoto = serializers.CharField(max_length=500)\n\tclass Meta():\n\t\tmodel = Negocio\n\t\tfields = ('idUsuario','idPlan','nombre','descripcion','emocion','num_tel','foto' ,)\n\nclass PublicidadSerializer(serializers.HyperlinkedModelSerializer):\n\tidNegocio = NegocioSerializer()\n\tbanner = serializers.CharField(max_length=500)\n\tclass Meta():\n\t\tmodel = Publicidad\n\t\tfields = ('idNegocio','banner' ,)\n\nclass EncuestaPreguntaSerializer(serializers.HyperlinkedModelSerializer):\n\tidNegocio = NegocioSerializer()\n\ttipo = serializers.IntegerField()\n\tpregunta = serializers.CharField(max_length=500)\n\tclass Meta():\n\t\tmodel = EncuestaPregunta\n\t\tfields = ('idNegocio','tipo','pregunta' ,)\n\nclass EncuestaRespuestaSerializer(serializers.HyperlinkedModelSerializer):\n\tidEncuestaPregunta = EncuestaPreguntaSerializer()\n\tidUsuario = PersonaSerializer()\n\tclass Meta():\n\t\tmodel = EncuestaRespuesta\n\t\tfields = ('idEncuestaPregunta','idUsuario' ,)\n\nclass CategoriaNegociosSerializer(serializers.HyperlinkedModelSerializer):\n\tidSubcategoria = SubcategoriaSerializer()\n\tidNegocio = NegocioSerializer()\n\tclass Meta():\n\t\tmodel = CategoriaNegocios\n\t\tfields = ('idSubcategoria','idNegocio' ,)\t\n\nclass GaleriaSerializer(serializers.HyperlinkedModelSerializer):\n\tidNegocio = NegocioSerializer()\n\tfoto = serializers.CharField(max_length=500)\n\tclass Meta():\n\t\tmodel = Galeria\n\t\tfields =('idNegocio','foto' ,)\n\nclass ChekingSerializer(serializers.HyperlinkedModelSerializer):\n\tidNegocio = NegocioSerializer()\n\tidUsuario = UsuarioSerializer()\n\tcalificacion = serializers.IntegerField()\n\tclass Meta():\n\t\tmodel = Cheking\n\t\tfields =('idNegocio','idUsuario','calificacion' ,)\n\t\n\t\t\t\n\n","sub_path":"rank/entidades/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"433573812","text":"from Client import *\r\n\r\nimport datetime\r\nimport discord\r\nimport os\r\n\r\ndef main():\r\n\t@client.event\r\n\tasync def on_message(msg):\r\n\t\tserver_id = client.get_guild(463678175009046539)\r\n\t\t\r\n\t\tif msg.content.find(\".hi\") != -1:\r\n\t\t\tawait msg.channel.send(\"Hello, World!\")\r\n\t\tif msg.content.find(\".Status\") != -1:\r\n\t\t\tawait msg.channel.send(f\"\"\"Members: {server_id.member_count}\"\"\")\r\n\t\tif msg.content.find(\".datetime\") != -1:\r\n\t\t\tawait msg.channel.send(f\"\"\"Today's date is {datetime.datetime.now()}\"\"\")\r\n\t\tif msg.content.find(\".warn\") != -1:\r\n\t\t\tcmd = msg.content\r\n\t\t\tcommand = cmd.split(' ')\r\n\t\t\tawait msg.channel.send(f\"\"\":white_check_mark: {command[1]} has been warned\"\"\")\r\n\t\tif msg.content.find(\".ping\") != -1:\r\n\t\t\tawait msg.channel.send(\"PONG!\")\r\n\r\n\t@client.event\r\n\tasync def on_ready():\r\n\t\tprint(\"Successfully logged in\")\r\n\r\n\tclient.run(token)\r\n\r\nif __name__ == '__main__':\r\n\tos.system(\"title FredRick - DiscordBot\")\r\n\tmain()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"140546290","text":"from json import dumps, loads\nimport os.path\nfrom datetime import datetime\nimport subprocess\n\nclass Backup:\n def __init__(self, date, name, scope):\n self.date = date\n self.name = name\n self.scope = scope\n\n def to_dict(self):\n return {\"date\": self.date, \"name\": self.name, \"scope\": self.scope}\n\ndef load_backups():\n backups_data = []\n if os.path.isfile(\"./backups.json\"):\n backups_file = open(\"backups.json\", \"r+\")\n backups_json = backups_file.read()\n backups_data = loads(backups_json)\n \n backups = []\n\n for backup in backups_data:\n backups.append(Backup(backup[\"date\"], backup[\"name\"], backup[\"scope\"]))\n\n if os.path.isfile(\"./backups.json\"):\n backups_file.close()\n return backups\n\ndef add_backup(date, name, scope):\n new_backup = Backup(date, name, scope)\n backups_list.append(new_backup)\n\ndef list_backups():\n for backup in backups_list:\n print(\"Backup ({}): Date: {} Scope: {}\".format(backup.name, backup.date, backup.scope))\n\ndef save_backups(backups):\n backups_save_list = []\n for b in backups:\n backups_save_list.append(b.to_dict())\n backups_file = open(\"backups.json\", \"w+\")\n backups_file.write(dumps(backups_save_list))\n backups_file.close()\n\nbackups_list = load_backups()\n\nprint(\"Welcome to the backup management tool!\")\n\nwhile True:\n print(\"Type 'home' to backup home folder\")\n print(\"Type 'full' to backup full system\")\n print(\"Type 'history' to show all backups\")\n print(\"Type 'quit' to quit the app\")\n\n command = input('Type a command: ')\n # print(command)\n if command == 'quit':\n save_backups(backups_list)\n break\n\n if command == 'home' or command == 'full':\n backup_name = input(\"Enter a name for the backup: \")\n backup_scope = command\n now = datetime.now() # current date and time\n backup_date = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n\n if command == 'home':\n subprocess.call(['rsync', '-avzh', '/home/pedroherub/dev/ka107', '/home/pedroherub/Downloads/backups/'])\n elif command == 'full':\n subprocess.call(['sudo', 'rsync', '-aAXv', '/', '--exclude={\"/dev/*\",\"/proc/*\",\"/sys/*\",\"/tmp/*\",\"/run/*\",\"/mnt/*\",\"/media/*\",\"/lost+found\"}', '/mnt'])\n\n add_backup(backup_date, backup_name, backup_scope)\n\n if command == 'history':\n list_backups()\n","sub_path":"foundation/backup_scheme.py","file_name":"backup_scheme.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"238185606","text":"'''\nusage:\n bf use [options] \n\nglobal options:\n -h --help Show help\n --profile= Use specified profile (instead of default)\n'''\n\nfrom docopt import docopt\n\nfrom .working_dataset import set_working_dataset\n\ndef main(bf):\n args = docopt(__doc__)\n\n dataset_id_or_name = args['']\n\n try:\n dataset = bf.get_dataset(dataset_id_or_name)\n set_working_dataset(dataset)\n except Exception as e:\n exit(e)\n","sub_path":"blackfynn/cli/bf_use.py","file_name":"bf_use.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"53221401","text":"from decouple import config\nfrom flask import request, Blueprint, jsonify\nimport json\nfrom simple_salesforce import Salesforce, SalesforceMalformedRequest\n\n\napi = Blueprint('api', __name__, url_prefix='/api')\n\n\n@api.route('/v1/case/feedback', methods=['POST'])\ndef case_feedback():\n\n \"\"\"\n \"\"\"\n\n sf = Salesforce(\n username=config('USERNAME'),\n password=config('PASSWORD'),\n security_token=config('SALESFORCE_TOKEN')\n )\n\n data = json.loads(request.data.decode())\n\n if request.method == 'POST' and not data['_bot-catch']:\n\n try:\n\n case = sf.Case.update(\n data.get('case_id', ''), {\n 'Rating__c': data.get('rating', ''),\n 'Feedback__c': data.get('feedback', '')\n }\n )\n\n response = f\"\"\"Case feedback has been successfully added to case: {case['id']}\"\"\"\n\n return jsonify(\n response=response,\n status=201,\n mimetype='application/json'\n )\n\n except SalesforceMalformedRequest as e:\n\n return jsonify(\n response=e,\n status=403,\n mimetype='application/json'\n )\n\n elif data['_bot-catch']:\n response = f\"\"\"It would appear that this was an attempt to complete a form via bot: {data['_bot-catch']}\"\"\"\n\n return jsonify(\n response=response,\n status=418,\n mimetype='application/json'\n )\n\n\n@api.route('/v1/lead', methods=['POST'])\ndef post_lead():\n\n \"\"\"\n \"\"\"\n\n sf = Salesforce(\n username=config('USERNAME'),\n password=config('PASSWORD'),\n security_token=config('SALESFORCE_TOKEN')\n )\n\n data = json.loads(request.data.decode())\n\n if request.method == 'POST' and not data['_bot-catch']:\n\n try:\n\n lead = sf.Lead.create(\n {\n 'FirstName': data.get('first_name', ''),\n 'LastName': data.get('last_name', ''),\n 'Email': data.get('email', ''),\n 'Company': data.get('company', ''),\n 'LeadSource': 'Website',\n }\n )\n\n return jsonify(\n response=f\"Lead successfully created: {lead['id']}\",\n status=201,\n mimetype='application/json'\n )\n\n except SalesforceMalformedRequest as e:\n\n return jsonify(\n response=e,\n status=403,\n mimetype='application/json'\n )\n\n elif data['_bot-catch']:\n\n response = f\"\"\"It would appear that this was an attempt to complete a form via bot: {data['_bot-catch']}\"\"\"\n return jsonify(\n response=response,\n status=418,\n mimetype='application/json'\n )\n","sub_path":"app/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60286657","text":"max=int(input(\"please enter the maximum value: \"))\r\n\r\neven_Sum=0\r\nodd_Sum=0\r\nfor num in range(1,max+1):\r\n if (num%2==0):\r\n even_Sum=even_Sum+num\r\n else:\r\n odd_Sum=odd_Sum+num\r\nprint(\"The sum of Even numbers 1 to {0} = {1}\".format(num,even_Sum))\r\nprint(\"The sum of odd numbers 1 to {0} = {1}\".format(num,odd_Sum))","sub_path":"python/sumevenodd(for).py","file_name":"sumevenodd(for).py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"352033698","text":"\"\"\"\nGiven an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.\n\nFollow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.\n\nExample 1:\n\nInput: nums = [-2,1,-3,4,-1,2,1,-5,4]\nOutput: 6\nExplanation: [4,-1,2,1] has the largest sum = 6.\n\nExample 2:\n\nInput: nums = [1]\nOutput: 1\n\nExample 3:\n\nInput: nums = [0]\nOutput: 0\n\nExample 4:\n\nInput: nums = [-1]\nOutput: -1\n\nExample 5:\n\nInput: nums = [-2147483647]\nOutput: -2147483647\n\"\"\"\n\n\ndef max_crossing_subarray(a, low, mid, high):\n left_sum = -float(\"inf\")\n current_sum = 0\n print(a, low, mid, high)\n for i in range(mid, low - 1, -1):\n print(i)\n current_sum += a[i]\n if current_sum > left_sum:\n left_sum = current_sum\n max_left = i\n right_sum = -float(\"inf\")\n current_sum = 0\n for j in range(mid + 1, high + 1):\n current_sum += a[j]\n if current_sum > right_sum:\n right_sum = current_sum\n max_right = j\n return (max_left, max_right, left_sum + right_sum)\n\n\ndef max_subarray(a, low, high):\n if low == high: # base case\n return (low, high, a[low])\n else:\n mid = (low + high) // 2\n left_low, left_high, left_sum = max_subarray(a, low, mid)\n right_low, right_high, right_sum = max_subarray(a, mid + 1, high)\n cross_low, cross_high, cross_sum = max_crossing_subarray(\n a, low, mid, high)\n\n if left_sum >= right_sum and left_sum >= cross_sum:\n return (left_low, left_high, left_sum)\n elif right_sum >= left_sum and right_sum >= cross_sum:\n return (right_low, right_high, right_sum)\n else:\n return (cross_low, cross_high, cross_sum)\n\n\nif __name__ == '__main__':\n change = [1, 3, -2, 6]\n m = max_subarray(change, 0, len(change) - 1)\n # print(\"change =\", change)\n # print(\"maximum subarray:\")\n # print(\"start:\", m[0], \" end:\", m[1], \" maximum sum:\", m[2])\n","sub_path":"leetcode/divide_and_conquer/53_max_sub_array.py","file_name":"53_max_sub_array.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"109994479","text":"import pymysql\nfrom neo4j.v1 import GraphDatabase, basic_auth\nfrom pymongo import MongoClient\nfrom dict import Dict\nfrom sarah import dictutils\nfrom datetime import datetime\nfrom isodate import date_isoformat, datetime_isoformat\nprint('starting gnucash.sync_dbs')\nprint('making connections')\nd1 = MongoClient('mongodb://comercialpicazo.com')\nd5 = GraphDatabase.driver('bolt://comercialpicazo.com', auth=basic_auth('alejandro', '47exI4'))\nd6 = pymysql.connect(host='comercialpicazo.com', port=3305, user='alejandro', password='5175202', autocommit=True)\n\nd1.admin.authenticate('alejandro', '47exI4')\n\nprint('connections made')\n\nd6_cursor_dict = d6.cursor(pymysql.cursors.DictCursor)\nd6_cursor = d6.cursor()\nd5_session = d5.session()\n\nprint('buscando ultimo snapshot')\n\nsnapshot = d1.gnucash.snapshot.find_one({}, {'_id': False}, sort=[('datetime', -1)])\n\nif snapshot is not None:\n snapshot = Dict(snapshot)\nelse:\n snapshot = Dict()\n\nstmt = \"\"\"insert gnucash.split (guid, description, account_guid, tx_guid, value) (select guid, IF(memo != '', memo, NULL) as description, account_guid, tx_guid, value_num / value_denom as value from gnucash.splits where datetime_created > %(splits_datetime)s and guid not in (select guid from gnucash.split));\nupdate gnucash.split inner join gnucash.splits on split.guid = splits.guid set split.description = IF(splits.memo != '', splits.memo, NULL), split.account_guid = splits.account_guid, split.tx_guid = splits.tx_guid, split.value = splits.value_num / splits.value_denom where splits.datetime_modified > %(splits_datetime)s and not splits.datetime_created > %(splits_datetime)s;\ndelete from gnucash.split where guid not in (select guid from gnucash.splits);\"\"\"\n\nif 'splits' in snapshot and 'datetime' in snapshot.splits:\n d6_cursor_dict.execute('select guid, value_num / value_denom as value, memo as description, account_guid, tx_guid '\n 'from gnucash.splits where datetime_created >= %s or datetime_modified >= %s;',\n (snapshot.splits.datetime, snapshot.splits.datetime))\n splits = [Dict(split) for split in d6_cursor_dict]\n d6_cursor.execute(stmt, {'splits_datetime': snapshot.splits.datetime})\nelse:\n d6_cursor_dict.execute('select guid, value_num / value_denom as value, memo as description, account_guid, tx_guid '\n 'from gnucash.splits;')\n splits = [Dict(split) for split in d6_cursor_dict]\nprint(0)\nfor split in splits:\n if 'description' in split and not split.description:\n del split.description\n split_node = Dict({'guid': split.guid, 'value': split.value})\n if 'description' in split:\n split_node.description = split.description\n dictutils.dec_to_float(split_node)\n rr = d5_session.run('merge (split{guid:{split_guid}}) set split:gnucash_split, split += {split} '\n 'merge (tx{guid:{tx_guid}}) merge (account{guid:{account_guid}}) '\n 'create unique (split)-[:transaction]->(tx), (tx)-[:split]->(split), '\n '(split)-[:account]->(account);',\n {'split_guid': split.guid, 'split': split_node, 'tx_guid': split.tx_guid,\n 'account_guid': split.account_guid})\n rr.single()\nprint(1)\nd6_cursor.execute('select guid from gnucash.splits')\nsplits_guid = [guid for guid, in d6_cursor]\n\nif 'splits' in snapshot and 'snapshot' in snapshot.splits:\n splits_to_delete = list(filter(lambda x: x not in splits_guid, snapshot.splits.snapshot))\n rr = d5_session.run('match (split) where split.guid in {splits_guid} detach delete split;',\n {'splits_guid': splits_to_delete})\n rr.single()\n\nif 'splits' not in snapshot:\n snapshot.splits = Dict()\nprint(2)\nsnapshot.splits.datetime = datetime_isoformat(datetime.now())\nsnapshot.splits.snapshot = splits_guid\n\nstmt = \"\"\"insert into gnucash.`transaction` (guid, description, num, date) (select guid, description, IF(num != '', num, NULL) as num, date(post_date) from gnucash.transactions where datetime_created > %(txs_datetime)s and guid not in (select guid from gnucash.transaction)); \nupdate gnucash.transaction as tx inner join gnucash.transactions as txs on tx.guid = txs.guid set tx.date = date(txs.post_date), tx.description = txs.description, tx.num = IF(txs.num != '', txs.num, NULL) where txs.datetime_modified > %(txs_datetime)s and not txs.datetime_created > %(txs_datetime)s; \ndelete from gnucash.transaction where guid not in (select guid from gnucash.transactions);\"\"\"\n\nif 'transactions' in snapshot and 'datetime' in snapshot.transactions:\n d6_cursor_dict.execute('select guid, description, num, date(post_date) as date from gnucash.transactions '\n 'where datetime_created >= %s or datetime_modified >= %s',\n (snapshot.transactions.datetime, snapshot.transactions.datetime))\n txs = [Dict(tx) for tx in d6_cursor_dict]\n\n d6_cursor.execute(stmt, {'txs_datetime': snapshot.transactions.datetime})\nelse:\n d6_cursor_dict.execute('select guid, description, num, date(post_date) as date from gnucash.transactions;')\n txs = [Dict(tx) for tx in d6_cursor_dict]\nprint(3)\n\nfor tx in txs:\n try:\n tx.num = int(tx.num)\n except ValueError:\n del tx.num\n\n tx.date = date_isoformat(tx.date)\n tx_node = Dict({'guid': tx.guid, 'date': tx.date, 'description': tx.description})\n if 'num' in tx:\n tx_node.num = tx.num\n\n rr = d5_session.run('merge (tx{guid:{guid}}) set tx:gnucash_transaction, tx += {tx};',\n {'guid': tx.guid, 'tx': tx_node})\nprint(4)\nd6_cursor.execute('select guid from gnucash.transactions')\ntxs_guid = [guid for guid, in d6_cursor]\n\nif 'transactions' in snapshot and 'snapshot' in snapshot.transactions:\n txs_guid_to_delete = list(filter(lambda x: x not in txs_guid, snapshot.transactions.snapshot))\n rr = d5_session.run('match (tx) where tx.guid in {txs_guid} detach delete tx;', {'txs_guid': txs_guid_to_delete})\n rr.single()\nprint(5)\nif 'transactions' not in snapshot:\n snapshot.transactions = Dict()\n\nsnapshot.transactions.snapshot = txs_guid\nsnapshot.transactions.datetime = datetime_isoformat(datetime.now())\n\n\n\nsnapshot.datetime = datetime_isoformat(datetime.now())\n\nd1.gnucash.snapshot.insert(snapshot)\nprint(6)\n","sub_path":"gnucash/sync_dbs.py","file_name":"sync_dbs.py","file_ext":"py","file_size_in_byte":6324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"130777992","text":"import imdbmini\nimport matplotlib.pyplot as plt\nimport operator\nimport collections\nfrom imdbpie import Imdb\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport nltk\nnltk.downloader.download('vader_lexicon')\n\ndef barchart(topic, limit):\n \"\"\"\n Accepts a topic as a list, or in this case a call to the imdbmini API.\n Displays the frequency of records (i.e. directors/stars) in the list in a bar chart\n Limits the plot based to the nu,ber of records specified by the user in 'limit'\n \"\"\"\n d = dict()\n subjects = topic\n for subject in subjects:\n if subject not in d:\n d[subject] = 1\n else:\n d[subject] += 1\n sorted_d = dict(sorted(d.items(), key=operator.itemgetter(1),reverse=True))\n sorted_d = dict(collections.Counter(sorted_d).most_common(limit))\n plt.bar(sorted_d.keys(), sorted_d.values())\n plt.show()\n\ndef hist(list):\n \"\"\"\n Accepts a list and returns a histogram of the freqeuncies at each point.\n \"\"\"\n plt.hist(list)\n plt.show()\n\ndef average_reviewscore(title):\n \"\"\"\n Accepts a movie title from user as a string.\n Calls the imdbpie API and iterates through each user review left for the specified title.\n Uses sentiment analysis and prints the average compound score of all reviews left for the particular title.\n \"\"\"\n imdb = Imdb()\n id = imdb.search_for_title(title)[0]['imdb_id']\n reviews = imdb.get_title_user_reviews(id)\n numberofreviews = len(reviews['reviews'])\n compound_scores = []\n for i in range(numberofreviews):\n review = reviews['reviews'][i]['reviewText']\n score = SentimentIntensityAnalyzer().polarity_scores(review)\n compound_scores.append(score['compound'])\n numerator = 0\n denominator = len(compound_scores)\n for i in range(denominator):\n numerator += compound_scores[i]\n average = numerator / denominator\n print(average)\n\ndef top_titles(limit):\n titles = imdbmini.get_titles()\n for i in range(0, limit):\n print(f'{i + 1}: {titles[i]}')\n\n\n\ndef main():\n barchart(imdbmini.get_directors(), 10)\n barchart(imdbmini.get_stars(), 10)\n hist(imdbmini.get_runtimes())\n top_titles(10)\n average_reviewscore('The Shawshank Redemption')\n average_reviewscore('Cats')\n\nif __name__ == \"__main__\":\n main()","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"167181176","text":"from spotipy import oauth2\n\nfrom django.conf import settings\n\nfrom .models import Credential\n\n\nPLAYLIST_NAME = 'Poke Radio'\n\n\ndef get_or_create_cred(user):\n \"\"\" Access or create the Credential model for the user\n \"\"\"\n try:\n return Credential.objects.get(user=user)\n except Credential.DoesNotExist:\n return Credential.objects.create(user=user)\n\n\ndef get_or_create_spotify_playlist(cred, sp):\n # Get or create Prad Playlist\n if cred.playlist_id:\n # Check its on sp\n res = sp.user_playlist(cred.spotify_id, cred.playlist_id)\n else:\n # Create playlist\n res = sp.user_playlist_create(cred.spotify_id, PLAYLIST_NAME, True)\n cred.playlist_id = res['id']\n cred.save()\n\n return res\n","sub_path":"web/pokeradio/spotify_playlist/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"411358828","text":"import telebot\nimport config\nimport random\nimport os\n\nfrom telebot import types\n\nbot = telebot.TeleBot(config.TOKEN)\n\n@bot.message_handler(commands=['start'])\ndef welcome(message):\n bot.send_message(message.chat.id, \"офай нахуй, {0.first_name}!!! \\nБез нигатива :) ♥ \".format(message.from_user),\n parse_mode='html') \n\n# @bot.message_handler(commands=['givePicture'])\n# def answer(message):\n# bot.send_message(message.chat.id, \"Можешь подождать {0.first_name}?. \\nПлииииз♥\".format(message.from_user), \n# parse_mode='html')\n\n@bot.message_handler(commands=['giveList'])\ndef fileCount(message):\n fileList = os.listdir(path=\"static\")\n\n markup = types.InlineKeyboardMarkup(row_width=1)\n item1 = types.InlineKeyboardButton(\"Хочешь чтобы я их сбросила?\", callback_data='send') \n\n markup.add(item1)\n\n bot.send_message(message.chat.id, \"Вот столько картинок я тебе загружу -> \" + str(len(fileList)-1), reply_markup=markup)\n\n@bot.message_handler(content_types=['text'])\ndef lalala(message):\n if message.chat.type == 'private':\n if message.text == 'Привет':\n bot.send_message(message.chat.id, \"Привет, меня зовут {0.first_name}\".format(bot.get_me()),\n parse_mode='html')\n else:\n bot.send_message(message.chat.id, \"Я не знаю что ответить (\")\n\n@bot.callback_query_handler(func=lambda call: True)\ndef callback_inline(call):\n try:\n if call.message:\n if call.data == 'send':\n rnd = random.randint(0,3)\n\n pic = open(f'static/qq ({rnd}).jpg', 'rb')\n bot.send_message(call.message.chat.id, \"Вот что у меня есть для тебя!, -> \" + str(rnd))\n bot.send_photo(call.message.chat.id, pic)\n except Exception as e:\n print(repr(e))\n\nbot.polling(none_stop=True)","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"424782637","text":"#!/usr/bin/env python\n\n# The On/Off code pairs correspond to the hand controller codes.\n# True = '1', False ='0'\n#\n# \"OUT OF THE BOX: Plug the Pi _transmitter board into the Raspberry Pi\"\n# \"GPIO pin-header ensuring correct polarity and pin alignment.\"\n# \"\"\n# \"The sockets will need to be inserted into separate mains wall sockets.\"\n# \"with a physical separation of at least 2 metres to ensure they don't\"\n# \"interfere with each other. Do not put into a single extension lead.\"\n# \"\"\n# \"For proper set up the sockets should be in their factory state with\"\n# \"the red led flashing at 1 second intervals. If this is not the case for\"\n# \"either socket, press and hold the green button on the front of the unit\"\n# \"for 5 seconds or more until the red light flashes slowly.\"\n# \"\"\n# \"A socket in learning mode will be listening for a control code to be\"\n# \"sent from a _transmitter. A socket can pair with up to 2 _transmitters\"\n# \"and will accept the following code pairs\"\n# \"\"\n# \"0011 and 1011 all sockets ON and OFF\"\n# \"1111 and 0111 socket 1 ON and OFF\"\n# \"1110 and 0110 socket 2 ON and OFF\"\n# \"1101 and 0101 socket 3 ON and OFF\"\n# \"1100 and 0100 socket 4 ON and OFF\"\n# \"\"\n# \"A socket in learning mode should accept the first code it receives\"\n# \"If you wish the sockets to react to different codes, plug in and\"\n# \"program first one socket then the other using this program.\"\n# \"\"\n# \"When the code is accepted you will see the red lamp on the socket\"\n# \"flash quickly then extinguish\"\n\nimport RPi.GPIO as GPIO\nfrom sys import argv\nimport time\n\nclass Energenie:\n\n codes = { \"socket1\" : { \"ON\": [1, 1, 1, 1], \"OFF\" : [0, 1, 1, 1] } }\n\n def __enter__(self):\n self.initialise_gpio()\n\n def __exit__(self, exception_type, exception_value, traceback):\n self.cleanup_gpio()\n\n\n def _transmit(self, code):\n # Set K0-K3\n GPIO.output (11, code[3])\n GPIO.output (15, code[2])\n GPIO.output (16, code[1])\n GPIO.output (13, code[0])\n # let it settle, encoder requires this\n time.sleep(0.1)\t\n # Enable the modulator\n GPIO.output (22, True)\n # keep enabled for a period\n time.sleep(0.25)\n # Disable the modulator\n GPIO.output (22, False)\n\n def socket1(self, state):\n \"\"\"Switch socket 1 on/off.\"\"\"\n if state == 1:\n self._transmit(self.codes[\"socket1\"][\"ON\"])\n elif state == 0:\n self._transmit(self.codes[\"socket1\"][\"OFF\"])\n\n def cleanup_gpio(self):\n GPIO.cleanup()\n\n def initialise_gpio(self):\n # set the pins numbering mode\n GPIO.setmode(GPIO.BOARD)\n\n # Select the GPIO pins used for the encoder K0-K3 data inputs\n GPIO.setup(11, GPIO.OUT)\n GPIO.setup(15, GPIO.OUT)\n GPIO.setup(16, GPIO.OUT)\n GPIO.setup(13, GPIO.OUT)\n\n # Select the signal to select ASK/FSK\n GPIO.setup(18, GPIO.OUT)\n\n # Select the signal used to enable/disable the modulator\n GPIO.setup(22, GPIO.OUT)\n\n # Disable the modulator by setting CE pin lo\n GPIO.output (22, False)\n\n # Set the modulator to ASK for On Off Keying \n # by setting MODSEL pin lo\n GPIO.output (18, False)\n\n # initialise_gpio K0-K3 inputs of the encoder to 0000\n GPIO.output (11, False)\n GPIO.output (15, False)\n GPIO.output (16, False)\n GPIO.output (13, False)\n\ndef main():\n state = int(argv[1])\n\n with Energenie() as e:\n e.socket1(state)\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"energenie.py","file_name":"energenie.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"149949612","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 26 13:50:45 2020\n\n@author: t1\n\"\"\"\nimport numpy as np\nfrom scipy.special import comb\n\ndef get_prob(k,N):\n ''' \n computes probability of at there bieng at least N AaBb organisms in kth generation\n probalem statement : http://rosalind.info/problems/lia/\n '''\n \n total_prob = 0.0\n # for i in range(N,2**k + 1):\n # total_prob += (1/4)**i\n \n for i in range(N,2**k+1):\n total_prob += comb(2**k, i)*((1/4)**i * (3/4)**(2 ** k - i))\n return total_prob\n\n# def get_n_from_k(k,n):\n# temp1 = np.prod(np.arange(1,k+1))\n# temp2 = np.prod(np.arange(1,n+1))\n# temp3 = np.prod(np.arange(1,k-n +1))\n# print(temp1,temp2,temp3)\n# return temp1/(temp2*temp3)\n\nprint(get_prob(7,32))","sub_path":"stronghold/independent_alleles.py","file_name":"independent_alleles.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"504303566","text":"from random import choice, randint\nfrom string import punctuation\n\n\ndef open_and_read_file(file_path):\n \"\"\"Takes file path as string; returns text as string.\n\n Takes a string that is a file path, opens the file, and turns\n the file's contents as one string of text.\n \"\"\"\n\n the_file = file_path.split(\" \")\n\n if len(the_file) > 1:\n file_1 = open(the_file[0])\n file_2 = open(the_file[1])\n contents_read = file_1.read() + file_2.read()\n file_1.close()\n file_2.close()\n else:\n file_1 = open(the_file[0])\n contents_read = file_1.read()\n file_1.close()\n\n text = contents_read.split()\n\n return text\n\n\ndef make_chains(text_string, num_words):\n \"\"\"Takes input text as string; returns _dictionary_ of markov chains.\n\n A chain will be a key that consists of a tuple of (word1, word2)\n and the value would be a list of the word(s) that follow those two\n words in the input text.\n\n For example:\n\n >>> make_chains(\"hi there mary hi there juanita\")\n {('hi', 'there'): ['mary', 'juanita'], ('there', 'mary'): ['hi'], ('mary', 'hi': ['there']}\n \"\"\"\n chains = {}\n\n #loops through the text string until it reaches the \"num\" to last word\n for index in range(0, len(text_string) - num_words):\n #defines a variable for the first word. makes a list of next words and puts first_word in it\n first_word = text_string[index]\n next_words = [first_word]\n\n #goes through the text string from right after your first word, above, to the number of words you need.\n #for each loop til you get to the number of words needed, appends the next word to your next_words list\n for i in range(1, num_words):\n next_words.append(text_string[index + i])\n # converts the next_words list into a tuple and gets the word that follows the tuple\n next_words_tuple = tuple(next_words)\n following_word = text_string[num_words + index]\n\n #if a key with a next_word_tuple is already in the dict,\n #appends the following word to the list already in place as the value.\n #else, creates the key with the following word in a list.\n if next_words_tuple in chains:\n chains[next_words_tuple].append(following_word)\n else:\n chains[next_words_tuple] = [following_word]\n\n return chains\n\n\ndef make_text(chains):\n \"\"\"Takes dictionary of markov chains; returns random text.\"\"\"\n \n #sets variable current_key equal to a random choice in the keys in dict chains\n current_key = choice(chains.keys())\n while not current_key[0].istitle():\n current_key = choice(chains.keys())\n \n #loops over current key tuple and sets text equal to the first current key value\n text = \"\"\n for i in range(len(current_key)):\n text += current_key[i] + \" \"\n\n #while the current_key is in the chains dict, loops through the below:\n while current_key in chains:\n current_value = choice(chains[current_key])\n text += current_value + \" \"\n current_key = current_key[1:] + (current_value,)\n\n return text\n\ndef make_tweet(text):\n\n #get a random length for the tweet\n tweet_length = 139\n\n #if the text length is less than the random length, make text length the tweet length\n # if len(text) < tweet_length:\n # tweet_length = len(text)\n\n #strip the text and split it by white space\n text_list = text.rstrip().split(\" \")\n\n tweet_list = []\n tweet_string = \"\"\n #iterate over each word in the text and add it to the tweet list until the\n #tweet reaches predefined length\n for word in text_list:\n if len(tweet_string + word) > tweet_length:\n break\n else: \n tweet_list.append(word)\n tweet_string += word\n\n #checks the tweet string to see if it has punctuation marks of any kind\n has_punctuation = False\n for char in punctuation:\n if char in tweet_string:\n has_punctuation = True\n break\n\n\n #if no punctuation in tweet string then add period at the end\n #else \n #while the last character in the last word in tweet list is not a punctuation mark\n #deletes the last word in the tweet list\n if not has_punctuation and len(tweet_string) < 140:\n tweet_list[-1] += '.'\n else:\n while tweet_list[-1][-1] not in punctuation:\n del tweet_list[-1]\n\n #joins the tweet list to make a string of words to tweet\n tweet = \" \".join(tweet_list)\n\n return tweet\n\n\n\n","sub_path":"markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":4540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"617301438","text":"import pygame, sys, random\n\nfrom pygame.locals import *\n\npygame.init()\n\nscreen_info = pygame.display.Info()\n# size = (width, height) = (int(screen_info.current_w), int(screen_info.current_h))\nsize = (width, height) = (1000,600)\nscreen = pygame.display.set_mode(size)\nclock = pygame.time.Clock()\nfish_image = pygame.image.load(\"fish.png\")\nfish_image = pygame.transform.smoothscale(fish_image, (300,200))\nfish_rect = fish_image.get_rect()\nfish_rect.center = (width // 2, height // 2)\nspeed = pygame.math.Vector2(0,10)\nrotation = random.randint(40,360)\nspeed.rotate_ip(rotation)\nprint(rotation)\nfish_image = pygame.transform.rotate(fish_image, 180 - rotation)\n\n\n\ndef move_fish():\n fish_rect.move_ip(speed)\n\n\n\n\ndef main():\n while True:\n clock.tick(60)\n for event in pygame.event.get():\n if event.type == QUIT:\n sys.exit()\n move_fish()\n screen.fill((0, 127, 255))\n screen.blit(fish_image, fish_rect)\n pygame.display.flip()\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"251200709","text":"import copy\n\nimport numpy as np\n\nimport nengo\nfrom nengo.utils.network import with_self\n\n\nclass EnsembleArray(nengo.Network):\n\n def __init__(self, neurons, n_ensembles, ens_dimensions=1, label=None,\n **ens_kwargs):\n if \"dimensions\" in ens_kwargs:\n raise TypeError(\n \"'dimensions' is not a valid argument to EnsembleArray. \"\n \"To set the number of ensembles, use 'n_ensembles'. To set \"\n \"the number of dimensions per ensemble, use 'ens_dimensions'.\")\n\n label_prefix = \"\" if label is None else label + \"_\"\n\n self.n_ensembles = n_ensembles\n self.dimensions_per_ensemble = ens_dimensions\n transform = np.eye(self.dimensions)\n\n self.input = nengo.Node(size_in=self.dimensions, label=\"input\")\n\n for i in range(n_ensembles):\n e = nengo.Ensemble(\n copy.deepcopy(neurons), self.dimensions_per_ensemble,\n label=label_prefix + str(i), **ens_kwargs)\n trans = transform[i * self.dimensions_per_ensemble:\n (i + 1) * self.dimensions_per_ensemble, :]\n nengo.Connection(self.input, e, transform=trans, synapse=None)\n\n self.add_output('output', function=None)\n\n @with_self\n def add_output(self, name, function, synapse=None, **conn_kwargs):\n if function is None:\n function_d = self.dimensions_per_ensemble\n else:\n func_output = function(np.zeros(self.dimensions_per_ensemble))\n function_d = np.asarray(func_output).size\n\n dim = self.n_ensembles * function_d\n output = nengo.Node(size_in=dim, label=name)\n setattr(self, name, output)\n\n for i, e in enumerate(self.ensembles):\n nengo.Connection(\n e, output[i*function_d:(i+1)*function_d], function=function,\n synapse=synapse, **conn_kwargs)\n return output\n\n @property\n def dimensions(self):\n return self.n_ensembles * self.dimensions_per_ensemble\n","sub_path":"nengo_alt/nengo/networks/ensemblearray.py","file_name":"ensemblearray.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"153762094","text":"\"\"\"\nModule for preprocessing functions\n\"\"\"\nimport re\nimport numpy as np\nimport pandas as pd\n\ndef keep_track_of_missing_values(df, missing_ind):\n \"\"\"\n Adds columns indicating if original columns had\n missing values.\n In:\n - df: pandas df\n - missing_ind: (str) indicates columns that keep track of\n missing data\n Out:\n - df: function modifies df inplace\n - list of newly created columns\n \"\"\"\n new_columns = []\n\n for column in df:\n\n if df[column].isnull().values.any():\n new_col_name = column + missing_ind\n new_col = np.zeros(len(df), dtype=np.int)\n new_col[df[column].isnull()] = 1\n df[new_col_name] = new_col\n new_columns.append(new_col_name)\n return new_columns\n\n\ndef fill_missing_values_with_mean(df, class_mean = False, key = None):\n \"\"\"\n Replaces missing values with mean of class.\n In:\n - df: pandas df\n - class_mean: (bool) whether or not to use mean of class\n - key: column for classes (opt)\n Out:\n - df\n \"\"\"\n if class_mean:\n key_col = df[key]\n df = df.groupby(key).transform(lambda x: x.fillna(x.mean()))\n df[key] = key_col\n return df\n else:\n return df.fillna(df.mean())\n\n\ndef in_bound_test(value, start, end):\n \"\"\"\n Helper function to recreate discretized dummy\n vars in test set.\n In:\n - value:\n - start:\n - end:\n Out:\n - 1 if value in range; 0 o/w\n \"\"\"\n if value >= start and value <= end:\n return 1\n else:\n return 0\n\n\ndef insert_discretize_quantiles(df, col_to_value_dict, drop_original=False):\n \"\"\"\n In:\n - df: pandas dataframe\n - col_to_value_dict: (dict) with columns (keys) to be\n created in df as dummy vars according to values\n {original_col: [(dummy_col, start, end),\n (dummy_col, start, end)]}\n - drop_original: (bool) whether or not to drop original column\n that discrete dummies are generated from\n Out:\n - df\n \"\"\"\n for original_col in col_to_value_dict:\n\n list_of_dum_cols = col_to_value_dict[original_col]\n\n for list_of_dum_col in list_of_dum_cols:\n dummy_col, start, end = list_of_dum_col\n df[dummy_col] = df.apply(lambda row: in_bound_test(row[original_col], start, end), axis=1)\n\n if drop_original:\n del df[original_col]\n\n return df\n\n\ndef build_col_to_value_dict(df, dummy_code):\n \"\"\"\n Function that builds dict with discretized columns\n and their dummy columns with cut-off values.\n In:\n - df: pandas dataframe\n - dummy_code: (str) to append to dummy columns\n Out:\n - dict\n \"\"\"\n col_to_value_dict = {}\n\n for col in df.columns:\n if dummy_code in col:\n\n start_pos = col.find(dummy_code + \"_\")\n original_col = (col[:start_pos])\n if not original_col in col_to_value_dict:\n col_to_value_dict[original_col] = []\n\n start_pos = col.find(dummy_code + \"_\")\n cut_off_vals = col[start_pos + len(dummy_code + \"_\"):]\n\n start = cut_off_vals[1: cut_off_vals.find(\",\")]\n if cut_off_vals[0] == \"[\":\n start = int(start)\n else:\n start = int(start) + 1\n\n end = cut_off_vals[cut_off_vals.find(\",\") + 1:]\n numbers = re.findall('[\\d\\.]+', end)\n if numbers:\n end = float(numbers[0])\n else:\n raise ValueError('Could not find number value for end in cut_off_vals.')\n if cut_off_vals[:-1] == \")\":\n end -= 1\n\n col_to_value_dict[original_col].append((col, start, end))\n\n return col_to_value_dict\n\n\ndef create_missing_value_colum_in_testset(traindf, testdf, missing_ind):\n \"\"\"\n Creates same columns indicating missing values as we\n have in training set.\n In:\n - traindf: pandas dataframe with training data\n - testdf: pandas dataframe with test data\n - missing_ind: (str) indicates columns that keep track of\n missing data\n Out:\n - df\n \"\"\"\n for col in traindf.columns:\n\n if col[-len(missing_ind):] == missing_ind:\n\n original_col = col[:col.find(missing_ind)]\n\n new_col = np.zeros(len(testdf), dtype=np.int)\n new_col[testdf[original_col].isnull()] = 1\n testdf[col] = new_col\n\n\ndef discretize_cont_var(df, cont_var, n, dummy_code, drop=False):\n \"\"\"\n Discretizes continuous variable.\n In:\n - df: pandas dataframe\n - cont_var: continues variable to be discretized\n - n: number of percentiles\n - dummy_code: (str) to append to dummy columns\n - drop: (bool) to drop continous variable\n Out:\n - df\n \"\"\"\n step_size = 1/n\n bucket_array = np.arange(0, 1+step_size, step_size)\n\n df[cont_var + dummy_code] = pd.qcut(df[cont_var], bucket_array)\n df = pd.get_dummies(df, columns=[cont_var + dummy_code])\n\n if drop:\n del df[cont_var]\n\n return df\n\n\ndef dummify_var(df, cat_vars, drop=False):\n \"\"\"\n Takes categorical variable and creates binary/dummy variables from it.\n In:\n - df: pandas dataframe\n - cat_vars: list of categorical variables\n - drop: (bool) whether or not to drop first dummy\n Out:\n - df: pandas dataframe\n \"\"\"\n return pd.get_dummies(df, columns=cat_vars, drop_first=drop)\n","sub_path":"Assignment2_ML_Pipeline/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":5559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"603325653","text":"import tensorflow as tf\nimport numpy as np\n\n\n\n\nx_data = np.matrix([[0,0,1,1], [0,1,0,1]]).T.astype(\"float32\")\ny_data = np.matrix([[0], [1], [1], [0]]).astype(\"float32\")\nprint(x_data)\n\n\nW = tf.Variable(tf.random_uniform([2,5], -1.0, 1.0, dtype=tf.float32))\nW2 = tf.Variable(tf.random_uniform([5, 1], -1.0, 1.0, dtype=tf.float32))\nb = tf.Variable(tf.zeros([5]))\ny = tf.nn.sigmoid(tf.matmul(tf.nn.sigmoid(tf.matmul(x_data, W) + b), W2))\nprint(\"Y:\", tf.cast(y, tf.float32))\n\n# Minimize the mean squared errors.\nloss = tf.reduce_mean(tf.square((y - y_data)))\noptimizer = tf.train.GradientDescentOptimizer(0.7)\ntrain = optimizer.minimize(loss)\n\n# Before starting, initialize the variables. We will 'run' this first.\ninit = tf.initialize_all_variables()\n\n# Launch the graph.\nsess = tf.Session()\nsess.run(init)\n\n# Fit the line.\nfor step in xrange(4000):\n sess.run(train)\n if step % 1000 == 0:\n print (step, sess.run(W),\"\\n\", sess.run(b))\n\ncp = y-y_data\naccuracy = tf.reduce_mean(tf.cast(cp, tf.float32))\nprint(sess.run(y), y_data)\n","sub_path":"nntest.py","file_name":"nntest.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"563906479","text":"import pygame, sys, random\nfrom pygame.locals import *\n\nclass Runner():\n __customes = (\"turtle\", \"fish\", \"prawn\", \"moray\", \"octopus\")\n \n def __init__(self, x=0, y=0):\n \n ixCustome = random.randint(0,4)\n \n self.custome = pygame.image.load(\"images/{}.png\".format(self.__customes[ixCustome]))\n self.position = [x, y]\n self.name = \"\"\n \n \nclass Game():\n def __init__(self):\n self.__screen = pygame.display.set_mode((640, 480))\n self.__background = pygame.image.load(\"images/background.png\")\n pygame.display.set_caption(\"Carrera de bichos\")\n \n self.runner = Runner(320, 240)\n \n def start(self):\n gameOver = False\n while not gameOver:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n elif event.type == KEYDOWN:\n if event.key == K_UP:\n #MOVER HACIA ARRIBA RUNNER\n self.runner.position[1] -= 5\n elif event.key == K_DOWN:\n #MOVER HACIA ABAJO RUNNER\n self.runner.position[1] += 5\n elif event.key == K_LEFT:\n #MOVER HACIA LA IZQUIERDA\n self.runner.position[0] -= 5\n elif event.key == K_RIGHT:\n #MOVER HACIA LA DERECHA\n self.runner.position[0] += 5\n else:\n pass\n \n self.__screen.blit(self.__background, (0, 0))\n self.__screen.blit(self.runner.custome, self.runner.position)\n pygame.display.flip()\n \n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n \n \nif __name__ == \"__main__\":\n game = Game()\n pygame.init()\n game.start()","sub_path":"carreraConMovimiento.py","file_name":"carreraConMovimiento.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"500611790","text":"from django.db.models import fields\nfrom rest_framework import serializers\nfrom pagos_app import models\n\nclass PagosSerializer(serializers.ModelSerializer):\n class Meta:\n fields =(\n 'id',\n 'title',\n 'description',\n 'amount',\n 'adress',\n 'typePage',\n 'date'\n )\n model = models.Pagos","sub_path":"apis/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"343311346","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, unicode_literals\n\nfrom django.apps import AppConfig\n\n\nclass WagbootConfig(AppConfig):\n name = 'wagboot'\n\n def ready(self):\n from wagtail.wagtailimages.formats import register_image_format, Format, unregister_image_format\n unregister_image_format('left')\n unregister_image_format('right')\n unregister_image_format('fullwidth')\n\n for align in ('centered', 'left', 'right', 'inline'):\n register_image_format(Format('{}-original'.format(align), '{}, original size'.format(align.capitalize()), 'richtext-image {}'.format(align), 'original'))\n for size in ('42', '50', '72', '100', '200', '500', '800'):\n register_image_format(\n Format('{}-{}'.format(align, size),\n '{}, {}px'.format(align.capitalize(), size),\n 'richtext-image {}'.format(align), 'width-{s}'.format(s=size)))\n","sub_path":"wagboot/apps.py","file_name":"apps.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"604618985","text":"'''\n Пользователь вводит месяц в виде целого числа от 1 до 12.\n Сообщить к какому времени года относится месяц (зима, весна, лето, осень).\n Напишите решения через list и через dict\n'''\n\nmonths = [str(x) for x in range(1,13)]\nseasons = [ \"winter\", \"winter\", \"winter\", \\\n \"spring\", \"spring\", \"spring\", \\\n \"summer\", \"summer\", \"summer\", \\\n \"fall\", \"fall\", \"fall\" ]\n\nselection = input(\"Pick a month as a digit:\\n\")\n\nif selection in months:\n print(seasons[months.index(selection)])\n","sub_path":"homework/lesson2/prob3b.py","file_name":"prob3b.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"429226310","text":"# coding: utf-8\n# authour: Yi Yao\n# edited by Timothy Sullivan\n\nimport tensorflow as tf\nfrom sklearn.model_selection import KFold\nimport numpy as np\nimport pandas as pd\nimport os\n\n# ===========================================================================================================================================\n\n#Helper functions:\n\n# input: np.array(unnormalized feature) + np.array(mean) + np.array(range) => np.array(normlized feature)\n# normalize the feature by the given parameters\ndef feature_normalize(Feature, mean_value, range_value):\n Feature = Feature - mean_value\n Feature = Feature / range_value\n return Feature\n\n# input: string(path of the file) => output: np.array(input/features for tensorflow model) + np.array(output/lable for tensorflow model) + np.array(mean) + np.array(range)\n# generating the input and output for tensorflow model and parameters for the latter normalization according to the given file\ndef get_input_output(path):\n df = pd.read_excel(path)\n L = df.to_numpy()\n L1 = np.delete(L, 0, 1)\n L1 = np.delete(L1, 0, 1)\n Y = L1[:,-1]\n L1 = np.delete(L1, -1, 1)\n X = np.asarray(L1).astype(np.float32) # Features\n # max_norm = np.max(X, axis=0)\n # min_norm = np.min(X, axis=0)\n # range_norm = (max_norm - min_norm) + (10 ** -8)\n range_norm = np.std(X, axis=0) + (10 ** -8)\n range_norm = range_norm.reshape((1, 131))\n mean_norm = np.mean(X, axis=0)\n mean_norm = mean_norm.reshape((1, 131))\n X = feature_normalize(X, mean_norm, range_norm)\n Y = np.asarray(Y).astype(np.float32) # Labels\n return X, Y, mean_norm, range_norm\n\n# input: List(AUC) + List(RMSE) + hyperparameters (Dropout value, layer 1 neurons, layer 2 neurons) => output: Dataframe ready to excel write\n# Saves the mean and standard deviation of AUC and RMSE for the Kfold validation sets\ndef save_auc_rmse_per_fold(AUC_list, RMSE_list, Dval, L1val, L2val):\n mean_auc = np.mean(AUC_list, axis=0)\n std_auc = np.std(AUC_list, axis=0)\n mean_rmse = np.mean(RMSE_list, axis=0)\n std_rmse = np.std(RMSE_list, axis=0)\n L_sum = [Dval, L1val, L2val, mean_auc, std_auc, mean_rmse, std_rmse]\n df = pd.DataFrame(L_sum).T\n df.columns = ['Dropout Val', 'Layer 1 Neurons', 'Layer 2 Neurons', 'AUC', 'St Dev', 'RMSE', 'St Dev']\n return df\n\n# input: List(dataframes) + List(sheet names) + String(path to excel file) => output: excel file\n# Writes given dataframes to their corresponding sheet names in the excel file at path\ndef write_to_excel(df_list, sheet_list, path):\n with pd.ExcelWriter(path) as writer:\n for i in range(len(df_list)):\n df_list[i].to_excel(writer, header=True, index=False, sheet_name=sheet_list[i])\n\n\n# =============================================================================================================================================\n# Main function: \n\n# Set paths and load data\n\ndata_path = os.path.dirname(os.path.realpath(__file__))\n\n# path = data_path + \"/features/Features of original tenary alloys.xlsx\"\npath = data_path + \"/features/Features of balanced ternary alloys.xlsx\"\nX, Y, mean_norm, range_norm= get_input_output(path)\n\n\n#Result Dataframes. Must remain outside of all loops\nperFold_df = pd.DataFrame(columns = ['Dropout Val', 'Layer 1 Neurons', 'Layer 2 Neurons', 'AUC', 'St Dev', 'RMSE', 'St Dev'])\n\nfor dv in [0, 0.01, 0.05, 0.1, 0.2, 0.5]:\n for l_1 in [50, 100, 150, 200, 250]:\n for l_2 in [10, 15, 20, 25, 50]:\n\n # Initialization Hyperparameters\n dropout_val = dv\n layer_1_neurons = l_1\n layer_2_neurons = l_2\n layer_1_activation = 'sigmoid'\n layer_2_activation = 'sigmoid'\n output_layer_activation = 'sigmoid'\n num_epochs = 20 #We could consider the number of epochs a hyperparameter in this model. There might be a way to train it with callbacks.EarlyStopping()\n\n # Define the K-fold Cross Validator\n kfold = KFold(n_splits=10, shuffle=True)\n\n # some list to save data\n auc_per_fold = []\n rmse_per_fold = []\n L_auc = [] # save auc for each single alloy system\n L_rmse = [] # save RMSE for each single alloy system\n\n # K-fold Cross Validation model evaluation\n fold_no = 1\n\n for train, test in kfold.split(X, Y):\n\n # Define the model architecture\n\n model = tf.keras.models.Sequential([\n tf.keras.Input(shape=(1,131)),\n tf.keras.layers.Dense(layer_1_neurons, activation=layer_1_activation),\n tf.keras.layers.Dropout(dropout_val),\n tf.keras.layers.Dense(layer_2_neurons, activation=layer_2_activation),\n tf.keras.layers.Dense(1, activation=output_layer_activation)])\n\n # Compile the model\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['AUC','RootMeanSquaredError'])\n\n # Generate a print\n print('------------------------------------------------------------------------')\n print(f'Training for fold {fold_no} ...')\n\n # Fit data to model\n history = model.fit(x=X[train], y=Y[train],\n batch_size=1,\n epochs=num_epochs,\n verbose=2)\n\n # Generate generalization metrics\n scores = model.evaluate(X[test], Y[test], verbose=0)\n print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]}; {model.metrics_names[2]} of {scores[2]}')\n auc_per_fold.append(scores[1])\n rmse_per_fold.append(scores[2])\n\n # Increase fold number\n fold_no = fold_no + 1\n\n # Save data\n df1 = save_auc_rmse_per_fold(auc_per_fold, rmse_per_fold, dropout_val, layer_1_neurons, layer_2_neurons)\n perFold_df = perFold_df.append(df1, ignore_index=True)\n\n\n#Final excel write. Must go outside all loops\n#save_path = data_path + \"/results/AUC_RMSE_original.xlsx\"\nsave_path = data_path + \"/results/AUC_RMSE_balanced.xlsx\"\n\nwrite_to_excel([perFold_df], [\"perFold\"], save_path)\n\n","sub_path":"3-hyperparameterTuning/ten-fold-CV-uahpc_bal.py","file_name":"ten-fold-CV-uahpc_bal.py","file_ext":"py","file_size_in_byte":6202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"413330451","text":"# An Accessory for a LED attached to pin 11.\nimport logging\n\nimport RPi.GPIO as GPIO\n\nfrom pyhap.accessory import Accessory, Category\nimport pyhap.loader as loader\n\n\nclass LightBulb(Accessory):\n\n category = Category.LIGHTBULB\n\n @classmethod\n def _gpio_setup(_cls, pin):\n if GPIO.getmode() is None:\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(pin, GPIO.OUT)\n\n def __init__(self, *args, pin=11, **kwargs):\n super(LightBulb, self).__init__(*args, **kwargs)\n self.pin = pin\n self._gpio_setup(pin)\n\n def __setstate__(self, state):\n self.__dict__.update(state)\n self._gpio_setup(self.pin)\n\n def set_bulb(self, value):\n if value:\n GPIO.output(self.pin, GPIO.HIGH)\n else:\n GPIO.output(self.pin, GPIO.LOW)\n\n def _set_services(self):\n super(LightBulb, self)._set_services()\n\n bulb_service = loader.get_serv_loader().get(\"Lightbulb\")\n self.add_service(bulb_service)\n bulb_service.get_characteristic(\"On\").setter_callback = self.set_bulb\n\n def stop(self):\n super(LightBulb, self).stop()\n GPIO.cleanup()\n","sub_path":"pyhap/accessories/LightBulb.py","file_name":"LightBulb.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128842869","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n\n# 参数分析\ndef variable_summaries(var):\n with tf.name_scope('summaries'):\n mean = tf.reduce_mean(var)\n tf.summary.scalar('mean', mean) # 均值\n\n with tf.name_scope('stddev'):\n stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n tf.summary.scalar('stddev', stddev) # 标准差\n tf.summary.scalar('max', tf.reduce_max(var)) # 最大值\n tf.summary.scalar('min', tf.reduce_min(var)) # 最小值\n tf.summary.histogram('histogram', var) # 直方图\n\n\nif __name__ == '__main__':\n # 读入数据\n mnist = input_data.read_data_sets('../dataset', one_hot=True)\n\n batch_size = 50\n # 计算批次\n n_batch = mnist.train.num_examples // batch_size\n\n # 命名空间\n with tf.name_scope('input'):\n x = tf.placeholder(tf.float32, [None, 784], name='x_input')\n y = tf.placeholder(tf.float32, [None, 10], name='y_input')\n\n with tf.name_scope('layer'):\n with tf.name_scope('wights'):\n # 权重\n W = tf.Variable(tf.zeros([784, 10]), name='W')\n variable_summaries(W)\n with tf.name_scope('biases'):\n # 偏置\n b = tf.Variable(tf.zeros([10]), name='b')\n variable_summaries(b)\n with tf.name_scope('Wx_pusl_b'):\n Wx_pusl_b = tf.matmul(x, W) + b\n\n with tf.name_scope('softmax'):\n # 预测值\n predication = tf.nn.softmax(Wx_pusl_b)\n\n with tf.name_scope('loss'):\n # 损失函数\n loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=predication))\n tf.summary.scalar('loss', loss)\n\n with tf.name_scope('train'):\n # 梯度下降\n train = tf.train.GradientDescentOptimizer(0.2).minimize(loss)\n\n with tf.name_scope('accuracy'):\n # axis=0时比较每一列的元素\n # axis=1时比较每一行的元素\n with tf.name_scope('correct_prediction'):\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(predication, 1))\n with tf.name_scope('accuracy'):\n # 准确率\n # tf.cast()函数的作用是执行 tensorflow 中张量数据类型转换\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.summary.scalar('accuracy', accuracy)\n\n # 初始化\n init = tf.global_variables_initializer()\n\n # 合并所有summary\n merged = tf.summary.merge_all()\n\n with tf.Session() as sess:\n sess.run(init)\n writer = tf.summary.FileWriter('logs/', sess.graph)\n for step in range(51):\n for i in range(n_batch):\n # 获取训练集\n batch_x, batch_y = mnist.train.next_batch(batch_size)\n summary, _ = sess.run([merged, train], feed_dict={x: batch_x, y: batch_y})\n\n writer.add_summary(summary, step)\n # 输入测试集\n Accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})\n\n print(step, '|loss:', sess.run(loss, feed_dict={x: batch_x, y: batch_y}), '|accuracy:', str(Accuracy))\n","sub_path":"5_3.py","file_name":"5_3.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"518535663","text":"from distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\n\nimport numpy as np\n\next = [\n Extension(\"resolving\", sources=[\"jester/resolving.pyx\"]),\n Extension(\"filtering\", sources=[\"jester/filtering.pyx\"]),\n Extension(\"matching\", sources=[\"jester/matching.pyx\"])\n]\n\nsetup(\n cmdclass={'build_ext': build_ext},\n ext_modules=ext,\n include_dirs=[np.get_include()]\n)\n","sub_path":"jester/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"486442458","text":"import scipy.io\nimport numpy as np\nimport matplotlib.pyplot\nfrom exceptions import*\n\n#On ouvre le fichier de données (qui doit être dans le dossier de l'exécutable !)\ndef lecture(chemin):\n \"\"\"\n méthode qui ouvre le fichier de données au nom spécifié en argument\n renvoie les données, indexées par ordre;\n et les affectations de chaque chiffre, indexées aussi par ordre\n \"\"\"\n mat = scipy.io.loadmat(chemin)\n data = np.transpose(mat['data'])\n label = np.array(mat['label'])\n label = label.astype(int)\n return data, label\n\n\nclass Donnees():\n \"\"\"\n objet contenant les images de la base de données sous forme de matrice numpy,\n et le chiffre correspondant à chaque image.\n Tout est indexé selon le \"numéro\" de chaque image.\n Le paramètre optionnel \"portion\" permet de diviser la base de données aléatoirement selon un pourcentage.\n Par défaut, la division est de 80%.\n \"\"\"\n\n def __init__ (self, images, labels, portion = 0.8):\n \"\"\"\n Un set de données contient huit propriétés:\n deux jeux d'images qu'il contient, divisés selon un pourcentage en paramètre;\n un set de classification par jeu d'images;\n pour chaque jeu d'image, un tableau d'index permettant une stabilité dans la numérotation;\n et le cardinal de chaque jeu.\n Si le pourcentage est paramétré à 100 ou 0, le jeu A sera le seul rempli.\n Par défaut, le chemin correspond au dossier de l'executable\n \"\"\"\n card_defaut = len(labels[0])\n \n if portion > 1 or portion < 0:\n raise PourcentageIncorrect\n \n if portion == 1 or portion == 0:\n self.imagesA = images\n self.categA = labels[0]\n self.cardinalA = card_defaut\n self.imagesB = np.zeros(1)\n self.categB = np.zeros(1)\n self.cardinalB = 1\n self.indexA = labels[0]\n self.indexB = np.zeros(1)\n\n if portion > 0 and portion < 1: #En commentaires, des notes sur la forme de chaque variable\n indices = np.random.permutation(images.shape[0])\n setA_indx, setB_indx = indices[:int(portion*card_defaut)], indices[int(portion*card_defaut):]\n self.indexA = setA_indx #matrice numpy de forme (portion*card_defaut,) (la virgule n'est pas accidentelle!)\n self.indexB = setB_indx #contiennent les \"anciens\" indices des images, dans l'ordre de leur nouvelle base\n self.imagesA = images[setA_indx,:] #matrice numpy de forme (portion*card_defaut, 784) \n self.imagesB = images[setB_indx,:] #contiennent les images, dans le nouvel ordre, sous forme de vecteurs de R784\n self.categA = labels[:,setA_indx] #matrice numpy de forme (1, portion*card_defaut)\n self.categB = labels[:,setB_indx] #ignorez le premier index, l'important est que le second vecteur fait correspondre les images à leur valeur, au même index\n self.cardinalA = int(card_defaut*portion)\n self.cardinalB = int(card_defaut*(1-portion))\n \n \"\"\"\n principe de l'index:\n je veux accéder à l'image numéro i dans la base de données originale.\n à supposer que je sais dans quelle base de données elle est (disons A),\n iA = int(np.where(self.indexA == i)[0]) est l'index de l'image i dans la base A\n self.imagesA[iA] me renvoie cette image.\n inversement, je veux savoir quel est l'index original de la i-ème image de ma base A:\n i0 = indexA[i] est exactement cet index.\n\n Je mets ça en commentaire pour plus de clarté, mais le processus est implémenté entièrement dans les\n méthodes respectives index_dans_bd et index_originel\n \"\"\"\n\n\n def index_dans_bd(self, i):\n \"\"\"\n renvoie l'index d'une image i dans la nouvelle répartition, et la base de donnée dans laquelle elle est\n \"\"\"\n if len(np.where(self.indexA == i)[0]) != 0:\n return int(np.where(self.indexA == i)[0]), 'A'\n if len(np.where(self.indexB == i)[0]) != 0:\n return int(np.where(self.indexB == i)[0]), 'B'\n else:\n raise IndexIncorrect\n\n def index_originel(self, i, bD):\n \"\"\"\n renvoie l'index originel d'une image i dans une BD spécifiée\n \"\"\"\n if bD == 'A':\n return self.indexA[i]\n if bD == 'B':\n return self.indexB[i]\n else:\n raise MauvaiseBD\n\n def affichage(self, i):\n \"\"\"\n affiche l'image numérotée i (dans la base de données originale !)\n \"\"\"\n index_converti, bD = self.index_dans_bd(i)\n if bD == 'A':\n V = self.imagesA[index_converti].reshape((28,28))\n matplotlib.pyplot.imshow(V, cmap = 'gray',vmin = 0 ,vmax = 255)\n matplotlib.pyplot.show()\n if bD == 'B':\n V = self.imagesB[index_converti].reshape((28,28))\n matplotlib.pyplot.imshow(V, cmap = 'gray',vmin = 0 ,vmax = 255)\n matplotlib.pyplot.show()\n if bD != 'A' and bD != 'B':\n raise MauvaiseBD\n\n def affichage_index_bD(self, i, bD):\n \"\"\"\n affiche l'image numérotée i dans la base de données spécifiée\n \"\"\"\n if bD != 'A' and bD != 'B':\n raise MauvaiseBD\n self.affichage(i, bD)\n \n\n def sortir_image(self, i):\n \"\"\"\n renvoie l'image numérotée i dans la base originelle\n \"\"\"\n iC, bD = self.index_dans_bd(i)\n if bD == 'A':\n return self.imagesA[iC]\n if bD == 'B':\n return self.imagesB[iC]\n\n def sortir_chiffre(self, n, bD = 'A'):\n \"\"\"\n Renvoie toutes les images correspondant au chiffre indiqué, dans une des bases\n Par défaut, la base A est utilisée\n Les images renvoyées sont toujours sous le même format numpy\n \"\"\"\n if n < 0 or n > 9:\n raise MauvaisChiffre\n if bD == 'A':\n index_temp = np.where(self.categA[0] == n)[0]\n images_temp = np.zeros((len(index_temp), 784))\n j = 0\n for i in index_temp:\n images_temp[j] = self.imagesA[i]\n j+=1\n return images_temp\n\n if bD == 'B':\n index_temp = np.where(self.categB[0] == n)[0]\n images_temp = np.zeros((len(index_temp), 784))\n j = 0\n for i in index_temp:\n images_temp[j] = self.imagesB[i]\n j+=1\n return images_temp\n\n else:\n raise MauvaiseBD\n\n\n\n\n\n\n\n\n\n \n\n","sub_path":"Algorithme distances/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"635428234","text":"#welcome screen\r\ndef introduction():\r\n for i in range(0,44):print(\"*\",end=\"*\")\r\n print(\"\\n..................................WELCOME TO THE QUIZ...............................\")\r\n print(\"\\n***************************** INSTRUCTIONS TO PLAY THE GAME*************************\")\r\n print(\"\\n 1) enter your name \")\r\n print(\"\\n 2) enter the subject number you want to attempt (1,2,3,4 or 5) .\")\r\n print(\"\\n 3) your quiz will began and now play the game.\")\r\n print(\"\\n 4) total 10 questions will be displayed(one by one) along with there four options \")\r\n print(\"\\n 5) you have to choose any one option among the four given options\")\r\n print(\"\\n 6) at the last you will be rewarded marks according to your correct answers\")\r\ndef welcome():\r\n for i in range(0,37):print(\"*\",end=\"*\")\r\n print(\"\\n..................................WELCOME TO THE QUIZ...............................\")\r\n pname=input(\"enter your name : \")\r\n print(\" choose any one subject (1 or 2 or 3 or 4 or 5) :\")\r\n print(\" 1)General Knowledge \")\r\n print(\" 2)Computer \")\r\n print(\" 3)Geography \")\r\n print(\" 4)History \")\r\n print(\" 5)English \")\r\n ch=input()\r\n if ch=='1':\r\n GK()\r\n elif ch=='2':\r\n Computer()\r\n elif ch=='3':\r\n Geography()\r\n elif ch=='4':\r\n History()\r\n elif ch=='5':\r\n English()\r\n else:\r\n print(pname,\"good bye , Have a nice day \")\r\n exit(0)\r\n for i in range(0,37):print(\"*\",end=\"*\")\r\n print(\"\\n\")\r\n\r\ndef GK():\r\n q=[]\r\n q.append(\"Q1) The Plaka is the oldest quarter of which city?\")\r\n \r\n \r\n q.append(\"Q2) What is an axolotl?\")\r\n \r\n\r\n q.append(\"Q3) The Panama Canal was officially opened by which US president?\")\r\n \r\n \r\n q.append(\"Q4) In which opera did Maria Callas make her last appearance at Covent Garden?\")\r\n \r\n \r\n q.append(\"Q5) After Adam, Eve, Cain and Abel who is the next person mentioned in the Bible?\")\r\n \r\n \r\n q.append(\"Q6) India is a federal union comprising twenty-nine states and how many union territories?\")\r\n \r\n \r\n q.append(\"Q7) Which of the following is the capital of Arunachal Pradesh?\")\r\n \r\n \r\n q.append(\"Q8) What are the major languages spoken in Andhra Pradesh?\")\r\n \r\n \r\n q.append(\"Q9) What is the state flower of Haryana?\")\r\n \r\n \r\n q.append(\"Q10)Which of the following states is not located in the North?\")\r\n \r\n \r\n\r\n\r\n opta=[]\r\n opta.append(\"a) Athens\")\r\n opta.append(\"a) A nerve in the brain\")\r\n opta.append(\"a) Calvin Coolidge \")\r\n opta.append(\"a) Carmen \")\r\n opta.append(\"a) Enoch \")\r\n opta.append(\"a) 6\")\r\n opta.append(\"a) Itanagar\")\r\n opta.append(\"a) English and Telugu\")\r\n opta.append(\"a) Lotus\")\r\n opta.append(\"a) Jharkhand\")\r\n \r\n\r\n optb=[]\r\n optb.append(\"b) Prague\")\r\n optb.append(\"b) A multi-axled vehicle\")\r\n optb.append(\"b) Herbert Hoover\")\r\n optb.append(\"b) Tosca\")\r\n optb.append(\"b) Jubal\")\r\n optb.append(\"b) 7\")\r\n optb.append(\"b) Dispur\")\r\n optb.append(\"b) Telugu and Urdu\")\r\n optb.append(\"b) Rhododendron\")\r\n optb.append(\"b) Jammu and Kashmir\")\r\n \r\n\r\n optc=[]\r\n optc.append(\"c) Rome\")\r\n optc.append(\"c) A type of mortice lock\")\r\n optc.append(\"c) Theodore Roosevelt\")\r\n optc.append(\"c) Madame Butterfly\")\r\n optc.append(\"c) Lamech\")\r\n optc.append(\"c) 8\")\r\n optc.append(\"c) Imphal\")\r\n optc.append(\"c) Telugu and Kannada\")\r\n optc.append(\"c) Golden Shower\")\r\n optc.append(\"c) Himachal Pradesh\")\r\n \r\n\r\n optd=[]\r\n optd.append(\"d) Vienna \\n\")\r\n optd.append(\"d) A species of salamander \\n\")\r\n optd.append(\"d) Woodrow Wilson \\n\")\r\n optd.append(\"d) La Boheme \\n\")\r\n optd.append(\"d) Zillah \\n\")\r\n optd.append(\"d) 9 \\n\")\r\n optd.append(\"d) Panaji \\n\")\r\n optd.append(\"d) All of the above languages \\n\")\r\n optd.append(\"d) Not declared \\n\")\r\n optd.append(\"d) Haryana \\n\")\r\n \r\n\r\n keys=[\"a\",\"d\",\"d\",\"b\",\"a\",\"b\",\"a\",\"b\",\"a\",\"a\"]\r\n \r\n print(\"your questions are as follows : \")\r\n score=0\r\n for i in range(0,10):\r\n print(q[i])\r\n print(opta[i])\r\n print(optb[i])\r\n print(optc[i])\r\n print(optd[i])\r\n print(\"enter the correct option(a,b,c,d) : \")\r\n ans=input()\r\n if ans==keys[i]:\r\n score+=1\r\n print(\"yous score out of 10 is: \",score)\r\n\r\ndef Computer():\r\n q=[]\r\n q.append(\"Q1) A light sensitive decide that converts drawing, printed test or othr images in digital form is?\")\r\n \r\n \r\n q.append(\"Q2) Which protocal provides e-mail facility among different hosts?\")\r\n \r\n\r\n q.append(\"Q3) The basic architecture of computer was developed by?\")\r\n \r\n \r\n q.append(\"Q4) In how many generations a computer can be classified?\")\r\n \r\n \r\n q.append(\"Q5) Fifth generation computer are based on?\")\r\n \r\n \r\n q.append(\"Q6) GUI stands for?\")\r\n \r\n \r\n q.append(\"Q7) Time during which a job is processed by a computer?\")\r\n \r\n \r\n q.append(\"Q8) The size of commanly used floppy disk?\")\r\n \r\n \r\n q.append(\"Q9) Which one of the following is not the application software?\")\r\n \r\n \r\n q.append(\"Q10)Which of the following circuit used as a memory device in computer?\")\r\n\r\n\r\n opta=[]\r\n opta.append(\"a) Keyboard\")\r\n opta.append(\"a) FTP\")\r\n opta.append(\"a) John Von Neumann \")\r\n opta.append(\"a) 3 \")\r\n opta.append(\"a) Artificial Intelligence \")\r\n opta.append(\"a) Graph use interface\")\r\n opta.append(\"a) Execution time\")\r\n opta.append(\"a) 4.5\")\r\n opta.append(\"a) Red Hat Linux\")\r\n opta.append(\"a) Rectifiers\")\r\n \r\n\r\n optb=[]\r\n optb.append(\"b) Clotter\")\r\n optb.append(\"b) SMTP\")\r\n optb.append(\"b) Charles Babbage\")\r\n optb.append(\"b) 4\")\r\n optb.append(\"b) Programming Intelligence\")\r\n optb.append(\"b) Graphical universal interface\")\r\n optb.append(\"b) Delay time\")\r\n optb.append(\"b) 3.5\")\r\n optb.append(\"b) MS Office\")\r\n optb.append(\"b) Flip Flop\")\r\n \r\n\r\n optc=[]\r\n optc.append(\"c) Scanner\")\r\n optc.append(\"c) TELNET\")\r\n optc.append(\"c) Blaise Pascal\")\r\n optc.append(\"c) 5\")\r\n optc.append(\"c) System Knowledge\")\r\n optc.append(\"c) Graphical user interface\")\r\n optc.append(\"c) Real time\")\r\n optc.append(\"c) 3.25\")\r\n optc.append(\"c) Open Office\")\r\n optc.append(\"c) Comparators\")\r\n \r\n\r\n optd=[]\r\n optd.append(\"d) OMR \\n\")\r\n optd.append(\"d) SNMP \\n\")\r\n optd.append(\"d) Garden Moore \\n\")\r\n optd.append(\"d) 6 \\n\")\r\n optd.append(\"d) None of these \\n\")\r\n optd.append(\"d) All of these \\n\")\r\n optd.append(\"d) Waiting time \\n\")\r\n optd.append(\"d) 5.5 \\n\")\r\n optd.append(\"d) Adobe pagemaker \\n\")\r\n optd.append(\"d) Attenuator \\n\")\r\n \r\n keys=[\"c\",\"b\",\"a\",\"c\",\"a\",\"c\",\"b\",\"a\",\"b\",\"a\"]\r\n \r\n print(\"your questions are as follows : \")\r\n score=0\r\n for i in range(0,10):\r\n print(q[i])\r\n print(opta[i])\r\n print(optb[i])\r\n print(optc[i])\r\n print(optd[i])\r\n print(\"enter the correct option(a,b,c,d) : \")\r\n ans=input()\r\n if ans==keys[i]:\r\n score+=1\r\n print(\"yous score out of 10 is: \",score)\r\n\r\n\r\n\r\ndef Geography():\r\n q=[]\r\n q.append(\"Q1) Study of the universe known as?\")\r\n \r\n \r\n q.append(\"Q2) Approximately how many galaxies are there?\")\r\n \r\n\r\n q.append(\"Q3) Big Bang Theory explains?\")\r\n \r\n \r\n q.append(\"Q4) Big Bang was an explosion that occurs?\")\r\n \r\n \r\n q.append(\"Q5) Which planet is known as dwarf planet ?\")\r\n \r\n \r\n q.append(\"Q6) Diameter of sun is?\")\r\n \r\n \r\n q.append(\"Q7) Which are the main gases present in sun?\")\r\n \r\n \r\n q.append(\"Q8) Surface temperature of sun is about?\")\r\n \r\n \r\n q.append(\"Q9) Diameter of moon is?\")\r\n\r\n \r\n q.append(\"Q10)Titan is satellite of?\")\r\n\r\n\r\n opta=[]\r\n opta.append(\"a) Sociology\")\r\n opta.append(\"a) 10 Billion\")\r\n opta.append(\"a) Origin of Universe\")\r\n opta.append(\"a) 10 billion years ago\")\r\n opta.append(\"a) mercury\")\r\n opta.append(\"a) 12 lakhs kms\")\r\n opta.append(\"a) Hydrogen and Helium\")\r\n opta.append(\"a) 5000 degree celcius\")\r\n opta.append(\"a) 3375 km\")\r\n opta.append(\"a) Mercury \")\r\n \r\n\r\n optb=[]\r\n optb.append(\"b) Cosmology\")\r\n optb.append(\"b) 100 Billion\")\r\n optb.append(\"b) Origin of sun\")\r\n optb.append(\"b) 15 billion years ago\")\r\n optb.append(\"b) pluto\")\r\n optb.append(\"b) 13 lakhs kms\")\r\n optb.append(\"b) Hydrogen and Argon\")\r\n optb.append(\"b) 5005 degree celcius\")\r\n optb.append(\"b) 3415 km\")\r\n optb.append(\"b) Earth\")\r\n \r\n\r\n optc=[]\r\n optc.append(\"c) Universology\")\r\n optc.append(\"c) 1000 Billion\")\r\n optc.append(\"c) Laws of physics\")\r\n optc.append(\"c) 20 billion years ago\")\r\n optc.append(\"c) mars\")\r\n optc.append(\"c) 14 lakhs kms\")\r\n optc.append(\"c) Argon and Helium\")\r\n optc.append(\"c) 6000 degree celcius\")\r\n optc.append(\"c) 3425 km\")\r\n optc.append(\"c) Venus\")\r\n \r\n\r\n optd=[]\r\n optd.append(\"d) Petology \\n\")\r\n optd.append(\"d) 10000 Billion \\n\")\r\n optd.append(\"d) None of the above \\n\")\r\n optd.append(\"d) 25 billion years ago \\n\")\r\n optd.append(\"d) uranus \\n\")\r\n optd.append(\"d) 15 lakhs kms \\n\")\r\n optd.append(\"d) Hydrogen and Carbon Diaoxide \\n\")\r\n optd.append(\"d) 6005 degree celcius \\n\")\r\n optd.append(\"d) 3475 km \\n\")\r\n optd.append(\"d) Saturn \\n\")\r\n \r\n keys=[\"b\",\"b\",\"a\",\"b\",\"b\",\"c\",\"a\",\"b\",\"d\",\"d\"]\r\n \r\n print(\"your questions are as follows : \")\r\n score=0\r\n for i in range(0,10):\r\n print(q[i])\r\n print(opta[i])\r\n print(optb[i])\r\n print(optc[i])\r\n print(optd[i])\r\n print(\"enter the correct option(a,b,c,d) : \")\r\n ans=input()\r\n if ans==keys[i]:\r\n score+=1\r\n print(\"yous score out of 10 is: \",score)\r\n\r\n\r\ndef History():\r\n q=[]\r\n q.append(\"Q1) What was the time period of Indus Civilization/Harappan Civilization?\")\r\n \r\n \r\n q.append(\"Q2) Which was the largest site of Indus Civilization?\")\r\n \r\n\r\n q.append(\"Q3) Which is the largest indin site of Indus Civilization?\")\r\n \r\n \r\n q.append(\"Q4) Which two indus sites found in Afghanistan?\")\r\n \r\n \r\n q.append(\"Q5) Which was the ancient port of Indus Civilization?\")\r\n \r\n \r\n q.append(\"Q6) Which is the oldest text in the world?\")\r\n \r\n \r\n q.append(\"Q7) How many Mandalas Rig Veda contains?\")\r\n \r\n \r\n q.append(\"Q8) Which veda is important for Indian Music?\")\r\n \r\n \r\n q.append(\"Q9) Ajatasatru was son of?\")\r\n \r\n \r\n q.append(\"Q10)The Shisunaga Dynasty was overthrown by?\")\r\n\r\n\r\n opta=[]\r\n opta.append(\"a) 2400BC-1700BC\")\r\n opta.append(\"a) Mohenjodaro\")\r\n opta.append(\"a) Mohenjodaro\")\r\n opta.append(\"a) Lothal and Daimabad\")\r\n opta.append(\"a) Harappa\")\r\n opta.append(\"a) Yajur Veda\")\r\n opta.append(\"a) 9 Mandalas\")\r\n opta.append(\"a) Sama Veda\")\r\n opta.append(\"a) Bimisara\")\r\n opta.append(\"a) Bimisara\")\r\n \r\n\r\n optb=[]\r\n optb.append(\"b) 2400BC-1750BC\")\r\n optb.append(\"b) Lothal\")\r\n optb.append(\"b) Lothal\")\r\n optb.append(\"b) Shatughai and Dainabad\")\r\n optb.append(\"b) Lothal\")\r\n optb.append(\"b) Atharva Veda\")\r\n optb.append(\"b) 10 Mandalas\")\r\n optb.append(\"b) Yajur Veda\")\r\n optb.append(\"b) Udayin\")\r\n optb.append(\"b) Ajatashatru\")\r\n \r\n\r\n optc=[]\r\n optc.append(\"c) 2500BC-1700BC\")\r\n optc.append(\"c) Chanhudaro\")\r\n optc.append(\"c) Chanhudaro\")\r\n optc.append(\"c) Shatughai and Dainabad\")\r\n optc.append(\"c) Dholavira\")\r\n optc.append(\"c) Rig Veda\")\r\n optc.append(\"c) 11 Mandalas\")\r\n optc.append(\"c) Atharva Veda\")\r\n optc.append(\"c) Shisunaga\")\r\n optc.append(\"c) Mahapadma\")\r\n \r\n\r\n optd=[]\r\n optd.append(\"d) 2500BC-1750BC \\n\")\r\n optd.append(\"d) Dholavira \\n\")\r\n optd.append(\"d) Dholavira \\n\")\r\n optd.append(\"d) Mundigaq and Dainabad \\n\")\r\n optd.append(\"d) Surkotada \\n\")\r\n optd.append(\"d) Sama Veda \\n\")\r\n optd.append(\"d) 12 Mandalas \\n\")\r\n optd.append(\"d) Rig Veda \\n\")\r\n optd.append(\"d) None of these \\n\")\r\n optd.append(\"d) Chandragupta Maurya \\n\")\r\n \r\n keys=[\"d\",\"a\",\"d\",\"c\",\"b\",\"c\",\"b\",\"a\",\"a\",\"c\"]\r\n \r\n print(\"your questions are as follows : \")\r\n score=0\r\n for i in range(0,10):\r\n print(q[i])\r\n print(opta[i])\r\n print(optb[i])\r\n print(optc[i])\r\n print(optd[i])\r\n print(\"enter the correct option(a,b,c,d) : \")\r\n ans=input()\r\n if ans==keys[i]:\r\n score+=1\r\n print(\"yous score out of 10 is: \",score)\r\n\r\n\r\n\r\ndef English():\r\n q=[]\r\n q.append(\"Q1) Extreme old age when a man behaves like a fool?\")\r\n \r\n \r\n q.append(\"Q2) That which cannot be corrected?\")\r\n \r\n\r\n q.append(\"Q3) The study of ancient society?\")\r\n \r\n \r\n q.append(\"Q4) A person of good understanding knowledge and resoning power?\")\r\n \r\n \r\n q.append(\"Q5) A person who insists on something?\")\r\n \r\n \r\n q.append(\"Q6) State in which the few govern the many?\")\r\n \r\n \r\n q.append(\"Q7) List of the business or subjects to be considered at a meeting?\")\r\n \r\n \r\n q.append(\"Q8) Leave or remove from a place considered dangerous?\")\r\n \r\n \r\n q.append(\"Q9) A person pretending to be somebody he is not?\")\r\n\r\n \r\n q.append(\"Q10)A person who knows many foreign languages?\")\r\n\r\n\r\n opta=[]\r\n opta.append(\"a) Imbecility\")\r\n opta.append(\"a) Unintelligible\")\r\n opta.append(\"a) Anthropology\")\r\n opta.append(\"a) Expert\")\r\n opta.append(\"a) Disciplinarian\")\r\n opta.append(\"a) Monarchy\")\r\n opta.append(\"a) Schedule\")\r\n opta.append(\"a) Evacuate\")\r\n opta.append(\"a) Magician\")\r\n opta.append(\"a) Linguist\")\r\n \r\n\r\n optb=[]\r\n optb.append(\"b) Youth\")\r\n optb.append(\"b) Indelible\")\r\n optb.append(\"b) Archaeology\")\r\n optb.append(\"b) Intellectual\")\r\n optb.append(\"b) Stickler\")\r\n optb.append(\"b) Oligarchy\")\r\n optb.append(\"b) Timetable\")\r\n optb.append(\"b) Evade\")\r\n optb.append(\"b) Rogue\")\r\n optb.append(\"b) Grammarian\")\r\n \r\n\r\n optc=[]\r\n optc.append(\"c) Dotage\")\r\n optc.append(\"c) Illegible\")\r\n optc.append(\"c) History\")\r\n optc.append(\"c) Snob\")\r\n optc.append(\"c) Instantaneous\")\r\n optc.append(\"c) Plutocracy\")\r\n optc.append(\"c) Agenda\")\r\n optc.append(\"c) Avoid\")\r\n optc.append(\"c) Liar\")\r\n optc.append(\"c) Polyglot\")\r\n \r\n\r\n optd=[]\r\n optd.append(\"d) Superannuation \\n\")\r\n optd.append(\"d) Incorrigible \\n\")\r\n optd.append(\"d) Ethnology \\n\")\r\n optd.append(\"d) Literate \\n\")\r\n optd.append(\"d) Boaster \\n\")\r\n optd.append(\"d) Autocracy \\n\")\r\n optd.append(\"d) Plan \\n\")\r\n optd.append(\"d) Exterminate \\n\")\r\n optd.append(\"d) Impostor \\n\")\r\n optd.append(\"d) Bilingual \\n\")\r\n \r\n keys=[\"c\",\"d\",\"b\",\"b\",\"b\",\"b\",\"c\",\"a\",\"d\",\"a\"]\r\n \r\n print(\"your questions are as follows : \")\r\n score=0\r\n for i in range(0,10):\r\n print(q[i])\r\n print(opta[i])\r\n print(optb[i])\r\n print(optc[i])\r\n print(optd[i])\r\n print(\"enter the correct option(a,b,c,d) : \")\r\n ans=input()\r\n if ans==keys[i]:\r\n score+=1\r\n print(\"yous score out of 10 is: \",score)\r\n\r\ndef main():\r\n introduction()\r\n def cls():\r\n print('\\n'*6)\r\n cls() \r\n welcome()\r\n print(\"\\n Do you want to continue 'Y' or 'N'? \\n\")\r\n print(\"Enter your choice \")\r\n c=input()\r\n if c=='Y' or c=='y':\r\n welcome()\r\n else:\r\n print(\"THANK YOU FOR PLAYING THE GAME \")\r\n exit(0)\r\n print(\"\\n\"*15)\r\n \r\n\r\nif __name__==\"__main__\":\r\n main()\r\n \r\n\r\n\r\n \r\n \r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n","sub_path":"QUIZ FINAL.py","file_name":"QUIZ FINAL.py","file_ext":"py","file_size_in_byte":14589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"469830856","text":"#=====================================================\n# import modules\n#=====================================================\n# os\nimport os\n\n#import netCDF4\nfrom netCDF4 import Dataset as netcdf_dataset\n\n# cartopy\n#import cartopy.crs as ccrs\n#from cartopy.mpl.geoaxes import GeoAxes\n#from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n#from cartopy.util import add_cyclic_point\n\n# matplotlib\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import AxesGrid\nimport matplotlib.colors as colors\nimport matplotlib.collections as collections\n\n# numpy\nimport numpy as np\n\n# scipy\nfrom scipy import stats\n\n# parameters\nfrom get_parameters import get_area_mean_min_max\n\n#------------------\n# start here\n#------------------\n\n# data path\nctl_name=\"CTL\" #os.environ[\"ctl_name\"]\nexp_name=\"TSIS\" #os.environ[\"exp_name\"]\nctl_pref=\"solar_CTL_cesm211_ETEST-f19_g17-ens_mean_2010-2019\"\nexp_pref=\"solar_TSIS_cesm211_ETEST-f19_g17-ens_mean_2010-2019\"\n\nfpath_ctl=\"/raid00/xianwen/cesm211_solar/\"+ctl_pref+\"/climo/\"\nfpath_exp=\"/raid00/xianwen/cesm211_solar/\"+exp_pref+\"/climo/\"\n \nyears=np.arange(2010,2020) \n#months_all=[\"01\",\"02\",\"03\",\"04\",\"05\",\"06\",\"07\",\"08\",\"09\",\"10\",\"11\",\"12\"]\n\nfigure_name=\"fig3d_zonal_sfc_net_uv+vis_nir_ANN_diff\"\nunits=r\"Wm$^-$$^2$\"\n\nvarnms_vis_dn=np.array([\"FSSDS13\",\"FSSDS12\",\"FSSDS11\",\"FSSDS10\",\"FSSDS09\"])\nvarnms_nir_dn=np.array([\"FSSDS08\",\"FSSDS07\",\"FSSDS06\",\"FSSDS05\",\"FSSDS04\",\\\n \"FSSDS03\",\"FSSDS02\",\"FSSDS01\",\"FSSDS14\"])\n\nvarnms_vis_up=np.array([\"FSSUS13\",\"FSSUS12\",\"FSSUS11\",\"FSSUS10\",\"FSSUS09\"])\nvarnms_nir_up=np.array([\"FSSUS08\",\"FSSUS07\",\"FSSUS06\",\"FSSUS05\",\"FSSUS04\",\\\n \"FSSUS03\",\"FSSUS02\",\"FSSUS01\",\"FSSUS14\"])\n\n# define empty variables to save the zonal annual means -->\nnlat=np.int64(96)\nmeans_yby_ctl_dn=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_yby_exp_dn=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_ctl_dn=np.zeros((2,nlat)) #multi-year mean for each variable\nmeans_exp_dn=np.zeros((2,nlat)) #multi-year mean for each variable\ndiffs_dn=np.zeros((2,nlat)) #multi-year exp-ctl diff for each variable\ngm_yby_ctl_dn=np.zeros((2,years.size)) #year by year mean for each variable\ngm_yby_exp_dn=np.zeros((2,years.size)) #year by year mean for each variable\n\nmeans_yby_ctl_up=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_yby_exp_up=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_ctl_up=np.zeros((2,nlat)) #multi-year mean for each variable\nmeans_exp_up=np.zeros((2,nlat)) #multi-year mean for each variable\ndiffs_up=np.zeros((2,nlat)) #multi-year exp-ctl diff for each variable\ngm_yby_ctl_up=np.zeros((2,years.size)) #year by year mean for each variable\ngm_yby_exp_up=np.zeros((2,years.size)) #year by year mean for each variable\n\nmeans_yby_ctl_net=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_yby_exp_net=np.zeros((years.size,2,nlat)) #year by year mean for each variable\nmeans_ctl_net=np.zeros((2,nlat)) #multi-year mean for each variable\nmeans_exp_net=np.zeros((2,nlat)) #multi-year mean for each variable\ndiffs_net=np.zeros((2,nlat)) #multi-year exp-ctl diff for each variable\ngm_yby_ctl_net=np.zeros((2,years.size)) #year by year mean for each variable\ngm_yby_exp_net=np.zeros((2,years.size)) #year by year mean for each variable\n\nmeans_yby_exp_fice=np.zeros((years.size,nlat)) #year by year mean for each variable\nmeans_exp_fice=np.zeros((nlat)) #multi-year mean for each variable\n\nfor iy in range(0,years.size): \n # open data file\n fctl=fpath_ctl+ctl_pref+\"_ANN_\"+str(years[iy])+\".nc\"\n fexp=fpath_exp+exp_pref+\"_ANN_\"+str(years[iy])+\".nc\"\n file_ctl=netcdf_dataset(fctl,\"r\")\n file_exp=netcdf_dataset(fexp,\"r\")\n \n # read lat and lon\n lat=file_ctl.variables[\"lat\"]\n lon=file_ctl.variables[\"lon\"]\n \n means_yby_exp_fice[iy,:]=means_yby_exp_fice[iy,:] + \\\n np.mean(file_exp.variables[\"ICEFRAC\"][0,:,:],axis=1) \n\n # read data and calculate mean/min/max\n for vn in varnms_vis_dn:\n dtctl_dn=file_ctl.variables[vn][0,:,:]\n dtexp_dn=file_exp.variables[vn][0,:,:] \n means_yby_ctl_dn[iy,0,:]= means_yby_ctl_dn[iy,0,:] + np.mean(dtctl_dn[:,:],axis=1)\n means_yby_exp_dn[iy,0,:]= means_yby_exp_dn[iy,0,:] + np.mean(dtexp_dn[:,:],axis=1)\n gm_yby_ctl_dn[0,iy]=gm_yby_ctl_dn[0,iy]+get_area_mean_min_max(dtctl_dn[:,:],lat[:])[0]\n gm_yby_exp_dn[0,iy]=gm_yby_exp_dn[0,iy]+get_area_mean_min_max(dtexp_dn[:,:],lat[:])[0]\n\n for vn in varnms_nir_dn:\n dtctl_dn=file_ctl.variables[vn][0,:,:]\n dtexp_dn=file_exp.variables[vn][0,:,:] \n means_yby_ctl_dn[iy,1,:]= means_yby_ctl_dn[iy,1,:] + np.mean(dtctl_dn[:,:],axis=1)\n means_yby_exp_dn[iy,1,:]= means_yby_exp_dn[iy,1,:] + np.mean(dtexp_dn[:,:],axis=1)\n gm_yby_ctl_dn[1,iy]=gm_yby_ctl_dn[1,iy]+get_area_mean_min_max(dtctl_dn[:,:],lat[:])[0]\n gm_yby_exp_dn[1,iy]=gm_yby_exp_dn[1,iy]+get_area_mean_min_max(dtexp_dn[:,:],lat[:])[0]\n \n for vn in varnms_vis_up:\n dtctl_up=file_ctl.variables[vn][0,:,:]\n dtexp_up=file_exp.variables[vn][0,:,:] \n means_yby_ctl_up[iy,0,:]= means_yby_ctl_up[iy,0,:] + np.mean(dtctl_up[:,:],axis=1)\n means_yby_exp_up[iy,0,:]= means_yby_exp_up[iy,0,:] + np.mean(dtexp_up[:,:],axis=1)\n gm_yby_ctl_up[0,iy]=gm_yby_ctl_up[0,iy]+get_area_mean_min_max(dtctl_up[:,:],lat[:])[0]\n gm_yby_exp_up[0,iy]=gm_yby_exp_up[0,iy]+get_area_mean_min_max(dtexp_up[:,:],lat[:])[0]\n\n for vn in varnms_nir_up:\n dtctl_up=file_ctl.variables[vn][0,:,:]\n dtexp_up=file_exp.variables[vn][0,:,:] \n means_yby_ctl_up[iy,1,:]= means_yby_ctl_up[iy,1,:] + np.mean(dtctl_up[:,:],axis=1)\n means_yby_exp_up[iy,1,:]= means_yby_exp_up[iy,1,:] + np.mean(dtexp_up[:,:],axis=1)\n gm_yby_ctl_up[1,iy]=gm_yby_ctl_up[1,iy]+get_area_mean_min_max(dtctl_up[:,:],lat[:])[0]\n gm_yby_exp_up[1,iy]=gm_yby_exp_up[1,iy]+get_area_mean_min_max(dtexp_up[:,:],lat[:])[0]\n means_yby_ctl_net[iy,:,:]=means_yby_ctl_dn[iy,:,:]-means_yby_ctl_up[iy,:,:]\n means_yby_exp_net[iy,:,:]=means_yby_exp_dn[iy,:,:]-means_yby_exp_up[iy,:,:]\n gm_yby_ctl_net[:,iy]=gm_yby_ctl_dn[:,iy]-gm_yby_ctl_up[:,iy]\n gm_yby_exp_net[:,iy]=gm_yby_exp_dn[:,iy]-gm_yby_exp_up[:,iy]\n\n# compute multi-year mean and ttest\nsiglev=0.05\nmeans_ctl_dn=np.mean(means_yby_ctl_dn,axis=0)\nmeans_exp_dn=np.mean(means_yby_exp_dn,axis=0)\ndiffs_dn=means_exp_dn-means_ctl_dn\nttest=stats.ttest_ind(means_yby_ctl_dn,means_yby_exp_dn,axis=0)\npvalues_dn=ttest.pvalue\ndiffs_sig_dn=np.zeros(diffs_dn.shape)\ndiffs_sig_dn[:,:]=np.nan\n\nmeans_ctl_up=np.mean(means_yby_ctl_up,axis=0)\nmeans_exp_up=np.mean(means_yby_exp_up,axis=0)\ndiffs_up=means_exp_up-means_ctl_up\nttest=stats.ttest_ind(means_yby_ctl_up,means_yby_exp_up,axis=0)\npvalues_up=ttest.pvalue\ndiffs_sig_up=np.zeros(diffs_up.shape)\ndiffs_sig_up[:,:]=np.nan\n\nmeans_ctl_net=np.mean(means_yby_ctl_net,axis=0)\nmeans_exp_net=np.mean(means_yby_exp_net,axis=0)\ndiffs_net=means_exp_net-means_ctl_net\nttest=stats.ttest_ind(means_yby_ctl_net,means_yby_exp_net,axis=0)\npvalues_net=ttest.pvalue\ndiffs_sig_net=np.zeros(diffs_net.shape)\ndiffs_sig_net[:,:]=np.nan\n\nmeans_exp_fice=np.mean(means_yby_exp_fice,axis=0)\n\nzeros=np.zeros(diffs_dn.shape)\n\n\nfor iv in range(pvalues_up.shape[0]):\n for ip in range(pvalues_up.shape[1]):\n if pvalues_up[iv,ip] < siglev:\n diffs_sig_up[iv,ip]=diffs_up[iv,ip]\n #else:\n # diffs_unsig[iv,ip]=diffs[iv,ip]\n\nfor iv in range(pvalues_dn.shape[0]):\n for ip in range(pvalues_dn.shape[1]):\n if pvalues_dn[iv,ip] < siglev:\n diffs_sig_dn[iv,ip]=diffs_dn[iv,ip]\n #else:\n # diffs_unsig[iv,ip]=diffs[iv,ip]\n\nfor iv in range(pvalues_net.shape[0]):\n for ip in range(pvalues_net.shape[1]):\n if pvalues_net[iv,ip] < siglev:\n diffs_sig_net[iv,ip]=diffs_net[iv,ip]\n #else:\n # diffs_unsig[iv,ip]=diffs[iv,ip]\n\n#----------------\n# make the plot\n#----------------\n\nfig=plt.figure(figsize=(7,4))\n\n#ax1=fig.add_axes([0.14,0.12,0.8,0.72])\n#ax1.plot(lat[:],means_ctl_dn[0,:],color=\"k\",lw=2,ls=\"-\",label=\"UV+VIS down\")\n#ax1.plot(lat[:],means_ctl_dn[1,:],color=\"r\",lw=2,ls=\"-\",label=\"NIR down\")\n#ax1.plot(lat[:],means_ctl_up[0,:],color=\"g\",lw=2,ls=\"-\",label=\"UV+VIS up\")\n#ax1.plot(lat[:],means_ctl_up[1,:],color=\"darkorchid\",lw=2,ls=\"-\",label=\"NIR up\")\n#ax1.legend(fontsize=8)\n#ax1.set_title(\"SFC Fluxes (CESM2)\",fontsize=14)\n#ax1.set_ylabel(units,fontsize=14)\n#ax1.set_xlim(-90,90)\n#ax1.set_ylim(-4,160)\n#plt.xticks(fontsize=12)\n#plt.yticks(fontsize=12)\n\nax2=fig.add_axes([0.14,0.15,0.8,0.72])\nax2.plot(lat[:],diffs_sig_dn[0,:],color=\"k\",lw=6,alpha=0.5)\nax2.plot(lat[:],diffs_sig_dn[1,:],color=\"red\",lw=6,alpha=0.5)\nax2.plot(lat[:],diffs_sig_up[0,:],color=\"yellowgreen\",lw=6,alpha=0.9)\nax2.plot(lat[:],diffs_sig_up[1,:],color=\"plum\",lw=6,alpha=1.0)\nax2.plot(lat[:],diffs_sig_net[0,:],color=\"blue\",lw=6,alpha=0.7)\nax2.plot(lat[:],diffs_sig_net[1,:],color=\"orange\",lw=6,alpha=1.0)\n\nax2.plot(lat[:],diffs_dn[0,:],color=\"k\",lw=1,label=\"\\u0394UV+VIS down\")\nax2.plot(lat[:],diffs_dn[1,:],color=\"r\",lw=1,label=\"\\u0394NIR down\")\nax2.plot(lat[:],diffs_up[0,:],color=\"g\",lw=1,ls=\"-\",label=\"\\u0394UV+VIS up\")\nax2.plot(lat[:],diffs_up[1,:],color=\"darkorchid\",lw=1,ls=\"-\",label=\"\\u0394NIR up\")\nax2.plot(lat[:],diffs_net[0,:],color=\"k\",lw=2,ls=\"--\",label=\"\\u0394UV+VIS net\")\nax2.plot(lat[:],diffs_net[1,:],color=\"r\",lw=2,ls=\"--\",label=\"\\u0394NIR net\")\nax2.plot(lat[:],zeros[0,:],color=\"lightgray\",lw=1)\nax2.legend(fontsize=8)\nax2.set_title(\"Diff in SFC Flux (TSIS-1 - CESM2)\",fontsize=14) #+var_long_name,fontsize=12)\nax2.set_ylabel(units,fontsize=14)\nax2.set_xlabel(\"Latitude\",fontsize=14)\nax2.set_xlim(-90,90)\nax2.set_ylim(-1.6,2.15)\nplt.xticks(fontsize=12)\nplt.yticks(fontsize=12)\n\n# add shading \ncollection = collections.BrokenBarHCollection.span_where(lat[:], ymin=-1.6, ymax=2.15, \\\n where=means_exp_fice >0.1,facecolor='y',alpha=0.3)\nax2.add_collection(collection)\n\nplt.savefig(\"./figures/\"+figure_name+\".pdf\")\nplt.savefig(\"./figures/\"+figure_name+\".png\",dpi=150)\nplt.show()\n\nexit()\n","sub_path":"plots/plot_fig3d_zonal_mean_VIS_NIR_diff.py","file_name":"plot_fig3d_zonal_mean_VIS_NIR_diff.py","file_ext":"py","file_size_in_byte":10137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"469568042","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n__main__\n~~~~~~~~~~~~~~~~~~~\nRun a simulation of the interaction with a coffee machine.\n\"\"\"\n\nfrom src.controllers.controller import Controller\nimport sys\n\n\nif __name__ == '__main__':\n sys.argv.pop(0)\n beveragesOrdered = sys.argv #contains the list of beverages order\n\n controller = Controller( #default capacity of each container of the machine is created\n outlets=4,\n hot_water_capacity=200,\n hot_milk_capacity=200,\n ginger_syrup_capacity=50,\n sugar_syrup_capacity=50,\n tea_leaves_syrup_capacity=100,\n )\n controller.prepare_beverage(beveragesOrdered)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"591838141","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# **\n#\n# ============ #\n# MAIN_MONGODB #\n# ============ #\n# Class for accessing MongoDB\n#\n# @author ES\n# **\nimport os\n\nfrom data_manager.dao.mongo_dao import MongoDAO\nfrom es_common.utils.csv_helper import CSVHelper\nimport time\n\n\ndef main():\n mongodb = MongoDAO()\n success = mongodb.connect()\n print('Success = {}'.format(success))\n\n # delete_databases(mongodb, db_names = mongodb.get_all_databases())\n\n db_names = mongodb.get_all_databases()\n # print(\"DBs: {}\".format(db_names))\n for db_name in db_names:\n if 'Interaction_DB' in db_name:\n get_dialogues(mongodb, db_name)\n\n\ndef delete_databases(mongodb, db_names):\n for db_name in db_names:\n if 'HRE_' in db_name:\n mongodb.delete_database(db_name)\n print(\"Remaining DBs: {}\".format(mongodb.get_all_databases()))\n\n\ndef get_dialogues(mongodb, db_name):\n mongodb.set_database(db_name=db_name)\n dialogues = mongodb.get_dialogue_designs()\n # print(dialogues)\n\n # TODO\n write_blocks_as_csv(db_name, dialogues)\n read_csv_data(db_name)\n\n '''\n for dialog in dialogues:\n print(dialog.keys())\n print(dialog['_id'])\n print(dialog['communication_style'])\n print(dialog['blocks'][\"{}\".format(0)].keys())\n\n for x in range(len(dialog['blocks'])):\n print(x)\n print(dialog['blocks'][\"{}\".format(x)])\n '''\n\n\ndef write_blocks_as_csv(db_name, dialogues):\n main_fieldnames = ['_id', 'date', 'time', 'communication_style']\n blocks_fieldnames = ['block_number', 'name', 'message', 'execution_result',\n 'start_time', 'end_time', 'interaction_time']\n\n fieldnames = []\n fieldnames.extend(main_fieldnames)\n fieldnames.extend(blocks_fieldnames)\n\n csv_helper = CSVHelper()\n csv_helper.set_csv_writer(fieldnames=fieldnames, filename=create_filename(db_name))\n\n for dialog in dialogues:\n to_row = {'_id': dialog['_id'],\n 'date': time.strftime(\"%D\", time.localtime(dialog['time'])),\n 'time': time.strftime(\"%T\", time.localtime(dialog['time'])),\n 'communication_style': dialog['communication_style']}\n\n # get each block as a new row\n blocks = dialog['blocks']\n # row numbers are the keys\n for i in range(len(blocks.keys())):\n row = {}\n row.update(to_row)\n b = blocks['{}'.format(i)]\n\n row['block_number'] = i\n row['name'] = b['name']\n row['message'] = b['speech_act'][\"message\"]\n row['execution_result'] = b['execution_result']\n row['start_time'] = b['interaction_start_time']\n row['end_time'] = b['interaction_end_time']\n row['interaction_time'] = b['interaction_end_time'] - b['interaction_start_time']\n\n csv_helper.write(row)\n\n\ndef create_filename(db_name):\n cwd = \"{}/logs/csv\".format(os.getcwd())\n return '{}/{}_dialogBlocks'.format(cwd, db_name)\n\n\ndef read_csv_data(db_name):\n csv_helper = CSVHelper()\n data = csv_helper.read(directory=\"\", filename=create_filename(db_name))\n\n if data is not None and len(data) > 0:\n print(data[0])\n\n\ndef write_raw_blocks_as_csv(db_name, dialogues):\n main_fieldnames = ['_id', 'time', 'communication_style']\n blocks_fieldnames = []\n blocks_fieldnames.extend(dialogues[0]['blocks'][\"{}\".format(0)].keys())\n\n fieldnames = []\n fieldnames.extend(main_fieldnames)\n fieldnames.append('row')\n fieldnames.extend(blocks_fieldnames)\n\n csv_helper = CSVHelper()\n csv_helper.set_csv_writer(fieldnames=fieldnames, filename='{}_dialogBlocks'.format(db_name))\n\n for dialog in dialogues:\n to_row = {}\n for k in main_fieldnames:\n to_row[k] = dialog[k]\n\n # get each block as a new row\n blocks = dialog['blocks']\n # row numbers are the keys\n for i in range(len(blocks.keys())):\n row = {}\n row.update(to_row)\n row['row'] = i\n row.update(blocks['{}'.format(i)])\n csv_helper.write(row)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main_mongodb.py","file_name":"main_mongodb.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"328869241","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 22 23:36:16 2021\n\n@author: alest\n\"\"\"\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\n\nimport keras\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Conv2D, BatchNormalization, Activation\nfrom keras.layers import Input, Flatten\nfrom keras.layers import AveragePooling2D as Pooling\n# from keras.layers import MaxPooling2D as Pooling\n\nfrom keras.regularizers import l2\nfrom keras.models import Model\n\nimport keras.optimizers as opt\n\nfrom keras.callbacks import LearningRateScheduler\n\n\n\nx_train = np.load(\"x_train.npy\")\ny_train = np.load(\"y_train.npy\")\n\nx_train = np.expand_dims(x_train, axis=3)\n\nprint(np.shape(x_train))\nprint(np.shape(y_train))\n\ndef net_layer(inputs,\n num_filters=16,\n kernel_size=3,\n strides=1,\n activation='relu',\n batch_normalization=True,\n conv_first=False):\n\n conv = Conv2D(num_filters,\n kernel_size=kernel_size,\n strides=strides,\n padding='same',\n kernel_initializer='he_normal',\n kernel_regularizer=l2(1e-4))\n\n x = inputs\n if conv_first:\n x = conv(x)\n if batch_normalization:\n x = BatchNormalization()(x)\n if activation is not None:\n x = Activation(activation)(x)\n else:\n if batch_normalization:\n x = BatchNormalization()(x)\n if activation is not None:\n x = Activation(activation)(x)\n x = conv(x)\n \n # x = GaussianNoise(0.2)(x)\n return x\n\n\ndef dense_net(input_shape, depth, num_classes=10, k=1):\n filters = 8\n\n inputs = Input(shape=input_shape)\n x = net_layer(inputs=inputs, conv_first=True, num_filters=filters)\n\n for d in range(depth):\n if d != 0:\n x = net_layer(inputs=x,\n num_filters=filters,\n activation=None,\n kernel_size=1\n )\n x = Pooling(pool_size=2)(x)\n\n y = net_layer(inputs=x,\n num_filters=filters)\n layers = [x, y]\n for kn in range(k-1):\n l = keras.layers.add(layers)\n l = net_layer(inputs=l,\n num_filters=filters)\n layers.append(l)\n\n x = keras.layers.add(layers)\n x = net_layer(inputs=x,\n num_filters=filters)\n filters*=2\n\n x = Pooling(pool_size=2)(x)\n y = Flatten()(x)\n \n f = Dense(filters//2,\n activation='relu',\n kernel_initializer='he_normal')(y)\n \n outputs = Dense(num_classes,\n activation='softmax',\n kernel_initializer='he_normal')(f)\n\n model = Model(inputs=inputs, outputs=outputs)\n\n return model\n\n\n\n\n\nnum_classes = 38\ninput_shape = (71,71, 1)\n\n\n# model = dense_net(input_shape=input_shape, num_classes=num_classes, depth=5, k=4)\n\nmodel = keras.applications.Xception(\n include_top=True,\n weights=None,\n input_tensor=None,\n input_shape=input_shape,\n pooling=None,\n classes=num_classes,\n)\n\nmodel.summary()\n\nmodel.compile(loss='binary_crossentropy',\n optimizer=opt.Adam(learning_rate=1e-3),\n metrics=['accuracy'])\n\ndef lr_schedule(epoch):\n\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr\n\nlr_scheduler = LearningRateScheduler(lr_schedule)\n\nindex=np.arange(np.shape(x_train)[0])\nnp.random.shuffle(index)\n# print(index[0:20])\n\nx_train = x_train[index]\ny_train = y_train[index]\n\ndef data_prep(image):\n # image = np.array(image)\n \n noise = np.array(np.random.random((71,71,1))*(190*np.random.random()), dtype='uint8')\n mean = np.mean(image[:25, :25])\n if mean == 0:\n s = np.random.randint(5, 71)\n noise = np.array(np.random.random((s,s))*255, dtype='uint8')\n noise = cv2.resize(noise, dsize=(71,71))\n noise = np.expand_dims(noise, axis=2)\n \n image = np.clip(image/2+noise, 0, 255)\n \n image = cv2.GaussianBlur(image,(7,7),0)\n \n image = np.where(np.array(image, dtype='uint8')<127, 0, 255)\n \n image = np.expand_dims(np.array(image, dtype='float32'), axis=2)\n \n return image\n\ndatagen = ImageDataGenerator(\n rescale=1./255,\n validation_split=0.2,\n width_shift_range=0.1,\n height_shift_range=0.05,\n shear_range=10,\n rotation_range=15,\n horizontal_flip=False,\n vertical_flip=False,\n zoom_range=(0.8, 1.1),\n fill_mode='reflect',\n preprocessing_function=data_prep,\n )\n\n\nbatch_size=64\nepochs = 180\n\ntrain_generator = datagen.flow(x_train, y_train, batch_size=batch_size, subset='training')\ntest_generator = datagen.flow(x_train, y_train, batch_size=batch_size//4, subset='validation')\n\nhistory = model.fit(train_generator, steps_per_epoch=64,\n validation_data=test_generator, validation_steps=32,\n epochs=epochs, verbose=1, workers=4, \n callbacks=[lr_scheduler])\n\nmodel_json = model.to_json()\nwith open(\"model.json\", \"w\") as json_file:\n\t\tjson_file.write(model_json)\nmodel.save_weights(\"model.h5\")\n\n\n# Plot training & validation accuracy values\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()\n\n# Plot training & validation loss values\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Test'], loc='upper left')\nplt.show()","sub_path":"train_character_recognizer.py","file_name":"train_character_recognizer.py","file_ext":"py","file_size_in_byte":5971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"493601870","text":"## fron current dir\r\n#\r\n#import os\r\n#try:\r\n# for file in os.listdir():\r\n# print(file)\r\n# \r\n#except Exception as err:\r\n# print(err)\r\n#\r\n#\r\n##from D dir\r\n# \r\n#import os\r\n#path = \"C:\\\\\"\r\n#print(\"**********\",path,\"**********\")\r\n#try:\r\n# for file in os.listdir(path):\r\n# print(file)\r\n#except Exception as err:\r\n# print(err)\r\n \r\n \r\n ## displaying .py files from current path\r\n \r\n#import os\r\n#try:\r\n# for file in os.listdir():\r\n# if file.endswith(\".py\"):\r\n# print(file)\r\n#except Exception as err:\r\n# print(err)\r\n \r\n # display files and directories\r\n#import os\r\n#\r\n#dirlist = []\r\n#filelist=[]\r\n#\r\n#try:\r\n# for file in os.listdir():\r\n# if os.path.isfile(file):\r\n# filelist.append(file)\r\n# elif os.path.isdir(file):\r\n# dirlist.append(file)\r\n#except Exception as err:\r\n# print(err)\r\n#else:\r\n# \r\n# print(\"--------------FILES--------\")\r\n# for file in filelist:\r\n# print(file)\r\n# print(\"--------------Dir--------\")\r\n# for file in dirlist:\r\n# print(file)\r\n# \r\n# ## file size\r\n# \r\n#import os\r\n#\r\n#try:\r\n# for file in os.listdir():\r\n# if os.path.isfile(file):\r\n# getsize = os.path.getsize(file)\r\n# print (file.ljust(40),getsize,\"bytes\")\r\n#except Exception as err:\r\n# print(err)\r\n \r\n #delete all .pdf files\r\n \r\n# \r\n#import os\r\n#\r\n#try:\r\n# for file in os.listdir():\r\n# if file.endswith(\".pdf\"):\r\n# os.remove(file)\r\n#except Exception as err:\r\n# print(err)\r\n \r\n \r\n \r\n#import os\r\n#\r\n#try:\r\n# for value in range(1,100):\r\n# dirname = \"dir\" + str(value)\r\n# os.rmdir(dirname)\r\n#except Exception as err:\r\n# print(err)\r\n \r\n #copy files\r\n \r\n#import shutil\r\n#import os\r\n#\r\n#source = \"C:\\\\Users\\\\ashwin.kk\\\\Desktop\\\\programs\\\\source\"\r\n#destination = \"C:\\\\Users\\\\ashwin.kk\\\\Desktop\\\\programs\\\\destination\"\r\n#os.chdir(source)\r\n#try:\r\n# if os.path.isdir(source) and os.path.isdir(destination):\r\n# for file in os.listdir():\r\n# if os.path.isfile(file):\r\n# shutil.copy(file,destination)\r\n# print(file, \"copied to \", destination)\r\n# else:\r\n# print(\"directories are not existing\")\r\n#except Exception as err:\r\n# print(err)\r\n# \r\n \r\nimport os\r\nimport time\r\ntry:\r\n for file in os.listdir():\r\n today_date = time.strftime(\"%d-%m-%Y_\")\r\n if file.endswith(\"csv\"):\r\n newfile =today_date + file\r\n \r\n os.rename(file,newfile)\r\n print(file, \"------------>\" + newfile)\r\nexcept Exception as err:\r\n print(err) ","sub_path":"osdemooo.py","file_name":"osdemooo.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"320801984","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 11 19:23:20 2020\nAvaliador\n11/06/2020 = Criação estrutural (primeira Versão)\n11/06/2020 = Funções recuperaMedidores()\n recuperaRegistros(minutosAtras, medidor) \n avaliaRegistros(df, intpt, coef)\n recuperaModeloAnotacoes(idmed, segmento, diaSemana)\n13/06/2020 = Função armazenaAlerta(dspaval)\n Funcional (Armazenando Alertas)\n@author: Layer\n\"\"\"\n#imports\nimport mysql.connector as sql\nfrom datetime import timedelta\nfrom datetime import datetime\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\n\nbanco = sql.connect( # Parametros do banco\n host=\"localhost\",\n database='simcona', \n user='aguasql', \n password='pass1368'\n)\ncursql = banco.cursor()\n\n# Função Recupera medidores\ndef recuperaMedidores():\n cursql.execute(\"SELECT id, nome, topico FROM Medidor\")\n resultadoMedidores = cursql.fetchall()\n return resultadoMedidores\n#recupera registros\ndef recuperaRegistros(minutosAtras, medidor):\n inicio = str(datetime.now() - timedelta(minutes=minutosAtras))\n fim =str (datetime.now())\n #Select e where \n dbsel = \"UNIX_TIMESTAMP(horario) AS horario, valor\"\n dbwhe = \"idMedidor=\"+str(medidor)+\" AND horario BETWEEN '\"+inicio+\"' AND '\"+fim+\"' ORDER BY id ASC\"\n #execução\n df = pd.read_sql(\"SELECT \"+dbsel+\" FROM Registro WHERE \"+dbwhe, con=banco)\n if df.empty:\n return pd.DataFrame()\n #Passando o tempo para absoluto (Calculos Epoch (delta))\n primeirohorario = df.loc[0, 'horario']\n df['horario'] = df['horario'].sub(primeirohorario)\n primeirohorario = df.loc[0, 'horario']\n #Cria terceira coluna\n df['acumulado'] = 0\n i=1\n while (i < len(df.index)):\n df['acumulado'][i] = df['acumulado'][i-1]+df['valor'][i]\n i +=1\n return df\n#avalia registros\ndef avaliaRegistros(df, intpt, coef): # Dataframe, coef, intercept\n #pega os parâmetros do df\n x = df.iloc[:,:1].values\n y = df.iloc[:,2:3].values\n #instancia o regressor\n regressor = LinearRegression()\n regressor.fit(x,y)\n regressor.intercept_ = intpt\n regressor.coef_[0] = coef\n #monta o regressor com os parâmetros enviados na função\n derror = y - regressor.predict(x)\n percent = 0\n soma=0\n contaerro=0\n totalleitura = len(derror)\n for leitura in derror:\n soma +=leitura\n if leitura>=0:\n contaerro += 1\n percent = (100*contaerro)/totalleitura\n # Porcentagem de erro Positivo: percent / Soma do erro: soma \n retornoDesempenho = [soma[0],percent]\n return retornoDesempenho\n\ndef recuperaModeloAnotacoes(idmed, segmento, diaSemana):\n if diaSemana < 5: diaSemSTR = \"< 5\"\n else: diaSemSTR = \"> 4\"\n dbsel = \"SELECT ModeloAnotacao.id, ModeloAnotacao.intpt, ModeloAnotacao.coef, Anotacao.id FROM ModeloAnotacao \"\n dbon = \"INNER JOIN Anotacao ON Anotacao.id=ModeloAnotacao.idAnotacao \"\n dbwhe = \"WHERE Anotacao.idMedidor = \"+str(idmed)+\" AND ModeloAnotacao.diaSemana \"+diaSemSTR+\" AND ModeloAnotacao.segHora = \"+str(segmento)#\n cursql.execute(dbsel + dbon + dbwhe)\n print(dbsel + dbon + dbwhe)\n resultadomodelos = cursql.fetchall()\n return resultadomodelos # idModeloanotacao, intpt, coef\ndef armazenaAlerta(dspaval):\n peso = 0\n if dspaval[3] > 50.00: peso +=50\n if dspaval[2] > 1: peso +=50\n valores = \"'Cons. Anormal','\"+str(datetime.now())+\"',\"+str(dspaval[4])+\",\"+str(dspaval[0])+\",\"+str(peso)\n cursql.execute(\"INSERT INTO Alerta (textoDescricao, horario, idAnotacao, idMedidor, peso) VALUES (\"+valores+\")\") \n banco.commit()\n\n#\"MAIN\"\nmedidores = recuperaMedidores()\nsegmentoAtual = (datetime.now().hour //3)\ndesempenhos = []\nmodelos = []\ndfmedidores = []\nmdmedidores = []\nest_totalmodelos = 0\nest_totalalertas = 0\n\nfor medidor in medidores: #iteração de medidores\n dataframedomedidor = recuperaRegistros(15, medidor[0])\n dfmedidores.append(dataframedomedidor)\n modelosAnotacoes = recuperaModeloAnotacoes(medidor[0], segmentoAtual,datetime.now().weekday())\n mdmedidores.append(modelosAnotacoes)\n if not(dataframedomedidor.empty): \n for modelo in modelosAnotacoes:\n est_totalmodelos +=1\n retornoAvalia = avaliaRegistros(dataframedomedidor, float(modelo[1]) , float(modelo[2]))\n resultadodesempenho = [medidor[0],modelo[0],retornoAvalia[0], retornoAvalia[1],modelo[3]]\n desempenhos.append(resultadodesempenho)\nfor desempenhoavaliado in desempenhos:\n if desempenhoavaliado[2] > 1 or desempenhoavaliado[3] > 50.00:\n armazenaAlerta(desempenhoavaliado)\n est_totalalertas +=1\nprint (\"Estatisticas gerais\") \nprint (\"Foram processados um total de \"+str(est_totalmodelos)+\" modelos\")\nprint (\"Foram armazenados um total de : \"+str(est_totalalertas)+ \" alertas\")\n\n","sub_path":"python_ML/Avaliador.py","file_name":"Avaliador.py","file_ext":"py","file_size_in_byte":4868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"98709082","text":"# Executar o script como sudo na maquina remota\nimport os\n\ngrupos = [\"grp01\", \"grp02\", \"grp03\", \"grp04\", \"grp05\", \"grp06\",\n \"grp07\", \"grp08\", \"grp09\", \"grp10\", \"grp11\", \"grp12\"]\n\nos.system(\"ipcs -m > log.txt\")\n\nf = open(\"log.txt\", \"r\")\nlinhas = f.readlines()\nf.close()\n\nfor linha in linhas:\n\tdados = linha.split(\" \")\n\t\n\tif len(dados) > 4:\n\t\tshmid = dados[1]\n\t\tgrupo = dados[3]\n\n\t\tif grupo in grupos:\n\t\t\tcomando = \"ipcrm -m \" + shmid\n\t\t\tos.system(comando)\n\nos.system(\"rm -rf log.txt\")","sub_path":"ipcs.py","file_name":"ipcs.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"118182718","text":"import multiprocessing as mp\nfrom functools import partial\nimport numpy as np\n\nfrom scipy.integrate import odeint\nfrom scipy.optimize import fsolve\nfrom scipy.signal import find_peaks\n\nfrom cslib import *\nfrom model import LTModel\n\nDPI = 300\ntrunc = 60000\n\nclass ContStim(LTModel):\n def lt(self, u, t, e, stim=0.):\n if t < 4.000: # Fixed time. Should be enough for steady state.\n stim = 0.\n\n Eprod = np.sum(u[:self.N] * u[self.N:])\n E = (-u[:self.N] + self.rectify((self.J / self.N) * Eprod + e + stim)) / self.τ # Equation 1\n\n xprod = self.U * u[:self.N] * u[self.N:]\n x = (1 - u[self.N:]) / self.τ_rec - xprod # Equation 2\n\n return np.concatenate((E, x))\n\nm = ContStim()\nt = m.t = np.linspace(0,10,100000)\ne, E_0, x_0 = m.gen_rand_params(513)\nH_arr = np.linspace(0, 5, 5000)\n\n\n\n#%% Phase plot of the stimulation\nparams = m.gen_rand_params(513)\n# stim_thr, sol = m.find_stim_thresh(*params)\n# Threshold = 2.18\nthrs = (2.3,)\nsols = [m.solve_lt(*params, stim=s) for s in thrs]\n\n\n#%% H During Stimulation\nH = [m.calc_H(sol) for sol in sols]\nsol_sep = [m.sep_Ex(sol) for sol in sols]\nwith genfig(dpi=200, n=(1,2), save='11cont.png') as axs:\n axs[0].plot(m.t, sol_sep[0][0])\n label(axs[0], '$t$', '$\\\\bar{E}(t)$')\n title(axs[0], '$\\\\bar{E}(t)$ During Cont. Stimulation')\n\n axs[1].plot(m.t, sol_sep[0][1])\n label(axs[1], '$t$', '$\\\\bar{x}(t)$')\n title(axs[1], '$\\\\bar{x}(t)$ During Cont. Stimulation')\n\n#%% Fixed point analysis\n\nH_star = fsolve(lambda x: m.g(x, e) - x, 1.)[0]\nH_s_cont = fsolve(lambda x: m.g(x, e+thrs[0]) - x, 1.)[0]\n\nE_star = m.rectify(m.J * H_star + e)\nx_star = 1 / (1 + m.τ_rec * m.U * E_star)\n\nE_star_p = m.rectify(m.J * 2.0084 + e)\n\nprint(np.mean(E_star))\nprint(np.mean(x_star))\n\ndef plot_H(ax, stim=None):\n H_arr = np.linspace(0, 5, 5000)\n crossing = m.find_crossing_idx(H_arr, H_star, e)\n if stim is not None:\n ax.plot(m.J * H_arr, m.g_bar(H_arr+stim, H_star, e), 'k--', label=\"$\\\\bar{g}(H + \\\\Delta s)$\")\n ax.plot(H_arr, m.g_bar(H_arr, H_star, e), '--', label=\"$\\\\bar{g}(H)$ Before Stim\")\n ax.plot(H_arr, m.g_bar(H_arr, H_s_cont, e), '-', label=\"$\\\\bar{g}(H)$ After Stim\")\n\n # ax.plot(H_arr, m.g(H_arr, e), 'b-', alpha=0.7, label=\"$g(H)$\")\n ax.plot([H_star], [H_star], 'kx', markersize=6, markeredgewidth=2, alpha=0.8, label=\"$H^*$ Before Stim\")\n ax.plot([H_s_cont], [H_s_cont], 'ko', markersize=6, markeredgewidth=2, alpha=0.8, label=\"$H^*$ After Stim\")\n ax.plot(H_arr, H_arr, 'k-', alpha=0.5, label=\"$H$\")\n label(ax, '$H$', '$H$')\n title(ax, f'Mean Field Approximation')\n ax.legend()\n print(H_arr[crossing])\n\nwith genfig(dpi=DPI, save='12hcont.png') as ax:\n plot_H(ax) # , 0.5)\n\n#%% Phase Portrait with Various Stimulation Strengths\n\n# E_fix, x_fix = np.mean(E_star), np.mean(x_star)\n# with genfig(dpi=DPI, save='7tstim.png') as ax:\n# for i, (E, x) in enumerate(sol_sep):\n# ax.plot(E[trunc:], x[trunc:], label=thrs[i])\n# # ax.plot([8.25, 8.25], [0.438, 0.444], 'k--') # Separatrix\n# ax.plot(E_fix, x_fix, 'kx')\n# ax.plot(np.mean(E_star_p), x_fix, 'kx')\n# ax.set_xlim(5.5, 10)\n# ax.set_ylim(0.43, 0.445)\n# label(ax, '$\\\\bar{E}$', '$\\\\bar{x}$')\n# title(ax, 'Phase Portrait w/ Various Stim Strengths')\n# ax.legend()\n\n\n\n#%% Periodic\n\nclass PeriodicStim(LTModel):\n def lt(self, u, t, e, stim=None):\n\n if stim(t): # Fixed time. Should be enough for steady state.\n stim = 2.3\n else:\n stim = 0\n\n Eprod = np.sum(u[:self.N] * u[self.N:])\n E = (-u[:self.N] + self.rectify((self.J / self.N) * Eprod + e + stim)) / self.τ # Equation 1\n\n xprod = self.U * u[:self.N] * u[self.N:]\n x = (1 - u[self.N:]) / self.τ_rec - xprod # Equation 2\n\n return np.concatenate((E, x))\n\nm = PeriodicStim()\nm.t = np.linspace(0, 15, 100000)\n\ndef gen_func(freq):\n return lambda t: (freq*t - np.floor(freq*t) < freq*0.001 and t > 4)\n\nsols = [m.solve_lt(*params, stim=f) for f in [gen_func(1), gen_func(5)]]\n\n\n#%% H During Stimulation\nH = [m.calc_H(sol) for sol in sols]\nsol_sep = [m.sep_Ex(sol) for sol in sols]\nwith genfig(dpi=200, n=(2,2), save='12periodic.png') as axs:\n axs[0].plot(m.t, sol_sep[0][0])\n label(axs[0], '$t$', '$\\\\bar{E}(t)$')\n title(axs[0], '$\\\\bar{E}(t)$ During 1 Hz Stimulation')\n\n axs[1].plot(m.t, sol_sep[0][1])\n label(axs[1], '$t$', '$\\\\bar{x}(t)$')\n title(axs[1], '$\\\\bar{x}(t)$ During 1 Hz Stimulation')\n\n axs[2].plot(m.t, sol_sep[1][0])\n label(axs[2], '$t$', '$\\\\bar{E}(t)$')\n title(axs[2], '$\\\\bar{E}(t)$ During 5 Hz Stimulation')\n\n axs[3].plot(m.t, sol_sep[1][1])\n label(axs[3], '$t$', '$\\\\bar{x}(t)$')\n title(axs[3], '$\\\\bar{x}(t)$ During 5 Hz Stimulation')\n","sub_path":"mean_field_cont.py","file_name":"mean_field_cont.py","file_ext":"py","file_size_in_byte":4766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"244605920","text":"# 퀴즈3. result03.csv에 출력\n\n# 조건1. 5개 열 중에서 part Number와 Purchase Date는 제거한다.\n\n# 조건2. Supplier Y 행은 지운다.\n\n# 조건3. 가격을 모두 1.5배 인상시킨다.\n\n# 그리고, 100달러 미만 단위는 버린다. 770 --> 700\n\n#\n\n# 심화퀴즈3. 열이 굉장히 많다고 가정해서 part Number와 Purchase Date의\n\n# 위치를 모른다.\n\n\n\n\n\n\n\ninput_file = \"d:\\\\data_analysis\\\\csv\\\\supplier_data.csv\"\noutput_file = \"d:\\\\data_analysis\\\\output\\\\result04.csv\"\n\n\n\n\nwith open(input_file, 'r', newline='') as filereader :\n\n with open(output_file, 'w', newline='') as filewriter :\n\n\n\n\n header = filereader.readline()\n\n header = header.strip() # 앞뒤 공백제거\n\n header_list = header.split(',')\n\n # part Number, Purchase Date\n\n idx1 = 0\n\n for h in header_list :\n\n if h.strip().upper() == 'part Number'.strip().upper() :\n\n break\n\n idx1 += 1\n\n idx2 = 0\n\n for h in header_list :\n\n if h.strip().upper() == 'Purchase Date'.strip().upper() :\n\n break\n\n idx2 += 1\n\n if idx1 > idx2 :\n\n idx1, idx2 = idx2, idx1\n\n # 리스트를 다시 콤마(,)로 분리된 문자열로 만들고 싶다.\n\n header_str = ','.join(map(str, header_list))\n\n filewriter.write(header_str + '\\n')\n\n for row in filereader : # 모든행은 row에 넣고 돌리기.\n\n row = row.strip()\n\n row_list = row.split(',')\n\n del(row_list[idx2])\n\n del(row_list[idx1])\n\n if row_list[0] == 'Supplier Y' :\n\n continue\n\n cost = float(row_list[2][1:])\n\n cost *= 1.5\n\n cost = int(cost/100) * 100\n\n cost_str = \"${0:.2f}\".format(cost)\n\n row_list[2] = cost_str\n\n row_str = ','.join(map(str, row_list))\n\n print(row_str)\n\n filewriter.write(row_str + '\\n')\n\n\n\n\n print('Ok~~')","sub_path":"day06_04_T.py","file_name":"day06_04_T.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"516993450","text":"#!/usr/bin/python2\nimport requests\nimport json\nimport random\nfrom os import system\nimport urllib\n\nlogin_url = 'https://www.douban.com/j/app/login'\nchannel_url = 'https://www.douban.com/j/app/radio/channels'\napi_url = 'https://www.douban.com/j/app/radio/people'\napp_name = 'radio_desktop_win'\nversion = '100'\ntype_map = {\n 'new': 'n',\n 'playing': 'p',\n 'rate': 'r',\n 'unrate': 'u',\n 'end': 'e',\n 'bye': 'b',\n 'skip': 's',\n }\n#get channel, huayu\n#r = requests.get(channel_url)\n#channels = r.json()[\"channels\"]\nchannel = 1\n# get play list\ndef download(name):\n\tsid = random.randint(1,10000)\n\tpayload = {\"app_name\": app_name, \"version\":version,\"channel\":channel,\"type\":\"p\",\"sid\":sid}\n\tr = requests.get(api_url,params=payload)\n\t#get song list\n\tif r.json()[\"r\"] == 0:\n\t\tsongs = r.json()[\"song\"]\n\tsongs_urls = [i[\"url\"] for i in songs]\n\tfor i in songs_urls:\n\t\ttry:\n\t\t\tsystem('aria2c %s -o %s' % (i,name))\t\n\t\texcept:\n\t\t\tcontinue\nif __name__ == \"__main__\":\t\t\n\tdownload(\"1.mp4\")\n\tdownload(\"2.mp4\")\n","sub_path":"douban_download.py","file_name":"douban_download.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"482006135","text":"#!/usr/bin/python3\nimport pandas as pd\nimport re\n\n#regex to pull the fields I want\nrex = r'(?PSwf link' % (instance.get_absolute_url())\r\n swf.allow_tags = True\r\n\r\n\r\nclass VideoDetailAdmin(admin.ModelAdmin):\r\n \"\"\"\r\n Video Detail Admin\r\n \"\"\"\r\n model = VideoDetail\r\n fk_name = 'skigit_id'\r\n readonly_fields = ('related_swf', 'title', 'get_inappropriate_skigit', 'get_status',)\r\n list_display = ('related_skigit_title', 'link_related_swf', 'related_skigit_category',\r\n 'related_user', 'made_by', 'related_swf', 'views', 'likes', 'get_plugin_video',\r\n 'created_date', 'get_status', 'get_inappropriate_skigit', 'get_copy_right_skigit',\r\n 'is_deleted')\r\n list_filter = ('category', 'status', 'inappropriate_skigit', 'created_date')\r\n actions = ['approve', 'unapprove', 'delete_model', ]\r\n search_fields = ['skigit_id__title', 'made_by__username', 'made_by__first_name', 'skigit_id__user__username',\r\n 'skigit_id__user__first_name', 'plugged_skigit__title', 'category__cat_name']\r\n ordering = ('status', '-created_date')\r\n\r\n list_per_page = 15\r\n\r\n def get_plugin_video(self, obj):\r\n \"\"\"\r\n Get Plugin Videos\r\n \"\"\"\r\n if obj.plugged_skigit:\r\n if obj.plugged_skigit:\r\n return obj.plugged_skigit.title\r\n return ''\r\n return ''\r\n get_plugin_video.allow_tags = True\r\n get_plugin_video.short_description = 'plugged Skigit'\r\n get_plugin_video.admin_order_field = 'plugged_skigit__title'\r\n\r\n def get_inappropriate_skigit(self, obj):\r\n \"\"\"\r\n Get Inappropriate Skigit\r\n \"\"\"\r\n\r\n if obj.inappropriate_skigit:\r\n if obj.inappropriate_skigit == '0':\r\n return 'Pending'\r\n elif obj.inappropriate_skigit == '1':\r\n return 'Appropriate'\r\n elif obj.inappropriate_skigit == '2':\r\n return 'Inappropriate'\r\n else:\r\n return ''\r\n else:\r\n return ''\r\n get_inappropriate_skigit.allow_tags = True\r\n get_inappropriate_skigit.short_description = 'Inappropriate Skigit'\r\n get_inappropriate_skigit.admin_order_field = 'inappropriate_skigit'\r\n\r\n def get_copy_right_skigit(self, obj):\r\n \"\"\"\r\n Get Copyright Skigit\r\n \"\"\"\r\n\r\n if obj.copyright_skigit:\r\n if obj.copyright_skigit == '0':\r\n return 'Pending'\r\n elif obj.copyright_skigit == '1':\r\n return 'Legal Skigit'\r\n elif obj.copyright_skigit == '2':\r\n return 'Illegal Skigit'\r\n else:\r\n return ''\r\n else:\r\n return ''\r\n get_copy_right_skigit.allow_tags = True\r\n get_copy_right_skigit.short_description = 'Copyright Infringement'\r\n get_copy_right_skigit.admin_order_field = 'copyright_skigit'\r\n\r\n def related_skigit_category(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Video Instance\r\n Returns: Category Name\r\n \"\"\"\r\n return obj.category.cat_name\r\n related_skigit_category.short_description = 'Category'\r\n\r\n def is_deleted(self, obj):\r\n \"\"\"\r\n Is Delete Status\r\n \"\"\"\r\n if obj.is_active:\r\n return False\r\n else:\r\n return True\r\n is_deleted.boolean = True\r\n is_deleted.short_description = 'Deleted by User'\r\n\r\n @staticmethod\r\n def views(obj):\r\n \"\"\"\r\n Vievs count\r\n \"\"\"\r\n return obj.view_count\r\n\r\n def get_status(self, obj):\r\n \"\"\"\r\n Get Status\r\n \"\"\"\r\n if obj.status == 0:\r\n return '

    Pending

    '\r\n elif obj.status == 1:\r\n return '

    Publish

    '\r\n elif obj.status == 2:\r\n return '

    Rejected

    '\r\n get_status.allow_tags = True\r\n get_status.short_description = 'Status'\r\n get_status.admin_order_field = 'status'\r\n\r\n @staticmethod\r\n def likes(obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n\r\n Returns: Likes count\r\n \"\"\"\r\n return obj.skigit_id.likes.filter(status=True).count()\r\n\r\n def related_swf(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: SWF Url\r\n \"\"\"\r\n return 'Show'\r\n related_swf.allow_tags = True\r\n related_swf.short_description = 'Video Link'\r\n\r\n def link_related_swf(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Url Of Youtube Video List Page\r\n \"\"\"\r\n return 'https://www.youtube.com/watch?v=%s' % \\\r\n (obj.skigit_id.video_id, obj.skigit_id.video_id)\r\n link_related_swf.allow_tags = True\r\n link_related_swf.short_description = 'Youtube URL'\r\n\r\n def related_user(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: User Name\r\n \"\"\"\r\n return obj.skigit_id.user\r\n related_user.short_description = 'User Name'\r\n\r\n def related_skigit_title(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Video Title\r\n \"\"\"\r\n return obj.skigit_id.title\r\n related_skigit_title.short_description = 'Primary Skigit'\r\n\r\n def approve(self, request, queryset):\r\n \"\"\"\r\n Args:\r\n request: Instance\r\n queryset: obj\r\n Returns: Video Status As Publish and send's email related users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.status == 1 and not obj.inappropriate_skigit == '2':\r\n obj.status = 1\r\n obj.save()\r\n\r\n if obj.plugged_skigit:\r\n vid_obj = VideoDetail.objects.get(skigit_id=obj.plugged_skigit.id)\r\n if vid_obj.made_by:\r\n if not obj.skigit_id.user.id == vid_obj.made_by.id:\r\n business_plug_invoice(request, obj.id, obj.skigit_id.user.id, vid_obj.id, vid_obj.made_by.id)\r\n if obj.status == 1:\r\n # Notification for New Post Skigit.\r\n ski_nt_message = \" \"\r\n ski_nt_message += \" Congratulations! Your Skigit \"\r\n ski_nt_message += obj.title\r\n ski_nt_message += \" has been posted to Skigit! \"\r\n\r\n if notification_settings(obj.skigit_id.user.id, 'skigit_notify') == True:\r\n if not Notification.objects.filter(msg_type='new_post', skigit_id=obj.skigit_id.id,\r\n user=obj.skigit_id.user, from_user=request.user).exists():\r\n Notification.objects.create(msg_type='new_post', skigit_id=obj.skigit_id.id,\r\n user=obj.skigit_id.user, from_user=request.user,\r\n message=ski_nt_message)\r\n\r\n # Accept Skigit Email Message Body\r\n message=\"\" \\\r\n \"\" \\\r\n \"\"\\\r\n \"

    \"\\\r\n \"\"\\\r\n \"Congratulations! Skigit would like to command you on your awesomeness!

    \" \\\r\n \"

    Your Skigit \"+str(obj.skigit_id.title)+\" was posted.

    \"\\\r\n \"
    \"\r\n\r\n subject = \"Your skigit was posted!\"\r\n coupan_email(subject, obj.skigit_id.user.email, EMAIL_HOST_VIDEO, message)\r\n if obj.receive_donate_sperk == 0 and obj.business_logo:\r\n user_profile = Profile.objects.get(logo_img=obj.business_logo.id)\r\n if user_profile.incentive == 1:\r\n image_url = request.build_absolute_uri(obj.business_logo.logo.url)\r\n company_title = user_profile.company_title\r\n profile_name = '%s/%s/' % (obj.made_by.id, obj.business_logo.id)\r\n if user_profile.coupan_image:\r\n coupan_url = request.build_absolute_uri(user_profile.coupan_image.url)\r\n else:\r\n coupan_url = ''\r\n\r\n insentive_txt = user_profile.skigit_incentive\r\n instruction = user_profile.redemoption_instrucations\r\n\r\n # Coupan sperk User Skigit Email Message Body\r\n ehtml_body =\"\" \\\r\n \"\" \\\r\n \"\"\\\r\n \"\" \\\r\n \"\" \\\r\n \"\" \\\r\n \"

    \"\\\r\n \"\"\\\r\n \"Congratulations! Skigit would like to command you on your awesomeness!

    \" \\\r\n \"Your skigit was posted and you are eligble to collect a reward from \"+ company_title +\"  \" \\\r\n \"\"\\\r\n \"\" \\\r\n \" for your great work of art!

    \"\\\r\n \"\"+obj.skigit_id.title+\"
    \" \\\r\n \"

    Reward : \"+insentive_txt+\"

    \" \\\r\n \"

    Reward collection Instructions : \"+instruction+\"

    \" \\\r\n \" Visit \" \\\r\n \" at www.skigit.com/\"+company_title+\"
    \" \\\r\n \"
    \"\\\r\n\r\n coupan_email('Redeem Your Sperk', obj.skigit_id.user.email, EMAIL_HOST_BUSINESS, ehtml_body)\r\n if obj.skigit_id.user.get_full_name():\r\n username = obj.skigit_id.user.get_full_name()\r\n else:\r\n username = obj.skigit_id.user.username\r\n\r\n # Coupan sperk Business User Skigit Email Message Body\r\n bhtml_body = \"\" \\\r\n \"\" \\\r\n \"\"\\\r\n \"\" \\\r\n \"\"\\\r\n \"\" \\\r\n \"

    \"\\\r\n \"\"\\\r\n \"Congratulations! Skigit would like to command you on your awesomeness!

    \" \\\r\n \"A \"\\\r\n \" Skigit was posted!

    \"\\\r\n \"\"\\\r\n \" \"+obj.skigit_id.title+\"
    \" \\\r\n \"

    Reward : \"+insentive_txt+\"

    \"\\\r\n \" \"+username+\" wishes to thank you!
    \" \\\r\n \" Visit \" \\\r\n \" at www.skigit.com/\"+ company_title +\"
    \" \\\r\n\r\n coupan_email('Redeem Your Sperk', user_profile.user.email, EMAIL_HOST_BUSINESS, bhtml_body)\r\n elif obj.receive_donate_sperk == 1 and obj.business_logo:\r\n user_profile = Profile.objects.get(logo_img=obj.business_logo.id)\r\n if user_profile.incentive == 1:\r\n image_url = request.build_absolute_uri(obj.business_logo.logo.url)\r\n company_title = user_profile.company_title\r\n insentive_value = user_profile.incetive_val\r\n insentive_txt = user_profile.skigit_incentive\r\n profile_name = '%s/%s/' % (obj.made_by.id, obj.business_logo.id)\r\n if obj.skigit_id.user.get_full_name():\r\n username = obj.skigit_id.user.get_full_name()\r\n else:\r\n username = obj.skigit_id.user.username\r\n b_message = \"\" \\\r\n \"\" \\\r\n \"\"\\\r\n \"\"\\\r\n \"\" \\\r\n \"\" \\\r\n \"

    \"\\\r\n \"\"\\\r\n \"Congratulations! Skigit would like to command you on your awesomeness!

    \" \\\r\n \"Your skigit was posted and you are eligble to collect a reward from \"+ company_title +\" \" \\\r\n \"\"\\\r\n \"\" \\\r\n \" for your great work of art !

    \"\\\r\n \"

    \" + obj.skigit_id.title + \"

    \"\\\r\n \"Thank you for donating your sperk to \"+ obj.donate_skigit.ngo_name +\" on behalf of \"+company_title+\"
    \" \\\r\n \" Visit \" \\\r\n \"  at www.skigit.com/\"+company_title +\"
    \"\r\n coupan_email('Thanks for your Donation!', obj.skigit_id.user.email, EMAIL_HOST_BUSINESS, b_message)\r\n\r\n business_message = \"\" \\\r\n \"\" \\\r\n \"\"\\\r\n \"\" \\\r\n \"\"\\\r\n \"\"\\\r\n \"\"\\\r\n \"\"\\\r\n \"\" \\\r\n \"\" \\\r\n \"

    \"\\\r\n \"\"\\\r\n \"Congratulations! Skigit would like to command you on your awesomeness!

    \" \\\r\n \"A  \"\\\r\n \" Skigit was posted!

    \"\\\r\n \"\"\\\r\n \"

    \"+str(obj.skigit_id.title)+\"

    \" \\\r\n \"

    Reward : \"+str(insentive_txt)+\"

    \"\\\r\n \" \"+ str(username) +\" \"\\\r\n \" just created a Skigit for your company and instructs you to donate his Sperk to

    Value: $\"+str(insentive_value)+\"

    \"+str(obj.donate_skigit.ngo_name)+\"

    \"\\\r\n \"\"\\\r\n \"

    \" + obj.donate_skigit.url +\"

    \" \\\r\n \"As required by the Skigit Business Terms of Service, you are must make the donation this tax year.

    \" \\\r\n \" Visit \" \\\r\n \" at www.skigit.com/\"+str(company_title)+\"
    \"\r\n coupan_email('Frank Spina Wants You to Join Skigit!', user_profile.user.email, EMAIL_HOST_BUSINESS, business_message)\r\n approve.short_description = \"Mark selected Skigit as Publish\"\r\n\r\n def unapprove(self, request, queryset):\r\n \"\"\"\r\n Args:\r\n request: requested user\r\n queryset: obj\r\n Returns: Video Statusr from Publish or Pending to Reject\r\n \"\"\"\r\n for obj in queryset:\r\n if not obj.status == 2 and not obj.inappropriate_skigit:\r\n obj.status = 2\r\n obj.save()\r\n message = \"
    \" \\\r\n \"
    Your Skigit \"+str(obj.skigit_id.title)+\" was rejected.
    \" \\\r\n \"
    \"\r\n subject = \"Your Skigit was Rejected\"\r\n send_email(subject, message, obj.skigit_id.user.email, '', EMAIL_HOST_VIDEO)\r\n # return ''\r\n elif obj.status == 2 and not obj.inappropriate_skigit:\r\n obj.status = 2\r\n obj.save()\r\n\r\n unapprove.short_description = \"Mark selected Skigit as Reject\"\r\n\r\n def save_model(self, request, obj, form, change):\r\n obj.save()\r\n\r\n if obj.status == 1:\r\n # Notification for New Post Skigit.\r\n ski_nt_message = \" \"\r\n ski_nt_message += \" Congratulations! Your Skigit \"\r\n ski_nt_message += obj.title\r\n ski_nt_message += \" has been posted to Skigit!\"\r\n if notification_settings(obj.skigit_id.user.id, 'skigit_notify')== True:\r\n if not Notification.objects.filter(msg_type='new_post', skigit_id=obj.skigit_id.id, user=obj.skigit_id.user,\r\n from_user=request.user).exists():\r\n Notification.objects.create(msg_type='new_post', skigit_id=obj.skigit_id.id, user=obj.skigit_id.user,\r\n from_user=request.user, message=ski_nt_message)\r\n\r\n def delete_model(self, request, queryset):\r\n \"\"\"\r\n Args:\r\n request: requested users\r\n queryset:obj\r\n Returns: 1 on video deleted as Success\r\n \"\"\"\r\n for obj in queryset:\r\n test = Video.objects.get(pk=obj.skigit_id.id)\r\n # sys.argv = ['', '--video_id', test.video_id]\r\n # runpy.run_path(os.path.join(settings.BASE_DIR, 'skigit/delete_video.py').replace('\\\\', '/'), run_name='__main__')\r\n delete_video(test.video_id)\r\n test.delete()\r\n delete_model.short_description = \"Delete Skigit\"\r\n\r\nadmin.site.unregister(User)\r\n\r\n\r\nclass ProfileInline(admin.StackedInline):\r\n \"\"\"\r\n Profile Inline Admin\r\n \"\"\"\r\n model = Profile\r\n fk_name = 'user'\r\n\r\n\r\nclass UserProfileAdmin(UserAdmin):\r\n \"\"\"\r\n User Profile Admin\r\n \"\"\"\r\n list_display = ('username', 'email', 'first_name', 'last_name', 'is_active', 'get_is_staff', 'get_video_rights',\r\n 'get_payment_rights', 'get_bug_rights', 'get_inappropriate_rights','get_copy_rights', 'date_joined')\r\n actions = ['assing_video_management', 'remove_video_management', 'assing_payment_management',\r\n 'remove_payment_management', 'assign_copyright_management', 'remove_copyright_management',\r\n 'assign_bug_management', 'remove_Bug_management', 'assign_inappropriate_management',\r\n 'remove_inappropriate_management', 'add_all_rights', 'remove_all_rights', 'delete_selected']\r\n list_filter = ('is_superuser', 'is_active', 'groups', 'date_joined',)\r\n inlines = [ProfileInline, ]\r\n\r\n def get_is_staff(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: model object\r\n Returns : True or False\r\n \"\"\"\r\n return obj.is_staff\r\n get_is_staff.short_description = 'Admin'\r\n get_is_staff.boolean = True\r\n get_is_staff.admin_order_field = 'is_staff'\r\n\r\n def get_video_rights(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Returns True or False\r\n \"\"\"\r\n if obj.id:\r\n p_obj = Profile.objects.get(user=obj.id)\r\n if p_obj.video_management_rights:\r\n return True\r\n else:\r\n if obj.is_superuser:\r\n p_obj.video_management_rights = True\r\n p_obj.save()\r\n return True\r\n else:\r\n return False\r\n get_video_rights.short_description = 'Video Management'\r\n get_video_rights.boolean = True\r\n get_video_rights.admin_order_field = 'profile__video_management_rights'\r\n\r\n def get_is_staff(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: returns is staff or not (true or false)\r\n \"\"\"\r\n return obj.is_staff\r\n get_is_staff.short_description = 'Admin'\r\n get_is_staff.boolean = True\r\n get_is_staff.admin_order_field = 'is_staff'\r\n\r\n def get_payment_rights(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: True or False\r\n \"\"\"\r\n if obj.id:\r\n p_obj = Profile.objects.get(user=obj.id)\r\n if p_obj.payment_management_rights:\r\n return True\r\n else:\r\n if obj.is_superuser:\r\n p_obj.payment_management_rights = True\r\n p_obj.save()\r\n return True\r\n else:\r\n return False\r\n get_payment_rights.short_description = 'Payment Management'\r\n get_payment_rights.boolean = True\r\n get_payment_rights.admin_order_field = 'profile__payment_management_rights'\r\n\r\n def get_copy_rights(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Returns True or False\r\n \"\"\"\r\n if obj.id:\r\n p_obj = Profile.objects.get(user=obj.id)\r\n if p_obj.copyright_investigation_rights:\r\n return True\r\n else:\r\n if obj.is_superuser:\r\n p_obj.copyright_investigation_rights = True\r\n p_obj.save()\r\n return True\r\n else:\r\n return False\r\n get_copy_rights.short_description = 'Copyright Management'\r\n get_copy_rights.boolean = True\r\n get_copy_rights.admin_order_field = 'profile__copyright_investigation_rights'\r\n\r\n def get_bug_rights(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Returns True or False\r\n \"\"\"\r\n if obj.id:\r\n p_obj = Profile.objects.get(user=obj.id)\r\n if p_obj.bug_rights:\r\n return True\r\n else:\r\n if obj.is_superuser:\r\n p_obj.bug_rights = True\r\n p_obj.save()\r\n return True\r\n else:\r\n return False\r\n get_bug_rights.short_description = 'Bug Management'\r\n get_bug_rights.boolean = True\r\n get_bug_rights.admin_order_field = 'profile__bug_rights'\r\n\r\n def get_inappropriate_rights(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Returns True or False\r\n \"\"\"\r\n if obj.id:\r\n p_obj = Profile.objects.get(user=obj.id)\r\n if p_obj.inappropriate_rights:\r\n return True\r\n else:\r\n if obj.is_superuser:\r\n p_obj.inappropriate_rights = True\r\n p_obj.save()\r\n return True\r\n else:\r\n return False\r\n get_inappropriate_rights.short_description = 'Inappropriate Management'\r\n get_inappropriate_rights.boolean = True\r\n get_inappropriate_rights.admin_order_field = 'profile__inappropriate_rights'\r\n\r\n def assing_video_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Video Management Add Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Video Management Rights of Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_add_skigit = Permission.objects.get(name='Can add Skigit')\r\n can_change_skigit = Permission.objects.get(name='Can change Skigit')\r\n if not obj.is_staff:\r\n obj.is_staff = True\r\n\r\n obj.user_permissions.add(can_add_skigit.id, can_change_skigit.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.video_management_rights:\r\n p_obj.video_management_rights = True\r\n obj.save()\r\n p_obj.save()\r\n else:\r\n obj.user_permissions.add(can_add_skigit.id, can_change_skigit.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.video_management_rights:\r\n p_obj.video_management_rights = True\r\n obj.save()\r\n p_obj.save()\r\n assing_video_management.short_description = \"Assign Video Management to selected Users\"\r\n\r\n def remove_video_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Video Management Remove Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Video Management Rights of Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_add_skigit = Permission.objects.get(name='Can add Skigit')\r\n can_change_skigit = Permission.objects.get(name='Can change Skigit')\r\n obj.user_permissions.remove(can_add_skigit.id, can_change_skigit.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if p_obj.payment_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.copyright_investigation_rights:\r\n obj.is_staff = True\r\n elif p_obj.bug_rights:\r\n obj.is_staff = True\r\n elif p_obj.inappropriate_rights:\r\n obj.is_staff = True\r\n else:\r\n obj.is_staff = False\r\n if p_obj.video_management_rights:\r\n p_obj.video_management_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_video_management.short_description = \"Remove Video Management to selected Users\"\r\n\r\n def assing_payment_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Payment Management Add Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Payment Management Rights to Marked Users\r\n \"\"\"\r\n can_add_payment = Permission.objects.get(name='Can add Payment')\r\n can_change_payment = Permission.objects.get(name='Can change Payment')\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n if not obj.is_staff:\r\n obj.is_staff = True\r\n obj.user_permissions.add(can_add_payment.id, can_change_payment.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.payment_management_rights:\r\n p_obj.payment_management_rights = True\r\n obj.save()\r\n p_obj.save()\r\n assing_payment_management.short_description = \"Assign Payment Management to selected Users\"\r\n\r\n def remove_payment_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Payment Management Remove Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Paymet Management Rights to Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_add_payment = Permission.objects.get(name='Can add Payment')\r\n can_change_payment = Permission.objects.get(name='Can change Payment')\r\n obj.user_permissions.remove(can_add_payment.id, can_change_payment.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = False\r\n for p_obj in profile_obj:\r\n if p_obj.video_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.copyright_investigation_rights:\r\n obj.is_staff = True\r\n elif p_obj.bug_rights:\r\n obj.is_staff = True\r\n elif p_obj.inappropriate_rights:\r\n obj.is_staff = True\r\n else:\r\n obj.is_staff = False\r\n if p_obj.payment_management_rights:\r\n p_obj.payment_management_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_payment_management.short_description = \"Remove Payment Management to selected Users\"\r\n\r\n def assign_copyright_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Copyright Management Add Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Copyright Management of Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_copyrightinfrigement = Permission.objects.get(name='Can change Copyright Infringement')\r\n can_change_copyrightinvestigation = Permission.objects.get(name='Can change Copyright Investigation')\r\n can_add_copyrightinvestigation = Permission.objects.get(name='Can add Copyright Investigation')\r\n if not obj.is_staff:\r\n obj.is_staff = True\r\n\r\n obj.user_permissions.add(can_change_copyrightinfrigement.id, can_change_copyrightinvestigation.id,\r\n can_add_copyrightinvestigation.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.copyright_investigation_rights:\r\n p_obj.copyright_investigation_rights = True\r\n obj.save()\r\n p_obj.save()\r\n else:\r\n obj.user_permissions.add(can_change_copyrightinvestigation.id, can_change_copyrightinfrigement.id,\r\n can_add_copyrightinvestigation.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.copyright_investigation_rights:\r\n p_obj.copyright_investigation_rights = True\r\n obj.save()\r\n p_obj.save()\r\n\r\n assign_copyright_management.short_description = \"Assign Copyright Management to selected Users\"\r\n\r\n def remove_copyright_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Copyright Management Remove Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Copyright Management to Marked Users\r\n \"\"\"\r\n # inappropriate_rights\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_copyrightinfrigement = Permission.objects.get(name='Can change Copyright Infringement')\r\n can_change_copyrightinvestigation = Permission.objects.get(name='Can change Copyright Investigation')\r\n can_add_copyrightinvestigation = Permission.objects.get(name='Can add Copyright Investigation')\r\n obj.user_permissions.remove(can_change_copyrightinfrigement.id, can_change_copyrightinvestigation.id,\r\n can_add_copyrightinvestigation.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = False\r\n for p_obj in profile_obj:\r\n if p_obj.video_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.payment_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.bug_rights:\r\n obj.is_staff = True\r\n elif p_obj.inappropriate_rights:\r\n obj.is_staff = True\r\n else:\r\n obj.is_staff = False\r\n if p_obj.copyright_investigation_rights:\r\n p_obj.copyright_investigation_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_copyright_management.short_description = \"Remove Copyright Management to selected Users\"\r\n\r\n def assign_inappropriate_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Copyright Management Add Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Copyright Management of Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_inappropriate = Permission.objects.get(name='Can change Inappropriate Skigit')\r\n can_change_investigation = Permission.objects.get(name='Can change Inappropriate skigit Investigator')\r\n can_add_investigation = Permission.objects.get(name='Can add Inappropriate skigit Investigator')\r\n\r\n if not obj.is_staff:\r\n obj.is_staff = True\r\n\r\n obj.user_permissions.add(can_change_inappropriate, can_add_investigation,\r\n can_change_investigation)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.inappropriate_rights:\r\n p_obj.inappropriate_rights = True\r\n obj.save()\r\n p_obj.save()\r\n else:\r\n obj.user_permissions.add(can_change_inappropriate.id, can_change_investigation.id,\r\n can_add_investigation.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.inappropriate_rights:\r\n p_obj.inappropriate_rights = True\r\n obj.save()\r\n p_obj.save()\r\n\r\n assign_inappropriate_management.short_description = \"Assign Inappropriate Management to selected Users\"\r\n\r\n def remove_inappropriate_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Inappropriate Management Remove Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Inappropriate Management to Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_inappropriate = Permission.objects.get(name='Can change Inappropriate Skigit')\r\n can_change_investigation = Permission.objects.get(name='Can change Inappropriate skigit Investigator')\r\n can_add_investigation = Permission.objects.get(name='Can add Inappropriate skigit Investigator')\r\n obj.user_permissions.remove(can_change_inappropriate, can_change_investigation,\r\n can_add_investigation)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = False\r\n for p_obj in profile_obj:\r\n if p_obj.video_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.payment_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.bug_rights:\r\n obj.is_staff = True\r\n elif p_obj.copyright_investigation_rights:\r\n obj.is_staff = True\r\n else:\r\n obj.is_staff = False\r\n if p_obj.inappropriate_rights:\r\n p_obj.inappropriate_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_inappropriate_management.short_description = \"Remove Inappropriate Management to selected Users\"\r\n\r\n def assign_bug_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Bug Management Add Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Bug Management of Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_bug = Permission.objects.get(name='Can change Bug Report Management')\r\n if not obj.is_staff:\r\n obj.is_staff = True\r\n\r\n obj.user_permissions.add(can_change_bug.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.bug_rights:\r\n p_obj.bug_rights = True\r\n obj.save()\r\n p_obj.save()\r\n else:\r\n obj.user_permissions.add(can_change_bug.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n for p_obj in profile_obj:\r\n if not p_obj.bug_rights:\r\n p_obj.bug_rights = True\r\n obj.save()\r\n p_obj.save()\r\n assign_bug_management.short_description = \"Assign Bug Management to selected Users\"\r\n\r\n def remove_Bug_management(self, request, queryset):\r\n \"\"\"\r\n Comment: Bug Management Remove Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Bug Management to Marked Users\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_bug = Permission.objects.get(name='Can change Bug Report Management')\r\n obj.user_permissions.remove(can_change_bug.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = False\r\n for p_obj in profile_obj:\r\n if p_obj.video_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.payment_management_rights:\r\n obj.is_staff = True\r\n elif p_obj.copyright_investigation_rights:\r\n obj.is_staff = True\r\n else:\r\n obj.is_staff = False\r\n if p_obj.bug_rights:\r\n p_obj.bug_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_Bug_management.short_description = \"Remove Bug Management to selected Users\"\r\n\r\n def add_all_rights(self, request, queryset):\r\n \"\"\"\r\n Comment: Add All Management Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Add All Management Permissions\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_inappropriate = Permission.objects.get(name='Can change Inappropriate Skigit')\r\n can_change_investigation = Permission.objects.get(name='Can change Inappropriate skigit Investigator')\r\n can_add_investigation = Permission.objects.get(name='Can add Inappropriate skigit Investigator')\r\n can_change_bug = Permission.objects.get(name='Can change Bug Report Management')\r\n can_add_skigit = Permission.objects.get(name='Can add Skigit')\r\n can_change_skigit = Permission.objects.get(name='Can change Skigit')\r\n can_add_payment = Permission.objects.get(name='Can add Payment')\r\n can_change_payment = Permission.objects.get(name='Can change Payment')\r\n can_change_copyrightinfrigement = Permission.objects.get(name='Can change Copyright Infringement')\r\n can_change_copyrightinvestigation = Permission.objects.get(name='Can change Copyright Investigation')\r\n can_add_copyrightinvestigation = Permission.objects.get(name='Can add Copyright Investigation')\r\n obj.user_permissions.add(can_change_copyrightinfrigement.id, can_change_copyrightinvestigation.id,\r\n can_add_skigit.id, can_change_skigit.id, can_add_payment.id,\r\n can_change_payment.id, can_add_copyrightinvestigation.id, can_change_bug.id,\r\n can_change_inappropriate.id, can_change_investigation.id,\r\n can_add_investigation.id)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = True\r\n for p_obj in profile_obj:\r\n p_obj.copyright_investigation_rights = True\r\n p_obj.payment_management_rights = True\r\n p_obj.video_management_rights = True\r\n p_obj.bug_rights = True\r\n p_obj.inappropriate_rights = True\r\n obj.save()\r\n p_obj.save()\r\n add_all_rights.short_description = \"Add All Management Rights to selected Users\"\r\n\r\n def remove_all_rights(self, request, queryset):\r\n \"\"\"\r\n Comment: Remove All Management Permissions\r\n Args:\r\n request: Requested user\r\n queryset: obj\r\n Returns: Remove All Management Permissions\r\n \"\"\"\r\n\r\n for obj in queryset:\r\n if not obj.is_superuser:\r\n can_change_bug = Permission.objects.get(name='Can change Bug Report Management')\r\n can_add_skigit = Permission.objects.get(name='Can add Skigit')\r\n can_change_skigit = Permission.objects.get(name='Can change Skigit')\r\n can_add_payment = Permission.objects.get(name='Can add Payment')\r\n can_change_payment = Permission.objects.get(name='Can change Payment')\r\n can_change_copyrightinfrigement = Permission.objects.get(name='Can change Copyright Infringement')\r\n can_change_copyrightinvestigation = Permission.objects.get(name='Can change Copyright Investigation')\r\n can_add_copyrightinvestigation = Permission.objects.get(name='Can add Copyright Investigation')\r\n can_change_inappropriate = Permission.objects.get(name='Can change Inappropriate Skigit')\r\n can_change_investigation = Permission.objects.get(name='Can change Inappropriate skigit Investigator')\r\n can_add_investigation = Permission.objects.get(name='Can add Inappropriate skigit Investigator')\r\n obj.user_permissions.remove(can_change_copyrightinfrigement, can_change_copyrightinvestigation,\r\n can_add_skigit, can_change_skigit, can_add_payment,\r\n can_change_payment, can_add_copyrightinvestigation, can_change_bug,\r\n can_change_inappropriate, can_change_investigation,\r\n can_add_investigation)\r\n profile_obj = Profile.objects.filter(user=obj.id)\r\n obj.is_staff = False\r\n for p_obj in profile_obj:\r\n p_obj.copyright_investigation_rights = False\r\n p_obj.payment_management_rights = False\r\n p_obj.video_management_rights = False\r\n p_obj.bug_rights = False\r\n p_obj.inappropriate_rights = False\r\n obj.save()\r\n p_obj.save()\r\n remove_all_rights.short_description = \"Remove All Management Rights to selected Users\"\r\nadmin.site.register(User, UserProfileAdmin)\r\n\r\n\r\nclass PaymentAdmin(admin.ModelAdmin):\r\n \"\"\"\r\n PAYMENT ADMIN\r\n \"\"\"\r\n list_display = ('payment_email', 'payment_name')\r\nadmin.site.register(Payment, PaymentAdmin)\r\n\r\n\r\nclass InappropriateSkigitReasonAdmin(admin.ModelAdmin):\r\n \"\"\"\r\n Inappropriate Skigit\r\n \"\"\"\r\n model = InappropriateSkigitReason\r\n actions = ['delete_selected', ]\r\n list_display = ('reason_title', 'created_date')\r\n list_filter = ('created_date', )\r\n search_fields = ['reason_title', ]\r\n prepopulated_fields = {'reason_slug': ('reason_title',)}\r\n\r\n\r\n# def appropriate_skigit(modeladmin, request, queryset):\r\n# \"\"\"\r\n# Args:\r\n# modeladmin:\r\n# request:\r\n# queryset:\r\n# Status = (\"1\", 'Open'), (\"2\", 'Under Investigation'), (\"3\", 'Closed'), (\"4\", 'Remove Skigit'),\r\n# \"\"\"\r\n# for obj in queryset:\r\n# if not obj.action:\r\n# obj.action = '1'\r\n# obj.save()\r\n# # obj.skigit.\r\n# # message = \"Dear %s to notify that %s is approved by Skigit team is not %s \" % (obj.reported_user.username,\r\n# # obj.skigit.skigit_id.title,\r\n# # obj.reason.reason_title)\r\n# # subject = \"Inappropriate | Skigit\"\r\n# # send_mail(subject, message, settings.EMAIL_HOST_USER, [obj.reported_user.email, ])\r\n# temp_action = None\r\n# temp_status = None\r\n# row = InappropriateSkigit.objects.filter(skigit=obj.skigit).first()\r\n#\r\n# if row.action is not False and not row.action:\r\n# temp_action = True\r\n# temp_status = False\r\n#\r\n# video = VideoDetail.objects.get(pk=obj.skigit.id)\r\n# video.inappropriate_skigit = temp_action\r\n# video.status = 1\r\n# video.save()\r\n#\r\n# # change status in inapp table\r\n# inapp = InappropriateSkigit.objects.filter(skigit=obj.skigit).first()\r\n# inapp.status = \"3\"\r\n# inapp.save()\r\n# appropriate_skigit.short_description = \"Mark selected Skigit as Appropriate\"\r\n#\r\n#\r\n# def inappropriate_skigit(modeladmin, request, queryset):\r\n# \"\"\"\r\n# Args:\r\n# modeladmin:\r\n# request:\r\n# queryset:\r\n# \"\"\"\r\n# for obj in queryset:\r\n# if not obj.action:\r\n# obj.action = False\r\n# obj.save()\r\n# message = \"

    Dear %s to notify %s is %s and removed by Skigit team

    \" % (obj.reported_user.username,\r\n# obj.skigit.skigit_id.title,\r\n# obj.reason.reason_title)\r\n# subject = \"Inappropriate | Skigit\"\r\n# send_mail(subject, message, settings.EMAIL_HOST_USER, [obj.reported_user.email, ])\r\n# message = \"Dear %s to notify that your uploaded Skigit %s is removed by due to the %s reason by Skigit team\"\\\r\n# % (obj.skigit.skigit_id.user.username, obj.skigit.skigit_id.title, obj.reason.reason_title)\r\n# subject = \"Inappropriate | Skigit\"\r\n# send_mail(subject, message, settings.EMAIL_HOST_USER, [obj.skigit.skigit_id.user.email, ])\r\n# temp_action = True\r\n# temp_status = False\r\n#\r\n# # mark status in inapp table\r\n# inapp = InappropriateSkigit.objects.get(skigit=obj.skigit)\r\n# inapp.status = 3\r\n# inapp.save()\r\n# else:\r\n# obj.action = False\r\n# obj.save()\r\n# temp_action = True\r\n# temp_status = False\r\n#\r\n# video = VideoDetail.objects.get(pk=obj.skigit.id)\r\n# video.inappropriate_skigit = temp_action\r\n# video.status = temp_status\r\n# video.save()\r\n#\r\n# # main_video = Video.objects.get(pk=video.skigit_id)\r\n# # main_video.delete()\r\n# inappropriate_skigit.short_description = \"Mark selected Skigit as Inappropriate\"\r\n\r\n\r\nclass MyModelForm(forms.ModelForm):\r\n \"\"\"\r\n My Model Form\r\n \"\"\"\r\n\r\n MY_CHOICES = (\r\n ('A', 'Choice A'),\r\n ('B', 'Choice B'),\r\n )\r\n stuff = forms.ChoiceField(choices=MY_CHOICES)\r\n\r\n\r\nclass InappropriateInvestigatorForm(forms.ModelForm):\r\n \"\"\"\r\n Inappropriate Investigator Admin Form\r\n \"\"\"\r\n\r\n def __init__(self, *args, **kwargs):\r\n super(InappropriateInvestigatorForm, self).__init__(*args, **kwargs)\r\n self.fields['result_remove_all'].choices = self.fields['result_remove_all'].choices[1:]\r\n self.fields['result_strike'].choices = self.fields['result_strike'].choices[1:]\r\n\r\n\r\nclass InappropriateInvestigatorInline(admin.StackedInline):\r\n \"\"\"\r\n Investigator Admin Inline\r\n \"\"\"\r\n form = InappropriateInvestigatorForm\r\n readonly_fields = ['get_investigator_name']\r\n fieldsets = (\r\n (\r\n None,\r\n {\r\n 'fields': ('get_investigator_name', 'result_remove_all', 'result_strike', 'investigation_discription',\r\n 'action_taken')\r\n }\r\n ),\r\n )\r\n\r\n radio_fields = {'result_remove_all': admin.HORIZONTAL, 'result_strike': admin.HORIZONTAL}\r\n model = InappropriateSkigitInvestigator\r\n max_num = 1\r\n\r\n def get_investigator_name(self, obj):\r\n \"\"\"\r\n Get Investigator Name\r\n \"\"\"\r\n if obj.investigating_user:\r\n if obj.investigating_user.get_full_name():\r\n return obj.investigating_user.get_full_name()\r\n else:\r\n return obj.investigating_user.username\r\n else:\r\n return ''\r\n get_investigator_name.allow_tags = True\r\n get_investigator_name.short_description = 'Investigator Name'\r\n\r\n def has_delete_permission(self, request, obj=None):\r\n return False\r\n\r\n\r\nclass InappropriateSkigitAdmin(admin.ModelAdmin):\r\n \"\"\"\r\n Inappropriate Model Admin\r\n \"\"\"\r\n inlines = [InappropriateInvestigatorInline]\r\n\r\n list_display = ('get_invastigation_id', 'related_skigit_title', 'related_skigit_category',\r\n 'related_skigit_create_user', 'get_strikes', 'related_skigit_busiess_user',\r\n 'related_inappropriate_reason', 'related_swf', 'created_date', 'status',\r\n 'get_inapp_action')\r\n readonly_fields = ['get_invastigation_id', 'get_user_name', 'get_skigit', 'reported_user', 'action', 'reason',\r\n 'get_submitter_email']\r\n fields = ['get_invastigation_id', 'get_user_name', 'get_skigit', 'reported_user', 'reason', 'get_submitter_email',\r\n 'status', 'action']\r\n fk_name = ('skigit', 'reported_user', 'reason')\r\n\r\n list_filter = ('skigit__category', 'reason', 'status', 'action', 'created_date')\r\n\r\n search_fields = ['skigit__skigit_id__title', 'id', 'skigit__skigit_id__user__username',\r\n 'skigit__skigit_id__user__first_name', 'skigit__category__cat_name', 'skigit__made_by__username',\r\n 'skigit__made_by__first_name', 'reason__reason_title']\r\n\r\n list_per_page = 15\r\n\r\n class Media:\r\n js = (\r\n '//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js', #jquery\r\n '/static/skigit/js/admin_js.js', #app static folder\r\n )\r\n\r\n def get_strikes(self, obj):\r\n if obj:\r\n strike_count = 0\r\n user_id = obj.skigit.skigit_id.user.id\r\n inappropriate_skigits = InappropriateSkigit.objects.filter(skigit__skigit_id__user__id=user_id)\r\n for inappro_skigit in inappropriate_skigits:\r\n if InappropriateSkigitInvestigator.objects.filter(id=inappro_skigit.id).exists():\r\n investigator = InappropriateSkigitInvestigator.objects.get(id=inappro_skigit.id)\r\n if investigator.get_result_strike_display() == 'Yes':\r\n strike_count += 1\r\n return strike_count\r\n get_strikes.allow_tags = True\r\n get_strikes.short_description = 'Strikes'\r\n\r\n def get_inapp_action(self, obj):\r\n if obj.action == '0':\r\n return 'Pending'\r\n elif obj.action == '1':\r\n return 'Appropriate'\r\n elif obj.action == '2':\r\n return 'Inappropriate'\r\n get_inapp_action.allow_tags = True\r\n get_inapp_action.short_description = 'Action'\r\n get_inapp_action.admin_order_field = 'action'\r\n\r\n def get_invastigation_id(self, obj):\r\n if obj.id:\r\n return '%010d' % obj.id\r\n get_invastigation_id.allow_tags = True\r\n get_invastigation_id.short_description = 'Investigation Id'\r\n get_invastigation_id.admin_order_field = 'id'\r\n\r\n def get_submitter_email(self, obj):\r\n if obj.reported_user:\r\n return '{0}'.format(obj.reported_user.email)\r\n get_submitter_email.allow_tags = True\r\n get_submitter_email.short_description = 'Submitter Email'\r\n get_submitter_email.admin_order_field = 'reported_user__user__email'\r\n\r\n\r\n def inappropriate_skigit_id(self, obj):\r\n return obj.id\r\n inappropriate_skigit_id.short_description = 'Inappropriate Skigit ID'\r\n\r\n def comaplaint_date(self, obj):\r\n return obj.created_date\r\n comaplaint_date.short_description = 'Complaint Date'\r\n\r\n def related_skigit_id(self, obj):\r\n return obj.skigit.id\r\n related_skigit_id.short_description = 'Skigit Id'\r\n\r\n def related_skigit_title(self, obj):\r\n return obj.skigit.title\r\n related_skigit_title.short_description = 'Skigit Title'\r\n\r\n def related_skigit_category(self, obj):\r\n return obj.skigit.category.cat_name\r\n related_skigit_category.short_description = 'Category'\r\n\r\n def related_reported_user_title(self, obj):\r\n return obj.reported_user.username\r\n related_reported_user_title.short_description = 'Submitted By'\r\n\r\n def related_reported_user_email(self, obj):\r\n return obj.reported_user.email\r\n related_reported_user_email.short_description = 'Submitter Email'\r\n\r\n def related_skigit_create_user(self, obj):\r\n return obj.skigit.skigit_id.user.username\r\n related_skigit_create_user.short_description = 'Skigit Creator'\r\n\r\n def related_skigit_busiess_user(self, obj):\r\n if obj.skigit.made_by:\r\n return obj.skigit.made_by.username\r\n elif obj.skigit.made_by_option:\r\n return obj.skigit.made_by_option\r\n else:\r\n return ''\r\n related_skigit_busiess_user.short_description = 'Submitter Name'\r\n\r\n def related_inappropriate_reason(self, obj):\r\n return obj.reason.reason_title\r\n related_inappropriate_reason.short_description = 'Inappropriate Reason'\r\n\r\n def related_swf(self, obj):\r\n return 'Show' % (obj.skigit.skigit_id.get_absolute_url())\r\n related_swf.allow_tags = True\r\n related_swf.short_description = 'Video Link'\r\n\r\n def select1(self, obj):\r\n return '' % obj.id\r\n select1.allow_tags = True\r\n select1.short_description = 'Action'\r\n\r\n def get_user_name(self, obj):\r\n if obj.reported_user:\r\n return obj.reported_user.get_full_name()\r\n else:\r\n return ''\r\n get_user_name.allow_tags = True\r\n get_user_name.short_description = 'Submitter Name'\r\n get_user_name.admin_order_field = 'bug_page_url'\r\n\r\n def get_skigit(self, obj):\r\n if obj.skigit:\r\n return \"Skigit ID: %s,\" \\\r\n \" Skigit Title: %s\"\\\r\n % (obj.skigit.skigit_id.id, obj.skigit.skigit_id.id, obj.skigit.skigit_id.id, obj.skigit.title)\r\n get_skigit.allow_tags = True\r\n get_skigit.short_description = 'Inappropriate work on Skigit'\r\n get_skigit.admin_order_field = 'skigit__skigit_id'\r\n\r\n def save_formset(self, request, form, formset, change):\r\n\r\n investigator = formset.save(commit=False)\r\n inappropriate = form.save(commit=False)\r\n inapp_status = InappropriateSkigit.objects.get(id=inappropriate.id).status\r\n result_remove_all = InappropriateSkigitInvestigator.objects.get(inapp_skigit=inappropriate.id).result_remove_all\r\n result_strike = InappropriateSkigitInvestigator.objects.get(inapp_skigit=inappropriate.id).result_strike\r\n for instance in investigator:\r\n instance.investigating_user = request.user\r\n instance.save()\r\n formset.save_m2m()\r\n if (inappropriate.status == '2'):\r\n inappropriate.status = '2'\r\n inappropriate.action = '0'\r\n if VideoDetail.objects.filter(id=inappropriate.skigit.id).exists():\r\n VideoDetail.objects.filter(id=inappropriate.skigit.id).update(status=1, inappropriate_skigit='0')\r\n elif(inappropriate.status == '1'):\r\n inappropriate.status = '1'\r\n inappropriate.action = '0'\r\n elif inappropriate.status == '3':\r\n InappropriateSkigitInvestigator.objects.filter(inapp_skigit=inappropriate.id).update(result_remove_all=False)\r\n inappropriate.action = '1'\r\n if VideoDetail.objects.filter(id=inappropriate.skigit.id).exists():\r\n VideoDetail.objects.filter(id=inappropriate.skigit.id).update(status=1, inappropriate_skigit='1')\r\n elif inappropriate.status == '4':\r\n InappropriateSkigitInvestigator.objects.filter(inapp_skigit=inappropriate.id).update(result_remove_all=True)\r\n inappropriate.action = '2'\r\n if VideoDetail.objects.filter(id=inappropriate.skigit.id).exists():\r\n VideoDetail.objects.filter(id=inappropriate.skigit.id).update(status=2, inappropriate_skigit='2')\r\n vidTitle = VideoDetail.objects.get(id=inappropriate.skigit.id).title\r\n vidUser = Video.objects.get(id=inappropriate.skigit.id)\r\n investigation_id = '%010d' % inappropriate.id\r\n message = \"
    \" \\\r\n \"Your Skigit \"+vidTitle+\" is inappropriate and has been removed from skigit.\" \\\r\n \"
    \"\r\n subject = \"Your Skigit is Inappropriate\"\r\n send_email(subject, message, vidUser.user.email, '', EMAIL_HOST_COPYRIGHT)\r\n message_complainer = \"
    \" \\\r\n \"

    Inappropriate Claim ID:\" +investigation_id+ \"

    \" \\\r\n \"The Skigit \"+vidTitle+\" was removed from Skigit.
    \" \\\r\n \"

    Best Regards,
    Skigit

    \"\r\n subject = \"Inappropriate Rejection\"\r\n send_email(subject, message_complainer, inappropriate.reported_user.email, '', EMAIL_HOST_COPYRIGHT)\r\n inappropriate.save()\r\n\r\n def save_model(self, request, obj, form, change):\r\n if InappropriateSkigitInvestigator.objects.filter(inapp_skigit=obj.id).exists():\r\n InappropriateSkigitInvestigator.objects.filter(\r\n inapp_skigit=obj.id).update(investigating_user=request.user)\r\n\r\n\r\n# Admin Bug Management\r\nclass AdminBugReportManagement(admin.ModelAdmin):\r\n readonly_fields = ['get_bug_id', 'user', 'skigit_id', 'get_skigit_id', 'bug_title', 'get_bug_page_url',\r\n 'get_created_date', 'get_bug_repeted', 'bug_description', 'get_updated_date']\r\n fields = ['get_bug_id', 'bug_title', 'user', 'get_skigit_id', 'skigit_id', 'get_bug_page_url', 'get_bug_repeted',\r\n 'bug_description', 'get_created_date', 'bug_comment', 'bug_status', 'get_updated_date', ]\r\n list_display = ['get_bug_id', 'bug_title', 'get_bug_page_url', 'bug_status', 'bug_repeated', 'get_created_date']\r\n actions = ['delete_selected', ]\r\n list_filter = ('bug_status', 'bug_repeated')\r\n search_fields = ['bug_title', 'id', 'bug_description', 'bug_status', 'bug_repeated']\r\n list_per_page = 20\r\n\r\n def get_created_date(self, obj):\r\n if obj.created_date:\r\n return obj.created_date.strftime(\"%d %b %Y\")\r\n return ''\r\n get_created_date.allow_tags = True\r\n get_created_date.admin_order_field = 'created_date'\r\n get_created_date.short_description = 'Date Reported'\r\n\r\n def get_updated_date(self, obj):\r\n if obj.updated_date:\r\n return obj.updated_date.strftime(\"%d %b %Y\")\r\n return ''\r\n get_updated_date.allow_tags = True\r\n get_updated_date.admin_order_field = 'updated_date'\r\n get_updated_date.short_description = 'Date submitted to maintenance'\r\n\r\n def get_bug_repeted(self, obj):\r\n if obj.bug_repeated:\r\n if obj.bug_repeated is True:\r\n return 'Yes'\r\n else:\r\n return 'No'\r\n return 'No'\r\n get_bug_repeted.allow_tags = True\r\n get_bug_repeted.short_description = 'Bug repeated'\r\n get_bug_repeted.admin_order_field = 'bug_repeated'\r\n\r\n def get_bug_id(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Url Of Youtube Video List Page\r\n \"\"\"\r\n return '%010d' % obj.id\r\n get_bug_id.allow_tags = True\r\n get_bug_id.short_description = 'Bug ID'\r\n get_bug_id.admin_order_field = 'id'\r\n\r\n def get_skigit_id(self, obj):\r\n if obj.skigit_id:\r\n return obj.skigit_id.id\r\n return ''\r\n get_skigit_id.allow_tags = True\r\n get_skigit_id.short_description = 'Skigit ID'\r\n get_skigit_id.admin_order_field = 'skigit_id__id'\r\n\r\n def get_bug_page_url(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Url Of Youtube Video List Page\r\n \"\"\"\r\n return '{0}'.format(obj.bug_page_url)\r\n get_bug_page_url.allow_tags = True\r\n get_bug_page_url.short_description = 'URL'\r\n get_bug_page_url.admin_order_field = 'bug_page_url'\r\n\r\n def has_delete_permission(self, request, obj=None):\r\n if request.user.is_superuser:\r\n return True\r\n else:\r\n return False\r\n\r\n def has_add_permission(self, request):\r\n if request.user.is_superuser:\r\n return True\r\n else:\r\n return False\r\nadmin.site.register(models.BugReport, AdminBugReportManagement)\r\n\r\n\r\nclass InvestigatorAdminForm(forms.ModelForm):\r\n\r\n def __init__(self, *args, **kwargs):\r\n super(InvestigatorAdminForm, self).__init__(*args, **kwargs)\r\n self.fields['remove_all'].choices = self.fields['remove_all'].choices[1:]\r\n self.fields['strike'].choices = self.fields['strike'].choices[1:]\r\n # self.fields['investigator_name'].queryset = Profile.objects.filter(copyright_investigation_rights=True)\r\n\r\n\r\nclass AdminInvestigatorInLine(admin.StackedInline):\r\n form = InvestigatorAdminForm\r\n readonly_fields = ['get_investigator_name']\r\n fieldsets = (\r\n (\r\n None,\r\n {\r\n 'fields': ('get_investigator_name', 'remove_all', 'strike', 'description', 'action')\r\n }\r\n ),\r\n )\r\n\r\n radio_fields = {'remove_all': admin.HORIZONTAL, 'strike': admin.HORIZONTAL}\r\n model = CopyRightInvestigation\r\n max_num = 1\r\n\r\n def get_investigator_name(self, obj):\r\n if obj.investigator_name:\r\n if obj.investigator_name.user.get_full_name():\r\n return obj.investigator_name.user.get_full_name()\r\n else:\r\n return obj.investigator_name.user.username\r\n else:\r\n return ''\r\n get_investigator_name.allow_tags = True\r\n get_investigator_name.short_description = 'Investigator Name'\r\n\r\n def has_delete_permission(self, request, obj=None):\r\n return False\r\n\r\n\r\nclass AdminCopyRightInfringement(admin.ModelAdmin):\r\n\r\n inlines = [AdminInvestigatorInLine]\r\n readonly_fields = ['get_copy_right_id', 'user_id', 'get_my_work_on_url', 'description',\r\n 'get_skigit_for_investigation', 'address', 'city', 'state', 'zip_code', 'country',\r\n 'phone', 'email', 'submitter_request', 'full_name', 'complaint_date']\r\n fields = ['get_copy_right_id', 'full_name', 'get_my_work_on_url', 'description', 'get_skigit_for_investigation',\r\n 'address', 'city', 'state', 'zip_code', 'country', 'phone', 'email', 'submitter_request', 'user_id',\r\n 'complaint_date', 'status']\r\n list_display = ['get_copy_right_id', 'user_id', 'get_my_work_on_url', 'phone', 'email', 'complaint_date',\r\n 'get_investigation_status']\r\n search_fields = ['id', 'complaint_date', 'email', 'phone']\r\n list_filter = ['status', ]\r\n list_per_page = 20\r\n\r\n def get_my_work_on_url(self, obj):\r\n \"\"\"\r\n Original work URL by Copyrigte complaint registered user\r\n \"\"\"\r\n return '{0}'.format(obj.urls)\r\n get_my_work_on_url.allow_tags = True\r\n get_my_work_on_url.short_description = 'User Identified work'\r\n get_my_work_on_url.admin_order_field = 'urls'\r\n\r\n def get_copy_right_id(self, obj):\r\n \"\"\"\r\n Args:\r\n obj: Instance\r\n Returns: Url Of Youtube Video List Page\r\n \"\"\"\r\n return '%010d' % obj.id\r\n get_copy_right_id.allow_tags = True\r\n get_copy_right_id.short_description = 'Investigation Id'\r\n get_copy_right_id.admin_order_field = 'id'\r\n\r\n def get_skigit_for_investigation(self, obj):\r\n \"\"\"\r\n Infringement Work on Skigit Name and ID\r\n \"\"\"\r\n if obj.skigit_id:\r\n if VideoDetail.objects.filter(id=obj.skigit_id).exists():\r\n skigitt = VideoDetail.objects.get(id=obj.skigit_id)\r\n return \"Skigit ID: {0}, \" \\\r\n \"Skigit Title: {1}\".format(\r\n obj.skigit_id, skigitt.title)\r\n else:\r\n return \"Skigit ID: %s\" % \\\r\n (obj.skigit_id, obj.skigit_id)\r\n else:\r\n return ''\r\n get_skigit_for_investigation.allow_tags = True\r\n get_skigit_for_investigation.short_description = 'Infringed work on Skigit'\r\n get_skigit_for_investigation.admin_order_field = 'skigit_id'\r\n\r\n def get_investigation_status(self, obj):\r\n if obj.status:\r\n if obj.status == 0:\r\n return \"Open\"\r\n elif obj.status == 1:\r\n return \"Under Investigation\"\r\n elif obj.status == 2:\r\n return \"Closed\"\r\n elif obj.status == 3:\r\n return \"Remove Skigit\"\r\n else:\r\n return \"Open\"\r\n else:\r\n return 'Open'\r\n get_investigation_status.allow_tags = True\r\n get_investigation_status.short_description = 'Status'\r\n get_investigation_status.admin_order_field = 'status'\r\n\r\n def has_add_permission(self, request):\r\n return False\r\n\r\n def has_delete_permission(self, request, obj=None):\r\n if request.user.is_superuser:\r\n return True\r\n else:\r\n return False\r\n\r\n def save_formset(self, request, form, formset, change):\r\n investigator = formset.save(commit=False)\r\n copy_right_content = form.save(commit=False)\r\n copy_status = CopyRightInfringement.objects.get(id=copy_right_content.id).status\r\n result_remove_all = CopyRightInvestigation.objects.get(copy_right=copy_right_content.id).remove_all\r\n result_strike = CopyRightInvestigation.objects.get(copy_right=copy_right_content.id).strike\r\n for instance in investigator:\r\n profile_user = Profile.objects.get(user=request.user.id)\r\n instance.investigator_name = profile_user\r\n instance.save()\r\n formset.save_m2m()\r\n if (copy_right_content.status == 0 or copy_right_content.status == 1) and (not result_remove_all or not result_strike):\r\n copy_right_content.status = 1\r\n if VideoDetail.objects.filter(id=copy_right_content.skigit_id).exists():\r\n VideoDetail.objects.filter(id=copy_right_content.skigit_id).update(status=1, copyright_skigit='0')\r\n elif copy_right_content.status == 2 and (not result_remove_all or not result_strike):\r\n CopyRightInvestigation.objects.filter(copy_right=copy_right_content.id).update(remove_all=False)\r\n if VideoDetail.objects.filter(id=copy_right_content.skigit_id).exists():\r\n VideoDetail.objects.filter(id=copy_right_content.skigit_id).update(status=1, copyright_skigit='1')\r\n if VideoDetail.objects.filter(id=copy_right_content.skigit_id).exists():\r\n skigitt = VideoDetail.objects.get(id=copy_right_content.skigit_id)\r\n investigation_id = '%010d' % copy_right_content.id\r\n message_user = \"\"\\\r\n \"
    \" \\\r\n \"

    \" \\\r\n \"Thank You for Tacking Action!

    \" \\\r\n \"

    Copyright Infringement Claim ID:\" +investigation_id+ \"

    \"\\\r\n \"

    The Skigit \" \\\r\n \"\" + skigitt.title + \\\r\n \"  was determined to be acceptable.

    Best Regards,
    \" \\\r\n \"Skigit

    \" \\\r\n \"\"\\\r\n \"
    \"\r\n subject = \"Copyright Acceptance\"\r\n send_email(subject, message_user, copy_right_content.email, '', EMAIL_HOST_COPYRIGHT)\r\n elif copy_right_content.status == 3:\r\n CopyRightInvestigation.objects.filter(copy_right=copy_right_content.id).update(remove_all=True)\r\n if VideoDetail.objects.filter(id=copy_right_content.skigit_id).exists():\r\n VideoDetail.objects.filter(id=copy_right_content.skigit_id).update(status=2, copyright_skigit='2')\r\n vidTitle = VideoDetail.objects.get(id=copy_right_content.skigit_id).title\r\n vidUser = Video.objects.get(id=copy_right_content.skigit_id)\r\n investigation_id = '%010d' % copy_right_content.id\r\n message = \"
    \" \\\r\n \"Your Skigit \"+vidTitle+\" was rejected due to copyright violation.\" \\\r\n \"
    \"\r\n subject = \"Skigit Copyright Violation\"\r\n send_email(subject, message, vidUser.user.email, '', EMAIL_HOST_COPYRIGHT)\r\n message_complainer = \"
    \" \\\r\n \"

    Copyright Infringement Claim ID:\" +investigation_id+ \"

    \" \\\r\n \"The Skigit \"+vidTitle+\" was removed from Skigit.
    \" \\\r\n \"

    Best Regards,
    Skigit

    \"\r\n\r\n subject = \"Copyright Rejection\"\r\n send_email(subject, message_complainer, copy_right_content.email, '', EMAIL_HOST_COPYRIGHT)\r\n copy_right_content.save()\r\n\r\n def save_model(self, request, obj, form, change):\r\n if CopyRightInvestigation.objects.filter(copy_right=obj.id).exists():\r\n CopyRightInvestigation.objects.filter(copy_right=obj.id).update(investigator_name=request.user)\r\n else:\r\n profile_user = Profile.objects.get(user=request.user.id)\r\n CopyRightInvestigation.objects.create(copy_right=obj, investigator_name=profile_user)\r\n\r\n\r\nadmin.site.register(models.CopyRightInfringement, AdminCopyRightInfringement)\r\nadmin.site.register(models.Video, VideoAdmin)\r\nadmin.site.register(models.VideoDetail, VideoDetailAdmin)\r\nadmin.site.register(models.InappropriateSkigit, InappropriateSkigitAdmin)\r\nadmin.site.register(models.Category, CategoryInline)\r\nadmin.site.register(models.SubjectCategory, SubjectCategoryInline)\r\nadmin.site.register(models.InappropriateSkigitReason, InappropriateSkigitReasonAdmin)\r\nadmin.site.register(Donation)\r\n","sub_path":"skigit/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":86382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"432361041","text":"# -*- coding:utf-8 -*-\n# Author : ZRQ\n# Data : 2019/6/27 21:14\n\nfrom threading import Thread\nimport time,os\ndef talk1():\n time.sleep(2)\n print('hello')\ndef talk2():\n time.sleep(2)\n print('you see see')\n\nif __name__ == '__main__':\n t1 = Thread(target=talk1)\n t2 = Thread(target=talk2)\n t1.daemon = True\n t1.start()\n t2.start()\n print('主线程',os.getpid())\n","sub_path":"多线程/05守护.py","file_name":"05守护.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"76171587","text":"import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport skimage.transform as sktr\n\n# 2.1. Harris corner detector \n\nfilename = 'data_q2/checkboard.png'\nimg = cv2.imread(filename)\ngray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n\ngray = np.float32(gray)\n# here is the cfunction call \n# k= 0.04, blockSize = 2\ndst = my_harris(img,k,blockSize) \n# should behave similar to \n# dst = cv2.cornerHarris(gray,2,3,0.04)\n\n# To get corner locations : threshold for an optimal value, it may vary depending on the image.\n# For visualization - put red color for corners\nimg[dst>0.01*dst.max()]=[0,0,255]\n\ncv2.imshow('dst',img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n#-------------------\n# 2.2 SIFT \nimg = cv2.imread('data_q2/episcopal_gaudi1.jpg',0) \nimg = cv2.resize(img,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)\n\n# Initiate SIFT detector\n# ...\n# compute the sift detectors (kp) and descriptors (des) using cv2 functions\n# ...\n\n# visualize results\nimg_d = cv2.drawKeypoints(img,kp,outImage=np.array([]),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\ncv2.imshow('SIFTfeatures',img_d)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n#cv2.imwrite('sift_keypoints.jpg',img)\n\n\n#-------------------\n# 2.3 Matching SIFT features \n\nimg1 = cv2.imread('data_q2/episcopal_gaudi1.jpg',0) # queryImage\nimg2 = cv2.imread('data_q2/episcopal_gaudi2.jpg',0) # trainImage\nimg1 = cv2.resize(img1,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)\nimg2 = cv2.resize(img2,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)\n\n# Initiate SIFT detector\n# ...\n\n# find the keypoints and descriptors with SIFT for the two images\n# descriptors returned as des1, des2\n# ...\n\n# initialize BFMatcher \n# ...\n\n# 2.3.1 brute force matching : compute and sort matches points into list matches\n# ...\n# Draw first 10 matches.\nimg_d = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], None, flags=2)\ncv2.imshow('SIFTmatches features-brute force',img_d)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n# 2.3.2 ratio distance ; use knnMatch with k=2 ; \n# gives list of best two matches for each feature\n# ...\n# Apply ratio test and save good matches in a list of DMatch elements\n# ...\n# Draw good matches \nimg_d = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good_match,None,flags=2)\ncv2.imshow('SIFTmatches features',img_d)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","sub_path":"A2/main_q2.py","file_name":"main_q2.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"381879725","text":"#!/usr/bin/python\nimport re\nimport socket\nimport uuid\nfrom json import load\nfrom urllib.request import urlopen\nimport os\nimport psutil\nfrom qqwry import QQwry, updateQQwry\n\nDISK_USAGE_LIMT = 70 # 磁盘使用界限值70%\nDIST_DISK = \"/\" # 目标监控磁盘\nCLEAN_TEMP_PATH = \"/*.7z\" # 清理路径\n\n\ndef get_host_ip(): # 获取本机ip地址\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.connect(('8.8.8.8', 80))\n p = s.getsockname()[0]\n print(\"本机ip地址：%s\" % p)\n\n\ndef get_mac_address(): # 获取本机mac地址\n node = uuid.getnode()\n mac = uuid.UUID(int=node).hex[-12:]\n return \":\".join([mac[e:e + 2] for e in range(0, 11, 2)])\n\n\ndef get_out_ip(): # 获取外网ip\n p = load(urlopen('http://httpbin.org/ip'))['origin']\n print(\"外网ip：%s\" % p)\n\n\n# 用os.system()去调用unix命令,返回0表示执行成功\ndef disk_cleaner():\n global CLEAN_TEMP_PATH\n cmd = \"ls -al {}\".format(CLEAN_TEMP_PATH)\n cmd_flag = os.system(cmd)\n cmd2 = \"rm {}\".format(CLEAN_TEMP_PATH)\n cmd2_flag = os.system(cmd2)\n print(\"Cleanup status {} {}\".format(cmd_flag, cmd2_flag))\n\n\n# disk_usage判断是否超出界限值\ndef monitor_disk():\n global DISK_USAGE_LIMT\n global DIST_DISK\n disk_percent = psutil.disk_usage(DIST_DISK).percent\n if disk_percent > DISK_USAGE_LIMT:\n print(\"Disk space usage: {}%, processing to cleanup... \".format(disk_percent))\n disk_cleaner()\n else:\n print(\"Disk space usage: {}%\".format(disk_percent))\n\n\ndef check_ip(urls): # 域名查ip\n p = socket.gethostbyname(urls)\n print(\"ip地址为：\", p)\n return p\n\n\n# 查询归属地\ndef check_Attribution(p):\n q = QQwry()\n q.load_file('qqwry.dat', False)\n result = q.lookup(p)\n print(\"归属地为:\", result)\n\n\n# 更新归属地数据\ndef check_Attribution_db():\n print(\"开始更新.......\")\n updateQQwry('qqwry.dat')\n print(\"更新完成。\")\n\n\ndef ping_ip():\n p = input(\"请输入ip地址：\")\n os.system(\"ping -c 4 %s\" % p)\n\n\nif __name__ == \"__main__\":\n while True:\n print(\"---------------网络查询小工具---------------------\")\n print('请选择要查看的选项：')\n print('1、本机mac地址')\n print('2、本机ip地址')\n print('3、外网地址')\n print('4、磁盘使用率')\n print('5、根据域名或ip查询归属地')\n print('6、归属地数据库更新')\n print('7、ping命令')\n print('8、退出程序')\n swicth_id = input(\"请选择上面某一功能(数字1-8)：\")\n if swicth_id == '1':\n print(\"本机mac地址:%s\" % get_mac_address())\n elif swicth_id == '2':\n get_host_ip()\n elif swicth_id == '3':\n get_out_ip()\n elif swicth_id == '4':\n print('------监控磁盘使用率-------')\n monitor_disk()\n disk_cleaner()\n elif swicth_id == '5':\n print('-------------------------')\n url = input(\"请输入要查询的域名或ip地址：\")\n if re.match(r\"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$\",\n url):\n check_Attribution(url)\n else:\n ip = check_ip(url)\n check_Attribution(ip)\n elif swicth_id == '6':\n print('-------------------------')\n check_Attribution_db()\n elif swicth_id == '7':\n ping_ip()\n elif swicth_id == '8':\n print(\"谢谢使用！\")\n exit()\n else:\n print(\"没有此选项！\")\n","sub_path":"mian/smallUtil.py","file_name":"smallUtil.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"218491180","text":"import socket, sys\n\nHOST = 'localhost'\nPORT = 8080\n\n# 1) create socket :\nmySocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# 2) send connexion request to server:\ntry:\n mySocket.connect((HOST, PORT))\nexcept socket.error:\n print(\"connexion failed\")\n sys.exit()\n\n# 3)communicate with server :\n\nmsgServer = mySocket.recv(1024).decode(\"Utf8\")\nprint(\"S>\", msgServer)\n\nwhile 1:\n #msgClient = input(\"C> \")\n #mySocket.send(msgClient.encode(\"Utf8\"))\n msgServer = mySocket.recv(1024).decode(\"Utf8\")\n print(\"S>\", msgServer)\n #mySocket.send(\"new\".encode(\"Utf8\"))\n\n# 4) close connexion :\nprint(\"Connexion ended.\")\nmySocket.close()","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"186520503","text":"import re\nclass Producto():\n existencia = 0\n def __init__(self, nombre='', precio=0.0):\n self.nombre = nombre\n self.precio = precio\n \n def iva(self):\n return self.precio * 0.16\n\n def entrada(self, cant):\n self.existencia += cant\n\n def salida(self, cant):\n self.existencia -= cant\n\ndef validareal(s):\n while True:\n num = input(s)\n if re.match(r'^[+]?\\d*\\.?\\d*([E|e][+|-]?\\d*)?$', num):\n return float(num)\n print('Debe ingresar un número real positivo.')\n\ndef validaentero(s):\n while True:\n num = input(s)\n if re.match(r'^[+]?\\d+$', num):\n return int(num)\n print('Debe ingresar un número entero positivo.')\n\nnombre = input('Nombre del producto: ')\nprecio = validareal(\"Ingrese el precio: \")\na = Producto(nombre, precio)\ncompra = validaentero('Ingrese la cantidad de producto entrante: ')\na.entrada(compra)\n\nprint('Existencia:', a.existencia)\nventa = validaentero('Ingrese la cantidad de producto vendido: ')\na.salida(venta)\nprint('Existencia:', a.existencia)\n","sub_path":"Productos.py","file_name":"Productos.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"117036067","text":"#!/usr/bin/python\nimport csv\n\ndef get_reader():\n\tcsvfile = open('../out/svm/parameter_gamma.log','r')\n\treader = csv.reader(csvfile, delimiter='\\t', quotechar='\\\"')\n\tnext(reader)\n\tnext(reader)\n\treturn reader\n\ndef get_writer():\n\tcsvfile = open('../out/svm/fscore_gamma.csv','w')\n\twriter = csv.writer(csvfile)\n\twriter.writerow(('f1-score','gamma','c', 'd'))\n\treturn writer\n\n\nrow_precision = 4 \nrow_recall = 5\nrow_gamma = 3\nrow_c = 0\nrow_degree = 2\n\nreader = get_reader()\nwriter = get_writer()\nstats = []\nfor line in reader:\n\tc=float(line[row_c])\n\tgamma=float(line[row_gamma])\n\trecall=float(line[row_recall])\n\tdegree=float(line[row_degree])\n\tprecision=float(line[row_precision])\n\tfscore = 2*(precision*recall)/(precision+recall)\n\tstats+=[(fscore,gamma,c,degree)]\n\nfor f,g,c,d in sorted(stats, key=lambda x: x[0]):\n\tprint('f-sorted: '+str(f))\n\tprint('-------------------')\n\twriter.writerow((f,g,c,d))\n\n\n\n","sub_path":"scripts/fscore_gamma.py","file_name":"fscore_gamma.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"318675916","text":"import numpy as np\nfrom processing.models.pytorch_tools.gradient_reversal import RevGrad\nimport torch\nimport os\nfrom processing.models.deep_tl_predictor import DeepTLPredictor\nimport torch.nn as nn\nfrom processing.models.pytorch_tools.training import fit, predict\n\nclass RETAIN_ATL(DeepTLPredictor):\n def __init__(self, subject, ph, params, train, valid, test):\n super().__init__(subject, ph, params, train, valid, test)\n\n self.model = self.RETAIN_Module(self.input_shape, self.params[\"n_features_emb\"], self.params[\"n_hidden_rnn\"],\n self.params[\"n_layers_rnn\"], self.params[\"emb_dropout\"],\n self.params[\"ctx_dropout\"],\n self.params[\"reverse_time\"], self.params[\"bidirectional\"], self.n_domains)\n\n self.model_parameters = [\n {'params': self.model.embeddings.parameters()},\n {'params': self.model.rnn_alpha.parameters(), 'weight_decay': 0},\n {'params': self.model.rnn_beta.parameters(), 'weight_decay': 0},\n {'params': self.model.alpha.parameters()},\n {'params': self.model.beta.parameters()},\n {'params': self.model.output.parameters()},\n ]\n\n self.model.cuda()\n\n def fit(self):\n x_train, y_train, t_train = self._str2dataset(\"train\")\n x_valid, y_valid, t_valid = self._str2dataset(\"valid\")\n train_ds = self._to_tensor_ds(x_train, y_train)\n valid_ds = self._to_tensor_ds(x_valid, y_valid)\n\n self.loss_func = self._compute_loss_func()\n\n self.opt = torch.optim.Adam(self.model_parameters, lr=self.params[\"lr\"], weight_decay=self.params[\"l2\"])\n\n fit(self.params[\"epochs\"], self.params[\"batch_size\"], self.model, self.loss_func, self.opt, train_ds, valid_ds,\n self.params[\"patience\"], self.checkpoint_file)\n\n def predict(self, dataset, clear=True):\n # get the data for which we make the predictions\n x, y, t = self._str2dataset(dataset)\n ds = self._to_tensor_ds(x, y)\n\n # create the model\n self.model.load_state_dict(torch.load(self.checkpoint_file))\n\n if self.params[\"domain_adversarial\"]:\n [y_trues_glucose, y_trues_subject], [y_preds_glucose, y_preds_subject] = predict(self.model, ds)\n results = self._format_results_source(y_trues_glucose, y_trues_subject, y_preds_glucose, y_preds_subject, t)\n else:\n y_true, y_pred = predict(self.model, ds)\n results = self._format_results(y_true, y_pred, t)\n\n if clear:\n self._clear_checkpoint()\n\n return results\n\n def save(self, file):\n self.model.load_state_dict(torch.load(self.checkpoint_file))\n no_da_retain = self.RETAIN_Module(self.input_shape, self.params[\"n_features_emb\"], self.params[\"n_hidden_rnn\"],\n self.params[\"n_layers_rnn\"], self.params[\"emb_dropout\"],\n self.params[\"ctx_dropout\"],\n self.params[\"reverse_time\"], self.params[\"bidirectional\"], 0)\n no_da_retain.embeddings.load_state_dict(self.model.embeddings.state_dict())\n no_da_retain.rnn_alpha.load_state_dict(self.model.rnn_alpha.state_dict())\n no_da_retain.rnn_beta.load_state_dict(self.model.rnn_beta.state_dict())\n no_da_retain.alpha.load_state_dict(self.model.alpha.state_dict())\n no_da_retain.beta.load_state_dict(self.model.beta.state_dict())\n no_da_retain.output.load_state_dict(self.model.output.state_dict())\n if not os.path.exists(os.path.dirname(file)):\n os.makedirs(os.path.dirname(file))\n torch.save(no_da_retain.state_dict(), file)\n\n def _compute_input_shape(self):\n x_train, _, _ = self._str2dataset(\"train\")\n return x_train.shape[2]\n\n def _reshape(self, data):\n x, y, t = super()._reshape(data)\n return x, y, t\n\n def get_attention_weights(self, dataset):\n x, y, t = self._str2dataset(dataset)\n ds = self._to_tensor_ds(x, y)\n\n emb = self.model.compute_embeddings(ds[0])\n alpha, beta = self.model.compute_alpha_beta(emb)\n\n return alpha, beta\n\n def get_embeddings_attr(self):\n return self.model.embeddings.weight\n\n def get_output_attr(self):\n return self.model.output.weight, self.model.output.bias\n\n def contribution(self, dataset):\n x, y, t = self._str2dataset(dataset)\n # ds = self._to_tensor_ds(x, y)\n\n xb = torch.Tensor(x).cuda()\n # xb = torch.Tensor(x)\n\n emb = self.model.compute_embeddings(xb)\n alpha, beta = self.model.compute_alpha_beta(emb)\n\n w_emb = self.model.embeddings.weight\n w_out, b_out = self.model.output.weight, self.model.output.bias\n\n contrib = []\n for i in range(xb.shape[1]):\n contrib_i = []\n for j in range(xb.shape[2]):\n contrib_ij = (alpha[:, i, :] * (\n torch.matmul((beta[:, i, :] * w_emb[:, j]), w_out.transpose(1, 0)))).squeeze() * xb[:, i, j]\n contrib_i.append(contrib_ij.detach().cpu().numpy())\n contrib.append(contrib_i)\n contrib = np.array(contrib)\n\n return contrib.transpose(2, 0, 1)\n\n def contribution_an(self, dataset):\n contrib = self.contribution(dataset)\n absolute_contrib = np.abs(contrib)\n sum_contrib = np.expand_dims(absolute_contrib.sum(1).sum(1),axis=[1,2])\n contrib_an = absolute_contrib / sum_contrib\n return contrib_an\n\n def extract_features(self, dataset, file):\n x, y, _ = self._str2dataset(dataset)\n self.model.load_state_dict(torch.load(file))\n self.model.eval()\n\n xb = torch.Tensor(x).cuda()\n emb = self.model.compute_embeddings(xb)\n alpha, beta = self.model.compute_alpha_beta(emb)\n c = self.model.compute_c(emb, alpha, beta)\n\n c = c.detach().cpu().numpy().reshape(c.shape[0], -1)\n\n return [c, y]\n\n class RETAIN_Module(nn.Module):\n\n def __init__(self, n_in, n_features_emb, n_hidden_rnn, n_layers_rnn, emb_dropout, ctx_dropout,\n reverse_time=True, bidirectional=False, adversarial_domains=0):\n super().__init__()\n\n self.embeddings = nn.Linear(n_in, n_features_emb, bias=False)\n self.rnn_alpha = nn.LSTM(n_features_emb, n_hidden_rnn, n_layers_rnn, batch_first=True,\n bidirectional=bidirectional)\n self.rnn_beta = nn.LSTM(n_features_emb, n_hidden_rnn, n_layers_rnn, batch_first=True,\n bidirectional=bidirectional)\n self.reverse_time = reverse_time\n self.emb_dropout = nn.Dropout(emb_dropout)\n self.ctx_dropout = nn.Dropout(ctx_dropout)\n\n self.alpha = nn.Sequential(\n nn.Linear(n_hidden_rnn * (1 + int(bidirectional)), 1, bias=True),\n nn.Softmax(dim=1)\n # Sparsemax(dim=1)\n )\n\n self.beta = nn.Sequential(\n nn.Linear(n_hidden_rnn * (1 + int(bidirectional)), n_features_emb, bias=True),\n nn.Tanh()\n )\n\n self.output = nn.Linear(n_features_emb, 1, bias=True)\n\n if adversarial_domains != 0:\n self.domain_classifier = nn.Sequential(\n RevGrad(),\n nn.Linear(n_features_emb, adversarial_domains, bias=True),\n nn.LogSoftmax(dim=1)\n )\n else:\n self.domain_classifier = None\n\n def forward(self, xb):\n emb = self.compute_embeddings(xb)\n alpha, beta = self.compute_alpha_beta(emb)\n c = self.compute_c(emb, alpha, beta)\n\n if self.domain_classifier is not None:\n return self.output(c).reshape((-1)), self.domain_classifier(c)\n else:\n return self.output(c).reshape((-1))\n\n def compute_embeddings(self, xb):\n emb = self.embeddings(xb)\n if self.reverse_time:\n emb = emb.flip(dims=[1])\n return self.emb_dropout(emb)\n\n def compute_alpha_beta(self, emb):\n return self.alpha(self.rnn_alpha(emb)[0]), self.beta(self.rnn_beta(emb)[0])\n\n def compute_c(self, emb, alpha, beta):\n return self.ctx_dropout((alpha * beta * emb).sum(dim=1))\n","sub_path":"processing/models/retain_atl.py","file_name":"retain_atl.py","file_ext":"py","file_size_in_byte":8471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"455385725","text":"# Script for creating different kind of indexes in a small space as possible.\n# This is intended for testing purposes.\n\nfrom tables import *\n\nclass Descr(IsDescription):\n var1 = StringCol(itemsize=4, shape=(), dflt='', pos=0)\n var2 = BoolCol(shape=(), dflt=False, pos=1)\n var3 = Int32Col(shape=(), dflt=0, pos=2)\n var4 = Float64Col(shape=(), dflt=0.0, pos=3)\n\n# Parameters for the table and index creation\nsmall_chunkshape = (2,)\nsmall_blocksizes = (64, 32, 16, 8)\nnrows = 43\n\n# Create the new file\nf = openFile('indexes_2_1.h5', 'w')\nt1 = f.createTable(f.root, 'table1', Descr)\nrow = t1.row\nfor i in range(nrows):\n row['var1'] = i\n row['var2'] = i\n row['var3'] = i\n row['var4'] = i\n row.append()\nt1.flush()\n\n# Do a copy of table1\nt1.copy(f.root, 'table2')\n\n# Create indexes of all kinds\nt1.cols.var1.createIndex(0,'ultralight',_blocksizes=small_blocksizes)\nt1.cols.var2.createIndex(3,'light',_blocksizes=small_blocksizes)\nt1.cols.var3.createIndex(6,'medium',_blocksizes=small_blocksizes)\nt1.cols.var4.createIndex(9,'full',_blocksizes=small_blocksizes)\n\nf.close()\n","sub_path":"tables/tests/create_backcompat_indexes.py","file_name":"create_backcompat_indexes.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"408914323","text":"# -*- coding: utf-8 -*-\n\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('comissoes', '0001_initial'),\n ('parlamentares', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='AcompanhamentoMateria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('email', models.CharField(max_length=100, verbose_name='Endere\\xe7o de E-mail')),\n ('hash', models.CharField(max_length=8)),\n ],\n options={\n 'verbose_name': 'Acompanhamento de Mat\\xe9ria',\n 'verbose_name_plural': 'Acompanhamentos de Mat\\xe9ria',\n },\n ),\n migrations.CreateModel(\n name='Anexada',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('data_anexacao', models.DateField(verbose_name='Data Anexa\\xe7\\xe3o')),\n ('data_desanexacao', models.DateField(null=True, verbose_name='Data Desanexa\\xe7\\xe3o', blank=True)),\n ],\n options={\n 'verbose_name': 'Anexada',\n 'verbose_name_plural': 'Anexadas',\n },\n ),\n migrations.CreateModel(\n name='AssuntoMateria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('assunto', models.CharField(max_length=200)),\n ('dispositivo', models.CharField(max_length=50)),\n ],\n options={\n 'verbose_name': 'Assunto de Mat\\xe9ria',\n 'verbose_name_plural': 'Assuntos de Mat\\xe9ria',\n },\n ),\n migrations.CreateModel(\n name='Autor',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('nome', models.CharField(max_length=50, null=True, verbose_name='Autor', blank=True)),\n ('cargo', models.CharField(max_length=50, null=True, blank=True)),\n ('username', models.CharField(max_length=50, null=True, blank=True)),\n ('comissao', models.ForeignKey(blank=True, to='comissoes.Comissao', null=True)),\n ('parlamentar', models.ForeignKey(blank=True, to='parlamentares.Parlamentar', null=True)),\n ('partido', models.ForeignKey(blank=True, to='parlamentares.Partido', null=True)),\n ],\n options={\n 'verbose_name': 'Autor',\n 'verbose_name_plural': 'Autores',\n },\n ),\n migrations.CreateModel(\n name='Autoria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('primeiro_autor', models.BooleanField(verbose_name='Primeiro Autor')),\n ('autor', models.ForeignKey(to='materia.Autor')),\n ],\n options={\n 'verbose_name': 'Autoria',\n 'verbose_name_plural': 'Autorias',\n },\n ),\n migrations.CreateModel(\n name='DespachoInicial',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('numero_ordem', models.IntegerField()),\n ('comissao', models.ForeignKey(to='comissoes.Comissao')),\n ],\n options={\n 'verbose_name': 'Despacho Inicial',\n 'verbose_name_plural': 'Despachos Iniciais',\n },\n ),\n migrations.CreateModel(\n name='DocumentoAcessorio',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('nome', models.CharField(max_length=30, verbose_name='Descri\\xe7\\xe3o')),\n ('data', models.DateField(null=True, verbose_name='Data', blank=True)),\n ('autor', models.CharField(max_length=50, null=True, verbose_name='Autor', blank=True)),\n ('ementa', models.TextField(null=True, verbose_name='Ementa', blank=True)),\n ('indexacao', models.TextField(null=True, blank=True)),\n ],\n options={\n 'verbose_name': 'Documento Acess\\xf3rio',\n 'verbose_name_plural': 'Documentos Acess\\xf3rios',\n },\n ),\n migrations.CreateModel(\n name='MateriaAssunto',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('assunto', models.ForeignKey(to='materia.AssuntoMateria')),\n ],\n options={\n 'verbose_name': 'Rela\\xe7\\xe3o Mat\\xe9ria - Assunto',\n 'verbose_name_plural': 'Rela\\xe7\\xf5es Mat\\xe9ria - Assunto',\n },\n ),\n migrations.CreateModel(\n name='MateriaLegislativa',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('numero_protocolo', models.IntegerField(null=True, verbose_name='N\\xfam. Protocolo', blank=True)),\n ('numero_ident_basica', models.IntegerField(verbose_name='N\\xfamero')),\n ('ano_ident_basica', models.SmallIntegerField(verbose_name='Ano')),\n ('data_apresentacao', models.DateField(null=True, verbose_name='Data Apresenta\\xe7\\xe3o', blank=True)),\n ('tipo_apresentacao', models.CharField(blank=True, max_length=1, null=True, verbose_name='Tipo de Apresenta\\xe7\\xe3o', choices=[(b'O', 'Oral'), (b'E', 'Escrita')])),\n ('data_publicacao', models.DateField(null=True, verbose_name='Data Publica\\xe7\\xe3o', blank=True)),\n ('numero_origem_externa', models.CharField(max_length=5, null=True, verbose_name='N\\xfamero', blank=True)),\n ('ano_origem_externa', models.SmallIntegerField(null=True, verbose_name='Ano', blank=True)),\n ('data_origem_externa', models.DateField(null=True, verbose_name='Data', blank=True)),\n ('apelido', models.CharField(max_length=50, null=True, verbose_name='Apelido', blank=True)),\n ('dias_prazo', models.IntegerField(null=True, verbose_name='Dias Prazo', blank=True)),\n ('data_fim_prazo', models.DateField(null=True, verbose_name='Data Fim Prazo', blank=True)),\n ('em_tramitacao', models.BooleanField(verbose_name='Em Tramita\\xe7\\xe3o?')),\n ('polemica', models.NullBooleanField(verbose_name='Mat\\xe9ria Pol\\xeamica?')),\n ('objeto', models.CharField(max_length=150, null=True, verbose_name='Objeto', blank=True)),\n ('complementar', models.NullBooleanField(verbose_name='\\xc9 Complementar?')),\n ('ementa', models.TextField(verbose_name='Ementa')),\n ('indexacao', models.TextField(null=True, verbose_name='Indexa\\xe7\\xe3o', blank=True)),\n ('observacao', models.TextField(null=True, verbose_name='Observa\\xe7\\xe3o', blank=True)),\n ('resultado', models.TextField(null=True, blank=True)),\n ('anexadas', models.ManyToManyField(related_name='anexo_de', through='materia.Anexada', to='materia.MateriaLegislativa')),\n ],\n options={\n 'verbose_name': 'Mat\\xe9ria Legislativa',\n 'verbose_name_plural': 'Mat\\xe9rias Legislativas',\n },\n ),\n migrations.CreateModel(\n name='Numeracao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('numero_ordem', models.IntegerField()),\n ('numero_materia', models.CharField(max_length=5, verbose_name='N\\xfamero')),\n ('ano_materia', models.SmallIntegerField(verbose_name='Ano')),\n ('data_materia', models.DateField(null=True, verbose_name='Data', blank=True)),\n ('materia', models.ForeignKey(to='materia.MateriaLegislativa')),\n ],\n options={\n 'verbose_name': 'Numera\\xe7\\xe3o',\n 'verbose_name_plural': 'Numera\\xe7\\xf5es',\n },\n ),\n migrations.CreateModel(\n name='Orgao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('nome', models.CharField(max_length=60, verbose_name='Nome')),\n ('sigla', models.CharField(max_length=10, verbose_name='Sigla')),\n ('unidade_deliberativa', models.BooleanField(verbose_name='Unidade Deliberativa')),\n ('endereco', models.CharField(max_length=100, null=True, verbose_name='Endere\\xe7o', blank=True)),\n ('telefone', models.CharField(max_length=50, null=True, verbose_name='Telefone', blank=True)),\n ],\n options={\n 'verbose_name': '\\xd3rg\\xe3o',\n 'verbose_name_plural': '\\xd3rg\\xe3os',\n },\n ),\n migrations.CreateModel(\n name='Origem',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('sigla', models.CharField(max_length=10, verbose_name='Sigla')),\n ('nome', models.CharField(max_length=50, verbose_name='Nome')),\n ],\n options={\n 'verbose_name': 'Origem',\n 'verbose_name_plural': 'Origens',\n },\n ),\n migrations.CreateModel(\n name='Parecer',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('tipo_conclusao', models.CharField(max_length=3, null=True, blank=True)),\n ('tipo_apresentacao', models.CharField(max_length=1, choices=[(b'O', 'Oral'), (b'E', 'Escrita')])),\n ('parecer', models.TextField(null=True, blank=True)),\n ('materia', models.ForeignKey(to='materia.MateriaLegislativa')),\n ],\n options={\n 'verbose_name': 'Parecer',\n 'verbose_name_plural': 'Pareceres',\n },\n ),\n migrations.CreateModel(\n name='Proposicao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('data_envio', models.DateTimeField(null=True, verbose_name='Data de Envio')),\n ('data_recebimento', models.DateTimeField(null=True, verbose_name='Data de Incorpora\\xe7\\xe3o', blank=True)),\n ('descricao', models.CharField(max_length=100, verbose_name='Descri\\xe7\\xe3o')),\n ('data_devolucao', models.DateTimeField(null=True, verbose_name='Data de devolu\\xe7\\xe3o', blank=True)),\n ('justificativa_devolucao', models.CharField(max_length=200, null=True, verbose_name='Justificativa da Devolu\\xe7\\xe3o', blank=True)),\n ('numero_proposicao', models.IntegerField(null=True, verbose_name='', blank=True)),\n ('autor', models.ForeignKey(to='materia.Autor')),\n ('documento', models.ForeignKey(verbose_name='Documento', blank=True, to='materia.DocumentoAcessorio', null=True)),\n ('materia', models.ForeignKey(verbose_name='Mat\\xe9ria', blank=True, to='materia.MateriaLegislativa', null=True)),\n ],\n options={\n 'verbose_name': 'Proposi\\xe7\\xe3o',\n 'verbose_name_plural': 'Proposi\\xe7\\xf5es',\n },\n ),\n migrations.CreateModel(\n name='RegimeTramitacao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descricao', models.CharField(max_length=50)),\n ],\n options={\n 'verbose_name': 'Regime Tramita\\xe7\\xe3o',\n 'verbose_name_plural': 'Regimes Tramita\\xe7\\xe3o',\n },\n ),\n migrations.CreateModel(\n name='Relatoria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('data_designacao_relator', models.DateField(verbose_name='Data Designa\\xe7\\xe3o')),\n ('data_destituicao_relator', models.DateField(null=True, verbose_name='Data Destitui\\xe7\\xe3o', blank=True)),\n ('comissao', models.ForeignKey(verbose_name='Localiza\\xe7\\xe3o Atual', blank=True, to='comissoes.Comissao', null=True)),\n ('materia', models.ForeignKey(to='materia.MateriaLegislativa')),\n ('parlamentar', models.ForeignKey(verbose_name='Parlamentar', to='parlamentares.Parlamentar')),\n ],\n options={\n 'verbose_name': 'Relatoria',\n 'verbose_name_plural': 'Relatorias',\n },\n ),\n migrations.CreateModel(\n name='StatusTramitacao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('sigla', models.CharField(max_length=10, verbose_name='Sigla')),\n ('descricao', models.CharField(max_length=60, verbose_name='Descri\\xe7\\xe3o')),\n ('indicador', models.CharField(max_length=1, verbose_name='Indicador da Tramita\\xe7\\xe3o', choices=[(b'F', 'Fim'), (b'R', 'Retorno')])),\n ],\n options={\n 'verbose_name': 'Status de Tramita\\xe7\\xe3o',\n 'verbose_name_plural': 'Status de Tramita\\xe7\\xe3o',\n },\n ),\n migrations.CreateModel(\n name='TipoAutor',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descricao', models.CharField(max_length=50, verbose_name='Descri\\xe7\\xe3o')),\n ],\n options={\n 'verbose_name': 'Tipo de Autor',\n 'verbose_name_plural': 'Tipos de Autor',\n },\n ),\n migrations.CreateModel(\n name='TipoDocumento',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descricao', models.CharField(max_length=50, verbose_name='Tipo Documento')),\n ],\n options={\n 'verbose_name': 'Tipo de Documento',\n 'verbose_name_plural': 'Tipos de Documento',\n },\n ),\n migrations.CreateModel(\n name='TipoFimRelatoria',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descricao', models.CharField(max_length=50, verbose_name='Tipo Fim Relatoria')),\n ],\n options={\n 'verbose_name': 'Tipo Fim de Relatoria',\n 'verbose_name_plural': 'Tipos Fim de Relatoria',\n },\n ),\n migrations.CreateModel(\n name='TipoMateriaLegislativa',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('sigla', models.CharField(max_length=5, verbose_name='Sigla')),\n ('descricao', models.CharField(max_length=50, verbose_name='Descri\\xe7\\xe3o ')),\n ('num_automatica', models.BooleanField()),\n ('quorum_minimo_votacao', models.IntegerField()),\n ],\n options={\n 'verbose_name': 'Tipo de Mat\\xe9ria Legislativa',\n 'verbose_name_plural': 'Tipos de Mat\\xe9rias Legislativas',\n },\n ),\n migrations.CreateModel(\n name='TipoProposicao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descricao', models.CharField(max_length=50, verbose_name='Descri\\xe7\\xe3o')),\n ('materia_ou_documento', models.CharField(max_length=1, verbose_name='Gera', choices=[(b'M', 'Mat\\xe9ria'), (b'D', 'Documento')])),\n ('modelo', models.CharField(max_length=50, verbose_name='Modelo XML')),\n ('tipo_documento', models.ForeignKey(verbose_name='Tipo Documento', blank=True, to='materia.TipoDocumento', null=True)),\n ('tipo_materia', models.ForeignKey(verbose_name='Tipo Mat\\xe9ria', blank=True, to='materia.TipoMateriaLegislativa', null=True)),\n ],\n options={\n 'verbose_name': 'Tipo de Proposi\\xe7\\xe3o',\n 'verbose_name_plural': 'Tipos de Proposi\\xe7\\xf5es',\n },\n ),\n migrations.CreateModel(\n name='Tramitacao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('data_tramitacao', models.DateField(null=True, verbose_name='Data Tramita\\xe7\\xe3o', blank=True)),\n ('data_encaminhamento', models.DateField(null=True, verbose_name='Data Encaminhamento', blank=True)),\n ('ultima', models.BooleanField()),\n ('urgente', models.BooleanField(verbose_name='Urgente ?')),\n ('turno', models.CharField(blank=True, max_length=1, null=True, verbose_name='Turno', choices=[(b'P', 'Primeiro'), (b'S', 'Segundo'), (b'\\xc3\\x9a', '\\xdanico'), (b'L', 'Suplementar'), (b'F', 'Final'), (b'A', 'Vota\\xe7\\xe3o \\xfanica em Regime de Urg\\xeancia'), (b'B', '1\\xaa Vota\\xe7\\xe3o'), (b'C', '2\\xaa e 3\\xaa Vota\\xe7\\xe3o')])),\n ('texto', models.TextField(null=True, verbose_name='Texto da A\\xe7\\xe3o', blank=True)),\n ('data_fim_prazo', models.DateField(null=True, verbose_name='Data Fim Prazo', blank=True)),\n ('materia', models.ForeignKey(to='materia.MateriaLegislativa')),\n ('status', models.ForeignKey(verbose_name='Status', blank=True, to='materia.StatusTramitacao', null=True)),\n ],\n options={\n 'verbose_name': 'Tramita\\xe7\\xe3o',\n 'verbose_name_plural': 'Tramita\\xe7\\xf5es',\n },\n ),\n migrations.CreateModel(\n name='UnidadeTramitacao',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('comissao', models.ForeignKey(verbose_name='Comiss\\xe3o', blank=True, to='comissoes.Comissao', null=True)),\n ('orgao', models.ForeignKey(verbose_name='\\xd3rg\\xe3o', blank=True, to='materia.Orgao', null=True)),\n ('parlamentar', models.ForeignKey(verbose_name='Parlamentar', blank=True, to='parlamentares.Parlamentar', null=True)),\n ],\n options={\n 'verbose_name': 'Unidade de Tramita\\xe7\\xe3o',\n 'verbose_name_plural': 'Unidades de Tramita\\xe7\\xe3o',\n },\n ),\n migrations.AddField(\n model_name='tramitacao',\n name='unidade_tramitacao_destino',\n field=models.ForeignKey(related_name='+', verbose_name='Unidade Destino', blank=True, to='materia.UnidadeTramitacao', null=True),\n ),\n migrations.AddField(\n model_name='tramitacao',\n name='unidade_tramitacao_local',\n field=models.ForeignKey(related_name='+', verbose_name='Unidade Local', blank=True, to='materia.UnidadeTramitacao', null=True),\n ),\n migrations.AddField(\n model_name='relatoria',\n name='tipo_fim_relatoria',\n field=models.ForeignKey(verbose_name='Motivo Fim Relatoria', blank=True, to='materia.TipoFimRelatoria', null=True),\n ),\n migrations.AddField(\n model_name='proposicao',\n name='tipo',\n field=models.ForeignKey(verbose_name='Tipo', to='materia.TipoProposicao'),\n ),\n migrations.AddField(\n model_name='parecer',\n name='relatoria',\n field=models.ForeignKey(to='materia.Relatoria'),\n ),\n migrations.AddField(\n model_name='numeracao',\n name='tipo_materia',\n field=models.ForeignKey(verbose_name='Tipo de Mat\\xe9ria', to='materia.TipoMateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='materialegislativa',\n name='local_origem_externa',\n field=models.ForeignKey(verbose_name='Local Origem', blank=True, to='materia.Origem', null=True),\n ),\n migrations.AddField(\n model_name='materialegislativa',\n name='regime_tramitacao',\n field=models.ForeignKey(verbose_name='Regime Tramita\\xe7\\xe3o', to='materia.RegimeTramitacao'),\n ),\n migrations.AddField(\n model_name='materialegislativa',\n name='tipo_id_basica',\n field=models.ForeignKey(verbose_name='Tipo', to='materia.TipoMateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='materialegislativa',\n name='tipo_origem_externa',\n field=models.ForeignKey(related_name='+', verbose_name='Tipo', blank=True, to='materia.TipoMateriaLegislativa', null=True),\n ),\n migrations.AddField(\n model_name='materiaassunto',\n name='materia',\n field=models.ForeignKey(to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='documentoacessorio',\n name='materia',\n field=models.ForeignKey(to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='documentoacessorio',\n name='tipo',\n field=models.ForeignKey(verbose_name='Tipo', to='materia.TipoDocumento'),\n ),\n migrations.AddField(\n model_name='despachoinicial',\n name='materia',\n field=models.ForeignKey(to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='autoria',\n name='materia',\n field=models.ForeignKey(to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='autor',\n name='tipo',\n field=models.ForeignKey(verbose_name='Tipo', to='materia.TipoAutor'),\n ),\n migrations.AddField(\n model_name='anexada',\n name='materia_anexada',\n field=models.ForeignKey(related_name='+', to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='anexada',\n name='materia_principal',\n field=models.ForeignKey(related_name='+', to='materia.MateriaLegislativa'),\n ),\n migrations.AddField(\n model_name='acompanhamentomateria',\n name='materia',\n field=models.ForeignKey(to='materia.MateriaLegislativa'),\n ),\n ]\n","sub_path":"materia/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":23540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"543750159","text":"\"\"\"\nCode to search for eclipsing binaries.\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom typing import Iterable\n\nfrom ramjet.data_interface.tess_data_interface import TessDataInterface\nfrom ramjet.photometric_database.tess_synthetic_injected_with_negative_injection_database import \\\n TessSyntheticInjectedWithNegativeInjectionDatabase\n\n\nclass EclipsingBinaryDatabase(TessSyntheticInjectedWithNegativeInjectionDatabase):\n \"\"\"\n A class to represent a database to train to find eclipsing binaries in 2 minute cadence TESS data.\n Uses known cases from Brian Powell's eclipsing binary catalog.\n \"\"\"\n\n def __init__(self, data_directory='data/eclipsing_binary_database'):\n super().__init__(data_directory=data_directory)\n self.catalog_csv_path = self.data_directory.joinpath('ebcat_partial_sectors.csv')\n self.allow_out_of_bounds_injection = True\n\n def get_all_synthetic_signal_paths(self) -> Iterable[Path]:\n \"\"\"\n Returns the list of all synthetic signals to use.\n\n :return: The list of synthetic signals.\n \"\"\"\n synthetic_signal_paths = self.synthetic_signal_directory.glob('**/*.fits')\n return synthetic_signal_paths\n\n def load_magnifications_and_times_from_synthetic_signal_path(self, synthetic_signal_path: str\n ) -> (np.ndarray, np.ndarray):\n \"\"\"\n Loads the synthetic signal from the path given.\n\n :param synthetic_signal_path: The path to the synthetic signal data file.\n :return: The magnifications and relative times of the synthetic signal.\n \"\"\"\n fluxes, times = self.tess_data_interface.load_fluxes_and_times_from_fits_file(synthetic_signal_path)\n synthetic_magnifications, synthetic_times = self.generate_synthetic_signal_from_real_data(fluxes, times)\n return synthetic_magnifications, synthetic_times\n\n def download_catalog_eclipsing_binaries(self):\n \"\"\"\n Downloads the eclipsing binaries listed in Brian Powell's catalog to the synthetic signals directory.\n \"\"\"\n catalog = pd.read_csv(self.catalog_csv_path)\n catalog = catalog[catalog['2min'] == 1]\n tess_data_interface = TessDataInterface()\n tess_observations = tess_data_interface.get_all_tess_time_series_observations(tic_id=catalog['ID'])\n single_sector_observations = tess_data_interface.filter_for_single_sector_observations(tess_observations)\n single_sector_observations = tess_data_interface.add_tic_id_column_to_single_sector_observations(\n single_sector_observations)\n single_sector_observations = tess_data_interface.add_sector_column_to_single_sector_observations(\n single_sector_observations)\n single_sector_data_products = tess_data_interface.get_product_list(single_sector_observations)\n data_products = single_sector_data_products[\n single_sector_data_products['productFilename'].str.endswith('lc.fits')\n ]\n download_manifest = self.tess_data_interface.download_products(\n data_products, data_directory=self.data_directory)\n print(f'Moving lightcurves to {self.synthetic_signal_directory}...')\n self.synthetic_signal_directory.mkdir(parents=True, exist_ok=True)\n for file_path_string in download_manifest['Local Path']:\n file_path = Path(file_path_string)\n file_path.rename(self.synthetic_signal_directory.joinpath(file_path.name))\n\n\nif __name__ == '__main__':\n database = EclipsingBinaryDatabase()\n database.download_catalog_eclipsing_binaries()\n","sub_path":"ramjet/photometric_database/eclipsing_binary_database.py","file_name":"eclipsing_binary_database.py","file_ext":"py","file_size_in_byte":3648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"207255616","text":"import json\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.core import serializers\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\n\nfrom Confercio.frame.common.RoleCheker import RoleChecker\nfrom Confercio.frame.database.services.MessageDBService import MessageDBService\nfrom Confercio.frame.database.services.OrganizerDBService import OrganizerDBService\nfrom Confercio.frame.database.services.ParticipantDBService import ParticipantDBService\n\n\n@login_required\ndef get_newest_messages(request):\n if request.method != 'POST':\n return HttpResponse(status=405)\n\n service = MessageDBService()\n from_user_id = request.user.id\n post = json.loads(request.body)\n to_user_id = post['to_user_id']\n conference_id = post['conference_id']\n\n result = service.getNewestMessages(from_user_id, to_user_id, conference_id)\n\n content = serializers.serialize('json', result)\n\n return HttpResponse(content, status=200)\n\n\n@login_required\ndef get_new_messages(request):\n if request.method != 'POST':\n return HttpResponse(status=405)\n\n service = MessageDBService()\n from_user_id = request.user.id\n post = json.loads(request.body)\n to_user_id = post['to_user_id']\n last_id = post['last_id']\n conference_id = post['conference_id']\n\n result = service.getNewMessages(from_user_id, to_user_id, last_id, conference_id)\n\n content = serializers.serialize('json', result)\n\n return HttpResponse(content, status=200)\n\n\n@login_required\ndef get_old_messages(request):\n if request.method != 'POST':\n return HttpResponse(status=405)\n\n service = MessageDBService()\n from_user_id = request.user.id\n post = json.loads(request.body)\n to_user_id = post['to_user_id']\n last_id = post['last_id']\n conference_id = post['conference_id']\n\n result = service.getOldMessages(from_user_id, to_user_id, last_id, conference_id)\n\n content = serializers.serialize('json', result)\n\n return HttpResponse(content, status=200)\n\n\n@login_required\ndef set_read(request):\n if request.method != 'POST':\n return HttpResponse(status=405)\n\n service = MessageDBService()\n\n data = json.loads(request.body)\n\n for id in data['messages']:\n service.setMessageRead(id)\n\n return HttpResponse(status=200)\n\n\n@login_required\ndef available_users(request, conference_id):\n if request.method != 'GET':\n return HttpResponse(status=405)\n\n if RoleChecker.isOrganizer(request.user.role):\n return available_participants_for_dialog(conference_id)\n\n else:\n return available_organizers_for_dialog(conference_id)\n\n\ndef available_participants_for_dialog(conference):\n service = ParticipantDBService()\n\n result = service.getAvailableParticipantsForDialog(conference)\n\n content = serializers.serialize('json', result)\n\n return HttpResponse(content, status=200)\n\n\ndef available_organizers_for_dialog(conference):\n service = OrganizerDBService()\n\n result = service.getAvailableOrganizersForDialog(conference)\n\n content = serializers.serialize('json', result)\n\n return HttpResponse(content, status=200)\n\n\n@login_required\ndef get_dialogs(request, conference_id):\n if request.user.isModerator():\n return HttpResponse('Forbidden', status=403)\n\n if request.user.isParticipant():\n conference_id = request.user.user_conference.id\n\n service = MessageDBService()\n dialogs = service.getDialogs(request.user.id, conference_id)\n\n return render(request, 'site/messaging/dialog_list.html', {'dialogs': dialogs}, 'text/html')\n\n","sub_path":"Confercio/frame/view/messaging/dialog_load.py","file_name":"dialog_load.py","file_ext":"py","file_size_in_byte":3582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"76708951","text":"#!/usr/bin/python\n\"\"\"\n EzDates Ver 1.1 2014-04-24\n\n EzDates provide a new data type for date & time handling:\n * __init__(self) - Initialize object with the current timestamp\n * __str__(self, ts) - Provide \"str(ts)\" and \"print ts\" functionality\n * __sub__(self, ts) - Return \"ts - self\" (time difference in seconds)\n * dateDiff(self, ts) - Return \"ts - self\" (time difference in seconds)\n * updateTimestamp(ts) - Update \"ts\" object with the current date and time\n * convertToSeconds(self) - Convert to seconds passed from 1970/01/01\n * convertToEzDates(self,ts) - Convert string timestamp to EzDates object\n\n Developer: ilan.shavit@gmail.com\n\"\"\"\nimport datetime\nimport time\n\nclass EzDates(object):\n \"\"\"\n Provide a new data type for date & time manipulation\n \"\"\"\n def __init__(self):\n ts1 = datetime.datetime.now()\n self.year = str(ts1.year)\n if ts1.month < 10:\n self.month = \"0\" + str(ts1.month)\n else:\n self.month = str(ts1.month)\n\n if ts1.day < 10:\n self.day = \"0\" + str(ts1.day)\n else:\n self.day = str(ts1.day)\n\n if ts1.hour < 10:\n self.hour = \"0\" + str(ts1.hour)\n else:\n self.hour = str(ts1.hour)\n\n if ts1.minute < 10:\n self.minute = \"0\" + str(ts1.minute)\n else:\n self.minute = str(ts1.minute)\n\n if ts1.second < 10:\n self.second = \"0\" + str(ts1.second)\n else:\n self.second = str(ts1.second)\n\n def __str__(self):\n # Provide functionality for str(EzDates_Object) & print EzDates_Object\n return self.year + '-' + self.month + '-' + self.day + ' ' +\\\n self.hour + ':' + self.minute + ':' + self.second\n\n def __sub__(self, ts1):\n # Return the difference between two dates (in seconds)\n return self.convert_to_seconds() - ts1.convert_to_seconds()\n\n def date_diff(self, ts1):\n \"\"\"\n Return the difference between two dates (in seconds)\n\n \"\"\"\n return self.convert_to_seconds() - ts1.convert_to_seconds()\n\n def update_timestamp(self):\n \"\"\"\n Update EzDates object with current timestamp\n\n \"\"\"\n ts1 = datetime.datetime.now()\n self.year = str(ts1.year)\n if ts1.month < 10:\n self.month = \"0\" + str(ts1.month)\n else:\n self.month = str(ts1.month)\n\n if ts1.day < 10:\n self.day = \"0\" + str(ts1.day)\n else:\n self.day = str(ts1.day)\n\n if ts1.hour < 10:\n self.hour = \"0\" + str(ts1.hour)\n else:\n self.hour = str(ts1.hour)\n\n if ts1.minute < 10:\n self.minute = \"0\" + str(ts1.minute)\n else:\n self.minute = str(ts1.minute)\n\n if ts1.second < 10:\n self.second = \"0\" + str(ts1.second)\n else:\n self.second = str(ts1.second)\n\n\n def convert_to_seconds(self):\n \"\"\"\n Return seconds passed from 1970-01-01\n\n \"\"\"\n ts1 = (int(self.year), int(self.month), int(self.day),\n int(self.hour), int(self.minute), int(self.second), 0, 0, 0)\n return time.mktime(ts1)\n\n\n def convert_to_ezdates(self, ts1):\n \"\"\"\n Convert string timestamp to EzDates object\n\n \"\"\"\n self.year = ts1[0:4]\n self.month = ts1[5:7]\n self.day = ts1[8:10]\n self.hour = ts1[11:13]\n self.minute = ts1[14:16]\n self.second = ts1[17:19]\n","sub_path":"Python/Modules/EzDates/EzDates.py","file_name":"EzDates.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"357694925","text":"#---------------------------\n# Creator: Brittany Manuel\n# Created: August 29, 2016\n# Revisions: September 7, 2016\n# File: HW1.py\n#---------------------------\n\n\n\n#---------------------------\n# INTRODUCTION\n#\n# Hello! This is the first assignment so I will explain how I stylize things. \n# I do my blocks of comments like this with a header and footer. There are 3 \n# lines between each function to make for cleaner reading. I try to have \n# compact code with out being illegible. All of my code comments will be \n# contained in the small block at the top of the function because I find it \n# messy to have comments next to the code and takes away from the readability.\n# I also have a hard time with single character variables. Anything that was\n# \"s\" I changed to \"string\" or something like that so that I could read it easier.\n# Please let me know if I need to change of this for future assignments. \n# Thanks! \n#\n# All of my tests passed using Mac OSX 10.11.6 using Python 3.5.2 in iTerm2. \n# My final output is in a block of comments above the main function. \n#---------------------------\n\n\n\n#---------------------------\n# MAKETTABLE\n#---------------------------\ndef makettable(string1, string2):\n\n # return dict(zip(list(string1.lower()+string1.upper()), list(string2.lower()+string2.upper())))\n\n return dict(zip(list(string1), list(string2)))\n\n\n\n#---------------------------\n# TRANSLATE\n# For translating, I decided I would create a copy of the original string and work\n# from that so that I didn't need to worry about overwriting myself as I worked. \n# I iterated over each character in the original string. For each character I looked\n# into my dictionary, if it was there I would replace that same character in the \n# copy with the dictionary value. Returned the translated string. \n#---------------------------\ndef trans(ttable, translate_string):\n\n translated_string = translate_string\n\n for char in translate_string:\n\n if ttable.get(char):\n\n translated_string = translated_string.replace(char, ttable.get(char))\n\n return translated_string\n\n\n\n#---------------------------\n# HISTOGRAM\n# For the histogram, I decided that it might be useful to create it using a dictionary\n# For each character in the string I was given I would try to access that character in\n# the dictionary I created, if it was there I would add 1 to the value (count), if not\n# I would create it and set the starting value (count) to 1. Once that was completed I\n# created a list of tuples from the dictionary. I then sorted it by alphabet, then by\n# how many times the character occurred and returned that list. \n#---------------------------\ndef histo(string):\n\n histo_table = dict()\n\n for char in string:\n\n if histo_table.get(char) : histo_table[char] += 1\n else : histo_table[char] = 1\n\n histo_list = histo_table.items() \n histo_list = sorted(histo_list)\n histo_list = sorted(histo_list, key = lambda x: x[1], reverse = True) \n\n return histo_list\n\n\n\n#---------------------------\n# DIGRAPHS\n# For my digraph I took the same approach as I did in the histogram by using a \n# dictionary I also decided to take the string and turn it into a list for each \n# character. I did this because I would also need to access the very next \n# character and a list is very good for that. Once I had my list, I iterated \n# through it from the first element to the second to last because I was using \n# [index + 1] to get the two characters together. Once I created the entry of \n# '/CC/' I would then look for that in the dictionary. If it was there I would \n# add 1 to the count, if not I would create the entry and start the count out \n# with 1. Just like with histogram I sorted it, then returned it. \n#---------------------------\ndef digraphs(string):\n\n digraph_table = dict()\n digraph_string = []\n\n for char in string : digraph_string.append(char)\n\n for index in range(len(digraph_string) - 1):\n\n entry = \"/\" + digraph_string[index] + digraph_string[index + 1] + \"/\"\n\n if digraph_table.get(entry) : digraph_table[entry] += 1\n else : digraph_table[entry] = 1\n\n digraph_list = digraph_table.items()\n digraph_list = sorted(digraph_list)\n digraph_list = sorted(digraph_list, key = lambda x: x[1], reverse = True) \n\n\n return digraph_list\n\n\n\n#---------------------------\n# TESTING\n# My three testing functions for the three above functions.\n#---------------------------\n\n\n\n#---------------------------\n# TEST TRANSLATE\n# For the translate test I took the tests given in the PDF then added another one.\n# Mine included different punctuation along with capitals and lower case. I \n# wanted to create more tests but I wasn't completely sure what sort of tests were\n# being looked for. I looked into using unittest but I felt that might be overkill \n# for this class. \n#---------------------------\ndef testtrans():\n\n table1 = makettable(\"abc\", \"xyz\")\n table2 = makettable(\"xyz\", \"abc\")\n table3 = makettable(\"bafghjknpqr\", \"ilovetsmycd\")\n tests = \"Now I know my abc's\"\n answer = \"Now I know my xyz's\"\n test2 = \"B afgh jf jhkj np qfrh!\"\n answer2 = \"I love to test my code!\" \n\n if trans(table1, tests) != answer : return False\n if trans(table3, test2) != answer2 : return False\n if trans(table1, \"\") != \"\" : return False\n if trans(makettable(\"\", \"\"), \"abc\") != \"abc\" : return False\n if trans(table2, trans(table1, tests)) != \"Now I know mb abc's\": return False\n else : return True\n\n\n\n#---------------------------\n# TEST HISTOGRAM\n# My first test is the simple one given in the PDF. The second one, I wanted to see \n# how it would handle punctuation. One thing I have not tested for is special \n# characters or foreign languages. \n#---------------------------\ndef testhisto():\n\n test = \"implemented\" \n answer = [('e', 3), ('m', 2), ('d', 1), ('i', 1), ('l', 1), ('n', 1), ('p', 1), ('t', 1)]\n test2 = \"aaaaaabbbbbbbccccccccxy!!.//$%\"\n answer2 = [('c', 8), ('b', 7), ('a', 6), ('!', 2), ('/', 2), ('$', 1), ('%', 1), ('.', 1), ('x', 1), ('y', 1)]\n\n if histo(test) != answer : return False\n if histo(test2) != answer2 : return False\n else : return True\n\n\n\n#---------------------------\n# TEST DISGRAPH\n# This test only has one set of items but I think for this assignment it covers\n# some of what is needed. It follows the style of the others with if X != Y : Z.\n#---------------------------\ndef testdisgraph():\n\n test = \"Text test <.> !! hello, text test !! hello HELLO\"\n answer = [('/ !/', 2), ('/ /', 1), ('/<./', 1), ('/> /', 1), ('/EL/', 1), ('/HE/', 1), ('/LL/', 1), ('/LO/', 1), ('/Te/', 1), ('/el/', 2), ('/es/', 2), ('/ex/', 2), ('/he/', 2), ('/ll/', 2), ('/lo/', 2), ('/o /', 1), ('/o,/', 1), ('/st/', 2), ('/t /', 4), ('/te/', 3), ('/xt/', 2)]\n\n if digraphs(test) != answer : return False\n else : return True\n\n\n\n#---------------------------\n# MAIN\n# I redid how the main function was shown in the PDF. I made a dictionary with the\n# function name as the key and the value was the result of that test. For each key\n# in the dictionary I then did a print statement based on the value. This eliminated \n# the duplicate code that I felt could be avoided (and suggested to do so). With \n# this way of doing it, you could add many more tests with very minimal work. Simply\n# adding the function name and a call to it and the print function remains unchanged.\n# \n# My output: \n# Brittanys-Macbook-Air:01_Python brittanymanuel$ python HW1.py\n# Histogram has passed the test! Good job!\n# \n# Disgraph has passed the test! Good job!\n# \n# Translate has passed the test! Good job!\n#---------------------------\nif __name__ == '__main__':\n \n \n testing_results = {\"Translate\" : testtrans(), \"Histogram\" : testhisto(), \"Disgraph\" : testdisgraph()} \n\n for key in testing_results : \n\n print (\"%s %s passed the test! %s\" % (key, (\"has\" if testing_results[key] else \"has not\"), (\"Good job! \\n\" if testing_results[key] else \"Try again! \\n\") ))\n\n\n\n#---------------------------\n# CRYPTOGRAPH\n# For future students you may want to mention these are two DIFFERENT ones. It took me\n# much longer to figure out the first one because I was trying to solve the whole thing.\n# Once I realized that they were two different cryptographs it went much faster. I don't\n# know if having not said that was part of the extra credit or not! \n#---------------------------\nthisIsTheCryptogramAnswer = \"\"\"\nthe population of burmese pythons presently established in the park is the result of accidental and/or intentional releases by pet owners. these introductions can have devastating consequences to our ecosystem. burmese pythons have been found to feed on a wide variety of mammals and birds in the everglades-even the occasional alligator! by preying on native wildlife, and competing with other native predators, pythons are seriously impacting the natural order of south florida's ecological communities. the continued proliferation of burmese pythons-and the continued introduction of new foreign species-can further threaten many of the endangered plants and animals we're working diligently to protect. (www.nps.gov/ever/naturescience/burmesepythonsintro.htm)\n\nbroadcast by the bbc between 1969 and 1974, flying circus was conceived, written, and performed by its members graham chapman, john cleese, terry gilliam, eric idle, terry jones, and michael palin. loosely structured as a sketch show, but with an innovative stream-of-consciousness approach (aided by gilliam's animation), it pushed the boundaries of what was acceptable in style and content. a self-contained comedy team responsible for both writing and performing their work, the pythons had creative control which allowed them to experiment with form and content, discarding rules of television comedy. their influence on british comedy has been apparent for years, while in north america, it has coloured the work of cult performers from the early editions of saturday night live through to more recent absurdist trends in television comedy. \"pythonesque\" has entered the english lexicon as a result.\n\"\"\"","sub_path":"01_Python/HW1.py","file_name":"HW1.py","file_ext":"py","file_size_in_byte":10200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"327300878","text":"import sys\nimport time\n\n# import datetime\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials as SAC\n\nGDriveJSON = 'PythonUpload.json'\nGSpreadSheet = 'TestGithubCommit'\nWaitSecond = 5\nprint('將資料記錄在試算表', GSpreadSheet, '每', WaitSecond, '秒')\nprint('按下 Ctrl-C中斷執行')\ncount = 1\nwhile True:\n try:\n scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']\n key = SAC.from_json_keyfile_name(GDriveJSON, scope)\n gc = gspread.authorize(key)\n worksheet = gc.open(GSpreadSheet).sheet1\n except Exception as ex:\n print('無法連線Google試算表', ex)\n sys.exit(1)\n # worksheet.append_row((datetime.datetime.now(), count))\n worksheet.append_row(['this is first col', 'this is second col'])\n count = count + 1\n print('新增一列資料到試算表', GSpreadSheet)\n time.sleep(WaitSecond)\n\n# gitLog = \"git log --pretty=format:'%h% -%d% %s (%ci) <%an>' --abbrev-commit|grep tag\"\n","sub_path":"ThirdPartAPI/Google-sheet/write_data_to_google_sheet/simple_code.py","file_name":"simple_code.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"64389429","text":"'''\nchallenge URL : https://www.codewars.com/kata/5c8bfa44b9d1192e1ebd3d15 \n'''\n\ndef warn_the_sheep(queue):\n l = len(queue)\n if queue[l-1] == \"wolf\":\n return \"Pls go away and stop eating my sheep\"\n else : \n for i in range(len(queue)):\n if queue[i] == \"wolf\":\n a = len(queue) - (i+1)\n return 'Oi! Sheep number ' + str(a) + '! You are about to be eaten by a wolf!'","sub_path":"8-KYU/A wolf in sheep's clothing/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"647456753","text":"from card import Card\nfrom random import shuffle\n\nclass Deck:\n\tdef __init__(self,shuf):\n\t\tself.cards = []\n\t\tself.shuf = shuf\n\t\tfor suit in range(0,4):\n\t\t\tfor rank in range(1,14):\n\t\t\t\tself.cards.append(Card(rank,suit))\n\t\tif self.shuf == True:\n\t\t\tshuffle(self.cards)\n\n\tdef draw(self):\n\t\tpop(self.cards)\n\n\tdef __str__(self):\n\t\treturn self.cards\n\ndeck1 = Deck(True)\nfor x in deck1.cards:\n\tprint(x)\ndeck1.draw()","sub_path":"deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"133214090","text":"\"\"\"\n根据每日 气温 列表，请重新生成一个列表，对应位置的输出是需要再等待多久温度才会升高超过该日的天数。\n如果之后都不会升高，请在该位置用 0 来代替。\n\n例如，给定一个列表 temperatures = [73, 74, 75, 71, 69, 72, 76, 73]，\n 你的输出应该是 [1, 1, 4, 2, 1, 1, 0, 0]。\n (隔几天后能发现比自己大的)\n\"\"\"\n\nclass Solution(object):\n def dailyTemperatures(self, T):\n \"\"\"\n :type T: List[int]\n :rtype: List[int]\n \"\"\"\n length = len(T)\n # 栈储存的只是元素的下标，当栈顶元素碰到比自己大的元素时，让两者的下标相减少\n stack = []\n\n # res的下标对应这的是元素的下标，上面的值代表着是 下一个比自己大的元素 在离自己有多远\n res = [0] * length\n\n for i in range(length):\n # 当栈不为空， 且当前温度大于栈顶温度时\n # 说明要把栈顶元素取出，然后 用当前 元素的下标 - 栈顶元素的下标，得到我们想要的答案\n while len(stack) > 0 and T[i] > T[stack[-1]]:\n temp = stack.pop(-1)\n res[temp] = i - temp\n\n stack.append(i)\n\n return res\n\n\"\"\"\n时间空间复杂度都是 O(n)\nhttps://leetcode-cn.com/problems/daily-temperatures/solution/leetcode-tu-jie-739mei-ri-wen-du-by-misterbooo/\n\n\n\"\"\"","sub_path":"leetcode/stack & queue/单调栈/739. 每日温度(递减栈).py","file_name":"739. 每日温度(递减栈).py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"430230596","text":"import numpy as np\nimport pandas as pd\nimport itertools as it\nfrom collections import defaultdict\nimport warnings\n\nimport lab_repo.analysis.behavior_analysis as ba\nfrom lab_repo.classes.classes import ExperimentGroup\nfrom lab_repo.classes import exceptions as exc\n\ndef lick_to_reward_distance(expt_grp, rewardPositions=None):\n \"\"\"Calculate the average lick to reward distance.\n\n Parameters\n ----------\n rewardPositions : {str, None, np.ndarray}\n If a string, assumed to be a condition label, and will use the\n reward positions used for each mouse during the condition.\n If 'None', uses the actual reward positions during the experiment.\n Otherwise pass in normalized reward positions.\n\n Returns\n -------\n pd.DataFrame\n\n \"\"\"\n result = []\n\n if rewardPositions is None:\n rewards_by_expt = {\n expt: expt.rewardPositions(units='normalized')\n for expt in expt_grp}\n else:\n rewards_by_expt = defaultdict(lambda: np.array(rewardPositions))\n\n for expt in expt_grp:\n\n rewards = rewards_by_expt[expt]\n\n for trial in expt.findall('trial'):\n bd = trial.behaviorData(imageSync=False)\n position = ba.absolutePosition(\n trial, imageSync=False, sampling_interval='actual')\n\n if np.any(rewards >= 1.0):\n trial_rewards = rewards / bd['trackLength']\n else:\n trial_rewards = rewards\n\n licking = bd['licking'][:, 0]\n licking = licking[np.isfinite(licking)]\n licking = licking / bd['samplingInterval']\n licking = licking.astype('int')\n\n licking_positions = position[licking] % 1\n\n # meshgrid sets up the subtraction below\n # basically tile expands the arrays\n rewards_mesh, licking_mesh = np.meshgrid(\n trial_rewards, licking_positions)\n\n reward_distance = licking_mesh - rewards_mesh\n # All distances should be on [-0.5, 0.5)\n reward_distance[reward_distance >= 0.5] -= 1.0\n reward_distance[reward_distance < -0.5] += 1.0\n\n reward_distance = np.amin(np.abs(reward_distance), axis=1)\n\n assert len(licking_positions) == len(reward_distance)\n for lick, position in it.izip(\n reward_distance, licking_positions):\n result.append(\n {'expt': expt.trial_id, 'position': position, 'value': lick, 'session': expt.session})\n return pd.DataFrame(result, columns=['expt', 'position', 'value', 'session'])\n","sub_path":"lab_repo/analysis/reward_analysis.py","file_name":"reward_analysis.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"242682689","text":"from ..config import config\nfrom ..db import db\nfrom .. import app, users\n\nfrom . import oauth\n\nfrom flask import redirect, url_for, request, session, flash, render_template\nfrom pprint import pprint\nfrom inspect import getmembers\nfrom pymongo.errors import DuplicateKeyError\n\nmymlh = oauth.remote_app(\n \"mymlh\",\n base_url = \"https://my.mlh.io/api/v1/\",\n authorize_url = \"https://my.mlh.io/oauth/authorize\",\n consumer_key = config[\"auth\"][\"mymlh\"][\"app_id\"],\n consumer_secret = config[\"auth\"][\"mymlh\"][\"secret\"],\n access_token_url = \"https://my.mlh.io/oauth/token\",\n request_token_url = None,\n request_token_params = {\"scope\": \"profile contact events\"}\n)\n\nmymlh_users = db[\"mymlh_users\"]\nmymlh_users.create_index(\"id\", unique=True)\n\n@mymlh.tokengetter\ndef mymlh_token_getter(token=None):\n return session.get(\"mymlh_token\")\n\n\n@app.route(\"/oauth/mymlh_login\")\ndef mymlh_login():\n return mymlh.authorize(callback=\"https://chat.mita.me/oauth/mymlh_callback\")\n\n\n@app.route('/oauth/mymlh_callback')\ndef mymlh_callback():\n next_url = request.args.get(\"next\") or url_for(\"index\")\n resp = mymlh.authorized_response()\n\n if resp is None:\n flash(u'You denied the request to sign in.')\n return redirect(\"/\")\n\n session['mymlh_token'] = [resp[\"access_token\"]]\n\n response = mymlh.get(\"user\")\n user_data = response.data[\"data\"]\n\n session['mymlh_user'] = user_data\n\n mymlh_user = {\n \"id\": user_data[\"id\"],\n \"token\": resp[\"access_token\"],\n }\n try:\n mymlh_users.insert(mymlh_user)\n except DuplicateKeyError:\n # log user in if they already have an account\n mymlh_users.update_one(\n {\"id\": user_data[\"id\"]},\n {\"$set\": {\"token\": resp[\"access_token\"]}}\n )\n\n res = users.users.find_one({\n \"provider.mymlh\": user_data[\"id\"]\n })\n\n print(res)\n\n if res:\n session[\"username\"] = res[\"username\"]\n return redirect(\"/c/main\")\n elif session[\"username\"]:\n # Add this account to the user if they are already logged in.\n user = users.get_user()\n\n users.users.find_one_and_update(\n {\"_id\": user[\"_id\"]},\n {\"$set\": {\"providers.mymlh\": mymlh_user[\"id\"]}}\n )\n return redirect(next_url)\n\n return redirect(url_for(\"mymlh_complete\", next=next_url))\n\n@app.route(\"/oauth/mymlh_complete\", methods=[\"GET\", \"POST\"])\ndef mymlh_complete():\n try:\n mymlh_user_id = session.get(\"mymlh_user\")[\"id\"]\n except KeyError:\n redirect(url_for(\"index\"))\n\n # if user already exists linked to this twitter account\n res = users.users.find_one({\n \"providers.mymlh\": mymlh_user_id\n })\n if res:\n session[\"username\"] = res[\"username\"]\n return redirect(url_for(\"index\"))\n\n if request.method == \"GET\":\n user_data = session.get(\"mymlh_user\")\n username = user_data[\"first_name\"] + user_data[\"last_name\"]\n return render_template(\"setUsername.html\", username=username)\n elif request.method == \"POST\":\n username = request.form[\"username\"]\n\n username_status = users.check_username(username)\n if not username_status[\"ok\"]:\n return render_template(\"setUsername.html\", username=username, error=username_status[\"message\"])\n\n res = users.create(\n username=username,\n provider=\"mymlh\",\n provider_id=mymlh_user_id,\n image_url=None\n )\n if not res[\"ok\"]:\n return render_template(\"setUsername.html\", username=username, error=res[\"message\"])\n\n session[\"username\"] = username\n return redirect(url_for(\"index\"))\n","sub_path":"chittr/auth/mymlh.py","file_name":"mymlh.py","file_ext":"py","file_size_in_byte":3718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"461763766","text":"from hashlib import sha256\nimport time\nMAX_NONCE = 100000000000 # Max Computation Power\n\n\ndef SHA256(text):\n # Converts The Text To SHA256 Type\n return sha256(text.encode(\"ascii\")).hexdigest()\n\n\ndef mine(block_number, transactions, previous_hash, prefix_zeroes):\n prefix_str = '0'*prefix_zeroes\n for nonce in range(MAX_NONCE):\n text = str(block_number) + transactions + previous_hash + str(nonce)\n new_hash = SHA256(text)\n if new_hash.startswith(prefix_str):\n print(\n f\"Yah! Successfully Mined Bitcoins With Nonce Value: {nonce}\")\n return new_hash\n\n raise BaseException(\n f\"Couldn;t Find Correct Has After Trying {MAX_NONCE} times\")\n\n\nif __name__ == '__main__':\n transactions = '''\n Sam->Shubham->10,\n Jaya>Vikrum->80\n '''\n difficulty = 10 # No Of Zeroes Before Hash Value\n start = time.time()\n print(\"Mining Started...\")\n new_hash = mine(\n 5, transactions, '000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f', difficulty)\n total_time = str((time.time() - start))\n print(f\"Mining Ended. Mining Took: {total_time} seconds\")\n print(new_hash)\n","sub_path":"Mining.py","file_name":"Mining.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"134605811","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: Sergio Rozada Doval\r\n\r\n@description: Test the performance of different models and algorithms\r\n\r\n\"\"\" \r\nimport os,sys,inspect\r\ncurrent_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\r\nparent_dir = os.path.dirname(current_dir)\r\nsys.path.insert(0, parent_dir)\r\n\r\nimport tensorflow as tf\r\nimport matplotlib.pyplot as plt\r\nimport architectures\r\nimport numpy as np\r\nimport dynamics as dn\r\nimport rl\r\n\r\n# Model and reward to test algorithms\r\nmodel_path = \"../models/maddpg_secondary\"\r\nreward_path = \"cummulative_reward_maddpg_secondary.pickle\"\r\n\r\nreward = rl.readData(reward_path)\r\n\r\n# Instances of the environment\r\ngenerator_1 = dn.Node_Secondary(1.5)\r\ngenerator_2 = dn.Node_Secondary(1.5)\r\n \r\narea = dn.Area_Secondary(frequencySetPoint=50,M=0.1,D=0.0160,Tg=30,Rd=0.1)\r\narea.setLoad(3.15)\r\narea.setGeneration(3.0)\r\narea.calculateDeltaF()\r\n\r\n# Define list of powers and frequencies\r\nz_1 = []\r\nz_2 = []\r\npower = []\r\nfrequencies = []\r\ndw = []\r\n\r\n# Let's tensorflow this\r\ntf.reset_default_graph()\r\ngraph = tf.train.import_meta_graph(model_path+\".meta\")\r\n\r\nsteps = 100\r\nh_size = 100\r\n\r\nwith tf.Session() as session: \r\n # Restore values of the graph\r\n graph.restore(session, model_path)\r\n \r\n # Create the actors\r\n lstm_actor_1 = tf.contrib.rnn.BasicLSTMCell(num_units=h_size,state_is_tuple=True)\r\n actor_1 = architectures.actor_maddpg(h_size,lstm_actor_1,'actor_1_test',len(tf.trainable_variables()))\r\n actor_1.createOpHolder(tf.trainable_variables(),1)\r\n \r\n n_params = len(actor_1.network_params)\r\n \r\n lstm_actor_2 = tf.contrib.rnn.BasicLSTMCell(num_units=h_size,state_is_tuple=True)\r\n actor_2 = architectures.actor_maddpg(h_size,lstm_actor_2,'actor_2_test',len(tf.trainable_variables()))\r\n actor_2.createOpHolder(tf.trainable_variables()[n_params*4:],1)\r\n \r\n # Initialize variables and copy params\r\n init_1 = tf.variables_initializer(actor_1.network_params)\r\n init_2 = tf.variables_initializer(actor_2.network_params)\r\n \r\n session.run(init_1)\r\n session.run(init_2)\r\n \r\n session.run(actor_1.update_network_params)\r\n session.run(actor_2.update_network_params)\r\n\r\n # State of the network\r\n state_1 = (np.zeros([1,h_size]),np.zeros([1,h_size]))\r\n state_2 = (np.zeros([1,h_size]),np.zeros([1,h_size]))\r\n \r\n for i in range(steps):\r\n \r\n # Store values \r\n z_1.append(generator_1.getZ())\r\n z_2.append(generator_2.getZ())\r\n power.append(area.getGeneration())\r\n frequencies.append(area.getFrequency())\r\n dw.append(area.getDeltaF())\r\n \r\n # Get state and take the best action\r\n current_f = area.getDeltaF()\r\n \r\n a_1, new_state_1 = session.run([actor_1.a,actor_1.rnn_state], \r\n feed_dict={actor_1.inputs: np.array(current_f).reshape(1,1),\r\n actor_1.state_in: state_1, actor_1.batch_size:1,actor_1.trainLength:1})\r\n a_2, new_state_2 = session.run([actor_2.a,actor_2.rnn_state], \r\n feed_dict={actor_2.inputs: np.array(current_f).reshape(1,1),\r\n actor_2.state_in: state_2, actor_2.batch_size:1,actor_2.trainLength:1})\r\n a_1 = a_1[0,0]\r\n a_2 = a_2[0,0]\r\n \r\n # Take the action, modify environment and get the reward\r\n generator_1.modifyZ(a_1)\r\n generator_2.modifyZ(a_2)\r\n Z = rl.getSumZ([generator_1,generator_2])\r\n area.calculatePg(Z)\r\n area.calculateDeltaF()\r\n \r\n # Set state again\r\n state_1 = new_state_1\r\n state_2 = new_state_2\r\n \r\nplt.figure(1)\r\nplt.scatter(np.arange(len(reward)),reward)\r\nplt.xlabel('Episodes')\r\nplt.ylabel('Cumm. reward per episode')\r\n\r\nplt.figure(2)\r\nplt.plot(power)\r\nplt.plot([3.15]*100)\r\nplt.xlabel('Steps')\r\nplt.ylabel('Power (MW)')\r\nplt.legend(['Total power','Power setpoint'])\r\n\r\nplt.figure(3)\r\nplt.plot(z_1)\r\nplt.plot(z_2)\r\nplt.plot(np.sum([np.array(z_1),np.array(z_2)],axis=0))\r\nplt.xlabel('Steps')\r\nplt.ylabel('Control action (Z)')\r\nplt.legend(['Gen 1 secondary action','Gen 2 secondary action','Total secondary action'])\r\n\r\nplt.figure(5)\r\nplt.plot(frequencies)\r\nplt.plot([50]*100)\r\nplt.xlabel('Steps')\r\nplt.ylabel('Frequency (Hz)')\r\nplt.legend(['System frequency','Frequency setpoint'])\r\n\r\nplt.figure(6)\r\nplt.plot(dw)\r\nplt.xlabel('Steps')\r\nplt.ylabel('dw')","sub_path":"tests/test_maddpg_secondary.py","file_name":"test_maddpg_secondary.py","file_ext":"py","file_size_in_byte":4424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"110986446","text":"from common.model import Model\nfrom common.utils import delete_file\n\nclass File(Model):\n table = 'file'\n\n def __init__(self, **kwargs):\n super(File, self).__init__(**kwargs)\n\n def delete(self):\n filename = getattr(self, 'file_id', None)\n if filename:\n delete_file(filename)\n delete_file('%s.json' % filename)\n super(File, self).delete()\n","sub_path":"models/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"317035902","text":"import typing\nimport warnings\nimport numpy as np\nfrom sklearn.model_selection import StratifiedShuffleSplit\nfrom automlToolkit.components.evaluators.cls_evaluator import ClassificationEvaluator\nfrom automlToolkit.components.evaluators.reg_evaluator import RegressionEvaluator\nfrom automlToolkit.utils.logging_utils import get_logger\nfrom ConfigSpace.hyperparameters import UnParametrizedHyperparameter\nfrom automlToolkit.components.feature_engineering.transformation_graph import DataNode\nfrom automlToolkit.components.fe_optimizers import build_fe_optimizer\nfrom automlToolkit.components.hpo_optimizer import build_hpo_optimizer\nfrom automlToolkit.components.evaluators.base_evaluator import fetch_predict_estimator\nfrom automlToolkit.components.utils.constants import *\nfrom automlToolkit.utils.decorators import time_limit\nfrom automlToolkit.utils.functions import get_increasing_sequence\n\n\nclass SecondLayerBandit(object):\n def __init__(self, task_type, estimator_id: str, data: DataNode, metric,\n share_fe=False, output_dir='logs',\n per_run_time_limit=120,\n per_run_mem_limit=5120,\n dataset_id='default',\n eval_type='holdout',\n mth='rb', sw_size=3,\n n_jobs=1, seed=1,\n enable_intersection=True,\n number_of_unit_resource=2):\n self.task_type = task_type\n self.metric = metric\n self.number_of_unit_resource = number_of_unit_resource\n # One unit of resource, that's, the number of trials per iteration.\n self.one_unit_of_resource = 5\n self.per_run_time_limit = per_run_time_limit\n self.per_run_mem_limit = per_run_mem_limit\n self.estimator_id = estimator_id\n self.evaluation_type = eval_type\n self.original_data = data.copy_()\n self.share_fe = share_fe\n self.output_dir = output_dir\n self.mth = mth\n self.seed = seed\n self.n_jobs = n_jobs\n self.sliding_window_size = sw_size\n self.logger = get_logger('%s:%s-%d=>%s' % (\n __class__.__name__, dataset_id, seed, estimator_id))\n np.random.seed(self.seed)\n\n # Bandit settings.\n self.arms = ['fe', 'hpo']\n self.rewards = dict()\n self.optimizer = dict()\n self.evaluation_cost = dict()\n self.update_flag = dict()\n # Global incumbent.\n self.inc = dict()\n self.local_inc = dict()\n self.local_hist = {'fe': [], 'hpo': []}\n for arm in self.arms:\n self.rewards[arm] = list()\n self.update_flag[arm] = False\n self.evaluation_cost[arm] = list()\n self.pull_cnt = 0\n self.action_sequence = list()\n self.final_rewards = list()\n self.incumbent_perf = float(\"-INF\")\n self.early_stopped_flag = False\n self.enable_intersection = enable_intersection\n\n # Fetch hyperparameter space.\n if self.task_type in CLS_TASKS:\n from automlToolkit.components.models.classification import _classifiers, _addons\n if estimator_id in _classifiers:\n clf_class = _classifiers[estimator_id]\n elif estimator_id in _addons.components:\n clf_class = _addons.components[estimator_id]\n else:\n raise ValueError(\"Algorithm %s not supported!\" % estimator_id)\n cs = clf_class.get_hyperparameter_search_space()\n model = UnParametrizedHyperparameter(\"estimator\", estimator_id)\n cs.add_hyperparameter(model)\n elif self.task_type in REG_TASKS:\n from automlToolkit.components.models.regression import _regressors, _addons\n if estimator_id in _regressors:\n reg_class = _regressors[estimator_id]\n elif estimator_id in _addons.components:\n reg_class = _addons.components[estimator_id]\n else:\n raise ValueError(\"Algorithm %s not supported!\" % estimator_id)\n cs = reg_class.get_hyperparameter_search_space()\n model = UnParametrizedHyperparameter(\"estimator\", estimator_id)\n cs.add_hyperparameter(model)\n else:\n raise ValueError(\"Unknown task type %s!\" % self.task_type)\n\n self.config_space = cs\n self.default_config = cs.get_default_configuration()\n self.config_space.seed(self.seed)\n\n # Build the Feature Engineering component.\n if self.task_type in CLS_TASKS:\n fe_evaluator = ClassificationEvaluator(self.default_config, scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)\n hpo_evaluator = ClassificationEvaluator(self.default_config, scorer=self.metric,\n data_node=self.original_data, name='hpo',\n resampling_strategy=self.evaluation_type,\n seed=self.seed)\n elif self.task_type in REG_TASKS:\n fe_evaluator = RegressionEvaluator(self.default_config, scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)\n hpo_evaluator = RegressionEvaluator(self.default_config, scorer=self.metric,\n data_node=self.original_data, name='hpo',\n resampling_strategy=self.evaluation_type,\n seed=self.seed)\n else:\n raise ValueError('Invalid task type!')\n\n self.optimizer['fe'] = build_fe_optimizer(self.evaluation_type, self.task_type, self.original_data,\n fe_evaluator, estimator_id, per_run_time_limit,\n per_run_mem_limit, self.seed,\n shared_mode=self.share_fe, n_jobs=n_jobs)\n\n self.inc['fe'], self.local_inc['fe'] = self.original_data, self.original_data\n\n # Build the HPO component.\n # trials_per_iter = max(len(self.optimizer['fe'].trans_types), 20)\n trials_per_iter = self.one_unit_of_resource * self.number_of_unit_resource\n\n self.optimizer['hpo'] = build_hpo_optimizer(self.evaluation_type, hpo_evaluator, cs, output_dir=output_dir,\n per_run_time_limit=per_run_time_limit,\n trials_per_iter=trials_per_iter,\n seed=self.seed, n_jobs=n_jobs)\n\n self.inc['hpo'], self.local_inc['hpo'] = self.default_config, self.default_config\n self.local_hist['fe'].append(self.original_data)\n self.local_hist['hpo'].append(self.default_config)\n\n def collect_iter_stats(self, _arm, results):\n for arm_id in self.arms:\n self.update_flag[arm_id] = False\n\n if _arm == 'fe' and len(self.final_rewards) == 0:\n self.incumbent_perf = self.optimizer['fe'].baseline_score\n self.final_rewards.append(self.incumbent_perf)\n\n self.logger.info('After %d-th pulling, results: %s' % (self.pull_cnt, results))\n\n score, iter_cost, config = results\n if score is None:\n score = 0.0\n self.rewards[_arm].append(score)\n self.evaluation_cost[_arm].append(iter_cost)\n self.local_inc[_arm] = config\n\n # Update global incumbent from FE and HPO.\n if score > self.incumbent_perf and np.isfinite(score):\n self.inc[_arm] = config\n self.local_hist[_arm].append(config)\n if _arm == 'fe':\n self.inc['hpo'] = self.default_config\n else:\n if self.mth not in ['alter_hpo', 'rb_hpo']:\n self.inc['fe'] = self.original_data\n else:\n self.inc['fe'] = self.local_inc['fe']\n\n self.incumbent_perf = score\n\n arm_id = 'fe' if _arm == 'hpo' else 'hpo'\n self.update_flag[arm_id] = True\n\n if self.mth in ['rb_hpo', 'alter_hpo'] and _arm == 'fe':\n self.prepare_optimizer(arm_id)\n if self.mth == 'alter_p':\n self.prepare_optimizer(arm_id)\n\n def optimize_rb(self):\n # First pull each arm #sliding_window_size times.\n if self.pull_cnt < len(self.arms) * self.sliding_window_size:\n arm_picked = self.arms[self.pull_cnt % 2]\n else:\n imp_values = list()\n for _arm in self.arms:\n increasing_rewards = get_increasing_sequence(self.rewards[_arm])\n\n impv = list()\n for idx in range(1, len(increasing_rewards)):\n impv.append(increasing_rewards[idx] - increasing_rewards[idx - 1])\n imp_values.append(np.mean(impv[-self.sliding_window_size:]))\n\n self.logger.debug('Imp values: %s' % imp_values)\n if imp_values[0] == imp_values[1]:\n # Break ties randomly.\n # arm_picked = np.random.choice(self.arms, 1)[0]\n arm_picked = 'fe' if self.action_sequence[-1] == 'hpo' else 'hpo'\n else:\n arm_picked = self.arms[np.argmax(imp_values)]\n\n # Early stopping scenario.\n if self.optimizer[arm_picked].early_stopped_flag is True:\n arm_picked = 'hpo' if arm_picked == 'fe' else 'fe'\n if self.optimizer[arm_picked].early_stopped_flag is True:\n self.early_stopped_flag = True\n return\n\n self.action_sequence.append(arm_picked)\n self.logger.info('Pulling arm: %s for %s at %d-th round' % (arm_picked, self.estimator_id, self.pull_cnt))\n results = self.optimizer[arm_picked].iterate()\n self.collect_iter_stats(arm_picked, results)\n self.pull_cnt += 1\n\n def optimize_alternatedly(self):\n # First choose one arm.\n _arm = self.arms[self.pull_cnt % 2]\n self.logger.info('Pulling arm: %s for %s at %d-th round' % (_arm, self.estimator_id, self.pull_cnt))\n\n # Execute one iteration.\n results = self.optimizer[_arm].iterate()\n self.collect_iter_stats(_arm, results)\n self.action_sequence.append(_arm)\n self.pull_cnt += 1\n\n def optimize_one_component(self, mth):\n _arm = 'hpo' if mth == 'hpo_only' else 'fe'\n self.logger.info('Pulling arm: %s for %s at %d-th round' % (_arm, self.estimator_id, self.pull_cnt))\n\n # Execute one iteration.\n results = self.optimizer[_arm].iterate()\n self.collect_iter_stats(_arm, results)\n self.action_sequence.append(_arm)\n self.pull_cnt += 1\n\n def evaluate_joint_solution(self):\n # Update join incumbent from FE and HPO.\n _perf = None\n try:\n with time_limit(600):\n if self.task_type in CLS_TASKS:\n _perf = ClassificationEvaluator(\n self.local_inc['hpo'], data_node=self.local_inc['fe'], scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)(self.local_inc['hpo'])\n else:\n _perf = RegressionEvaluator(\n self.local_inc['hpo'], data_node=self.local_inc['fe'], scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)(self.local_inc['hpo'])\n except Exception as e:\n self.logger.error(str(e))\n # Update INC.\n if _perf is not None and _perf > self.incumbent_perf and np.isfinite(_perf):\n self.inc['hpo'] = self.local_inc['hpo']\n self.inc['fe'] = self.local_inc['fe']\n self.incumbent_perf = _perf\n\n def play_once(self):\n if self.early_stopped_flag:\n return self.incumbent_perf\n\n if self.mth in ['rb', 'rb_hpo']:\n self.optimize_rb()\n self.evaluate_joint_solution()\n elif self.mth in ['alter', 'alter_p', 'alter_hpo']:\n self.optimize_alternatedly()\n self.evaluate_joint_solution()\n elif self.mth in ['fe_only', 'hpo_only']:\n self.optimize_one_component(self.mth)\n else:\n raise ValueError('Invalid method: %s' % self.mth)\n\n self.final_rewards.append(self.incumbent_perf)\n return self.incumbent_perf\n\n def fetch_local_incumbents(self):\n return self.optimizer['fe'].local_datanodes\n\n def sync_global_incumbents(self, global_nodes: typing.List[DataNode]):\n fe_optimizer = self.optimizer['fe']\n fe_optimizer.global_datanodes = []\n for node in global_nodes:\n _node = node.copy_()\n _node.depth = node.depth\n fe_optimizer.global_datanodes.append(_node)\n fe_optimizer.refresh_beam_set()\n\n def predict_proba(self, X_test, is_weighted=False):\n \"\"\"\n weight source: ...\n model 1: local_inc['fe'], default_hpo\n model 2: default_fe, local_inc['hpo']\n model 3: local_inc['fe'], local_inc['hpo']\n :param X_test:\n :param is_weighted:\n :return:\n \"\"\"\n X_train_ori, y_train_ori = self.original_data.data\n X_train_inc, y_train_inc = self.local_inc['fe'].data\n\n model1_clf = fetch_predict_estimator(self.task_type, self.default_config, X_train_inc, y_train_inc)\n model2_clf = fetch_predict_estimator(self.task_type, self.local_inc['hpo'], X_train_ori, y_train_ori)\n model3_clf = fetch_predict_estimator(self.task_type, self.local_inc['hpo'], X_train_inc, y_train_inc)\n model4_clf = fetch_predict_estimator(self.task_type, self.default_config, X_train_ori, y_train_ori)\n\n if is_weighted:\n # Based on performance on the validation set\n # TODO: Save the results so that the models will not be trained again\n from automlToolkit.components.ensemble.ensemble_selection import EnsembleSelection\n from autosklearn.metrics import balanced_accuracy\n sss = StratifiedShuffleSplit(n_splits=1, test_size=0.33, random_state=1)\n X, y = X_train_ori.copy(), y_train_ori.copy()\n _X, _y = X_train_inc.copy(), y_train_inc.copy()\n for train_index, test_index in sss.split(X, y):\n X_train, X_val, y_train, y_val = X[train_index], X[test_index], y[train_index], y[test_index]\n _X_train, _X_val, _y_train, _y_val = _X[train_index], _X[test_index], _y[train_index], _y[test_index]\n\n assert (y_val == _y_val).all()\n model1_clf_temp = fetch_predict_estimator(self.task_type, self.default_config, _X_train, _y_train)\n model2_clf_temp = fetch_predict_estimator(self.task_type, self.local_inc['hpo'], X_train, y_train)\n model3_clf_temp = fetch_predict_estimator(self.task_type, self.local_inc['hpo'], _X_train, _y_train)\n model4_clf_temp = fetch_predict_estimator(self.task_type, self.default_config, X_train, y_train)\n pred1 = model1_clf_temp.predict_proba(_X_val)\n pred2 = model2_clf_temp.predict_proba(X_val)\n pred3 = model3_clf_temp.predict_proba(_X_val)\n pred4 = model4_clf_temp.predict_proba(X_val)\n\n # Ensemble size is a hyperparameter\n es = EnsembleSelection(ensemble_size=20, task_type=1, metric=balanced_accuracy,\n random_state=np.random.RandomState(self.seed))\n es.fit([pred1, pred2, pred3, pred4], y_val, None)\n weights = es.weights_\n print(\"weights \" + str(weights))\n\n # Make sure that the estimator has \"predict_proba\"\n _test_node = DataNode(data=[X_test, None], feature_type=self.original_data.feature_types.copy())\n _X_test = self.optimizer['fe'].apply(_test_node, self.local_inc['fe']).data[0]\n pred1 = model1_clf.predict_proba(_X_test)\n pred2 = model2_clf.predict_proba(X_test)\n pred3 = model3_clf.predict_proba(_X_test)\n pred4 = model4_clf.predict_proba(X_test)\n\n if is_weighted:\n final_pred = weights[0] * pred1 + weights[1] * pred2 + weights[2] * pred3 + weights[3] * pred4\n else:\n final_pred = (pred1 + pred2 + pred3 + pred4) / 4\n\n return final_pred\n\n def predict(self, X_test, is_weighted=False):\n proba_pred = self.predict_proba(X_test, is_weighted)\n return np.argmax(proba_pred, axis=-1)\n\n def prepare_optimizer(self, _arm):\n if _arm == 'fe':\n # Build the Feature Engineering component.\n if self.task_type in CLS_TASKS:\n fe_evaluator = ClassificationEvaluator(self.inc['hpo'], scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)\n elif self.task_type in REG_TASKS:\n fe_evaluator = RegressionEvaluator(self.inc['hpo'], scorer=self.metric,\n name='fe', resampling_strategy=self.evaluation_type,\n seed=self.seed)\n else:\n raise ValueError('Invalid task type!')\n self.optimizer[_arm] = build_fe_optimizer(self.evaluation_type, self.task_type, self.inc['fe'],\n fe_evaluator, self.estimator_id, self.per_run_time_limit,\n self.per_run_mem_limit, self.seed, n_jobs=self.n_jobs,\n shared_mode=self.share_fe)\n else:\n # trials_per_iter = self.optimizer['fe'].evaluation_num_last_iteration // 2\n # trials_per_iter = max(20, trials_per_iter)\n trials_per_iter = self.one_unit_of_resource * self.number_of_unit_resource\n if self.task_type in CLS_TASKS:\n hpo_evaluator = ClassificationEvaluator(self.default_config, scorer=self.metric,\n data_node=self.inc['fe'], name='hpo',\n resampling_strategy=self.evaluation_type,\n seed=self.seed)\n elif self.task_type in REG_TASKS:\n hpo_evaluator = RegressionEvaluator(self.default_config, scorer=self.metric,\n data_node=self.inc['fe'], name='hpo',\n resampling_strategy=self.evaluation_type,\n seed=self.seed)\n else:\n raise ValueError('Invalid task type!')\n\n self.optimizer[_arm] = build_hpo_optimizer(self.evaluation_type, hpo_evaluator, self.config_space,\n output_dir=self.output_dir,\n per_run_time_limit=self.per_run_time_limit,\n trials_per_iter=trials_per_iter,\n seed=self.seed, n_jobs=self.n_jobs)\n\n self.logger.info('=' * 30)\n self.logger.info('UPDATE OPTIMIZER: %s' % _arm)\n self.logger.info('=' * 30)\n","sub_path":"automlToolkit/bandits/second_layer_bandit.py","file_name":"second_layer_bandit.py","file_ext":"py","file_size_in_byte":19726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"580907637","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule contains statistic evaluations functions\n\nCreated on 08.07.2018\n\n@author: mate.pentek@tum.de, anoop.kodakkal@tum.de, michael.andre@tum.de\n\"\"\"\n\n\nimport matplotlib.mlab as mlab\nimport numpy as np\nfrom scipy.stats import gaussian_kde, tmean, tstd, skew, kurtosis # , mode\n# from scipy.stats.mstats import mode\n\n\ndef get_pdf_kde(data_series):\n '''\n Evaluates the probability distribution function (pdf)\n of the samples by using a non-parametric estimation technique called Kernal Desnity\n Estimation (KDE). More details can be found at\n https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.gaussian_kde.html.\n '''\n results = {}\n\n data_series_max = np.max(data_series)\n data_series_min = np.min(data_series)\n\n if len(data_series) > 1:\n kde = gaussian_kde(data_series)\n results['x'] = np.linspace(data_series_min, data_series_max, 1000)\n results['y'] = kde(results['x'])\n elif len(data_series) == 0:\n print(\"Data series has 1 element\")\n print(\"Fallback solution\")\n results['x'] = [0.0]\n results['y'] = data_series[0]\n else:\n print(\"Data series has no elements\")\n print(\"Fallback solution\")\n results['x'] = []\n results['y'] = []\n\n return results\n\n\ndef get_pdf_normal(data_series):\n '''\n Estimates the normal pdf of the signal from the mean\n and standard deviation of the samples. Recall the fact that a Normal distribution\n can be entirely defined by two parameters, namely the mean and standard deviation.\n More details about the function mlab.normpdf can be found at\n https://matplotlib.org/api/mlab_api.html.\n '''\n\n results = {}\n\n data_series_max = np.max(data_series)\n data_series_std = np.std(data_series)\n data_series_mean = np.mean(data_series)\n data_series_min = np.min(data_series)\n data_series_step = (data_series_max - data_series_min) / 1000\n\n if data_series_std == 0.0:\n results['x'] = np.zeros(len(data_series))\n results['y'] = np.zeros(len(data_series))\n\n else:\n data_series_pdf = mlab.normpdf(np.arange(data_series_min, data_series_max + data_series_step, data_series_step),\n data_series_mean,\n data_series_std)\n results['x'] = np.arange(\n data_series_min, data_series_max + data_series_step, data_series_step)\n results['y'] = data_series_pdf\n\n return results\n\n\ndef get_pdf(data_series, case='KDE'):\n\n if case == 'KDE':\n return get_pdf_kde(data_series)\n\n elif case == 'Normal':\n return get_pdf_normal(data_series)\n\n else:\n raise Exception(\n \"PDF type not implemented, choose either KDE or Normal\")\n\n\ndef get_general_statistics(data_series, calculate_mode):\n\n results = {}\n\n results['mean'] = tmean(data_series)\n try:\n results['std'] = tstd(data_series)\n except:\n print(\"Probably not enough data in data series to calculate std, length of array: \", str(\n len(data_series)))\n print(\"Fallback solution: returning std = 0.\")\n results['std'] = 0.0\n\n results['skewness'] = skew(data_series)\n # one can choose between Fisher's and Pearson's definition\n results['kurtosis'] = kurtosis(data_series, fisher=True)\n # new additions\n results['min'] = min(data_series)\n results['max'] = max(data_series)\n\n results['pdf'] = get_pdf(data_series)\n\n # NOTE: mode is time-consuming\n if calculate_mode:\n # version 1\n # scipy.stats.mode seems to have a problem and deliver bad results\n # r esults['mode'] = mode(data_series)[0][0]\n\n # version 2\n # alternative would be scipy.stats.mstat.mode as new_mode\n # which seems to deliver more correct results than versiom 1\n # and be the most robust\n # it also seems to be a lot faster than version 1\n # results['mode'] = mode(data_series)[0][0]\n\n # version 3\n # note: as PDF is anyway calculated, taking mode from there as the max\n # value in the PDF\n '''\n Assuming unimodal functions:\n A mode of a continuous probability distribution is a value at which the\n probability density function (pdf) attains its maximum value\n '''\n if (len(results['pdf']['y']) > 1):\n results['mode'] = results['pdf']['x'][np.argmax(\n results['pdf']['y'])]\n else:\n print(\"y component of pdf has no values\")\n print(\"Fallback solution taking mode = 0.\")\n results['mode'] = 0.\n\n return results\n\n\ndef get_extreme_values_statistics(data_series, ramp_up_idx, block_size, calculate_mode, case='BM'):\n\n if case == 'BM':\n print('## Evaluating BM - Block-Maxima')\n return get_block_maxima(data_series, ramp_up_idx, block_size, calculate_mode)\n\n elif case == 'POT':\n print('## Evaluating POT - Peak-Over-Threshol')\n raise Exception(\"POT not implemented, choose BM\")\n\n else:\n raise Exception(\n \"Extreme value evaluation not implemented, choose either BM or POT\")\n\n\ndef get_block_maxima(data_series, ramp_up_idx, nr_of_blocks, calculate_mode):\n\n block_size = np.round(len(data_series) / nr_of_blocks)\n nr_of_sections = int(np.round(len(data_series) / block_size))\n series_sections = np.array_split(data_series, nr_of_sections)\n global_idx_adjustment = ramp_up_idx\n\n classical_extremes_val = []\n classical_extreme_idx = []\n alternative_extremes_val = []\n alternative_extremes_idx = []\n block_start_idx = []\n\n for section in series_sections:\n block_start_idx.append(global_idx_adjustment)\n\n # sign of mean_val and max_val has to coincide by definition\n mean_val = np.mean(section)\n\n if mean_val >= 0.0:\n min_val = np.min(section)\n max_val = np.max(section)\n\n else:\n max_val = np.min(section)\n min_val = np.max(section)\n\n classical_extremes_val.append(max_val)\n classical_extreme_idx.append(np.where(section == max_val)[\n 0][0] + global_idx_adjustment)\n\n if np.sign(max_val) != np.sign(min_val):\n alternative_extremes_val.append(min_val)\n alternative_extremes_idx.append(np.where(section == min_val)[\n 0][0] + global_idx_adjustment)\n\n global_idx_adjustment += len(section)\n\n block_start_idx.append(global_idx_adjustment - 1)\n\n classical_extremes_stat = get_general_statistics(\n classical_extremes_val, calculate_mode)\n\n if alternative_extremes_val:\n alternative_extremes_stat = get_general_statistics(\n alternative_extremes_val, calculate_mode)\n\n else:\n print(\"## No alternative extremes found, using 0.0 as dummy statistic values not to break plotting\")\n alternative_extremes_val.append(0.0)\n alternative_extremes_idx.append(0)\n\n alternative_extremes_stat = {}\n alternative_extremes_stat['mean'] = 0.0\n alternative_extremes_stat['std'] = 0.0\n alternative_extremes_stat['kurtosis'] = 0.0\n alternative_extremes_stat['skewness'] = 0.0\n alternative_extremes_stat['min'] = 0.0\n alternative_extremes_stat['max'] = 0.0\n\n if calculate_mode:\n alternative_extremes_stat['mode'] = 0.0\n\n alternative_extremes_stat['pdf'] = {}\n alternative_extremes_stat['pdf']['x'] = np.asarray([])\n alternative_extremes_stat['pdf']['y'] = np.asarray([])\n\n results = {}\n results['block_start_idx'] = np.asarray(block_start_idx)\n results['classical'] = {}\n results['classical']['val'] = np.asarray(classical_extremes_val)\n results['classical']['idx'] = np.asarray(classical_extreme_idx)\n results['classical']['statistics'] = classical_extremes_stat\n results['alternative'] = {}\n results['alternative']['val'] = np.asarray(alternative_extremes_val)\n results['alternative']['idx'] = np.asarray(alternative_extremes_idx)\n results['alternative']['statistics'] = alternative_extremes_stat\n\n return results\n\n\ndef get_velocity_and_pressure_autocorrelation(time_series, velocity_series, pressure_series, target_lux=[80.0, 100.0, 120.0]):\n '''\n Spectral length for target autocorrelation\n specified by default\n '''\n\n t = time_series\n # time shift to start from 0 the autocorrelation results\n t = t - t[0]\n\n u = velocity_series\n p = pressure_series\n\n umean = np.mean(u)\n u = u - umean\n nx = len(u)\n ur = np.array([u[i] for i in range(nx - 1, -1, -1)])\n u = np.hstack((u, u[:-1]))\n # it is the correlation of velocity with itself - so autocorrelation\n r_uu = np.convolve(ur, u, mode='valid') / nx\n # r/r[0] represents the normalized autocorrelation of velocity\n r_uu = r_uu / r_uu[0]\n\n pmean = np.mean(p)\n p = p - pmean\n nx = len(p)\n pr = np.array([p[i] for i in range(nx - 1, -1, -1)])\n # it is the correlation of pressure with itself - so autocorrelation\n p = np.hstack((p, p[:-1]))\n r_pp = np.convolve(pr, p, mode='valid') / nx\n # r/r[0] represents the normalized autocorrelation of pressure\n r_pp = r_pp / r_pp[0]\n\n results = {}\n results['time'] = t\n results['velocity'] = r_uu\n results['pressure'] = r_pp\n results['target'] = {}\n\n for tl in target_lux:\n f1 = np.exp(-0.822 * (umean * t / tl)**0.77)\n target_autocorr = 0.5 * (f1 + f1**2)\n\n results['target'][\"ESDU Lux={:5.1f}\".format(tl)] = target_autocorr\n\n return results\n\n\ndef get_velocity_spectra(time_series, velocity_series, z=25.0, z0=0.06):\n '''\n Default values for z and z0 hardcoded - here for the gable_roof_wind_2.h5\n All results should be based upon this generated wind\n '''\n def Fu_exact(k1z): return 52.5 * k1z / (1. + 33. * k1z)**(5. / 3.)\n\n kxz_fit = np.array([0.00198943678865, 0.00296803190755, 0.00442799361834, 0.00660610400926, 0.00985561722592,\n 0.0147035515589, 0.021936163255, 0.0327264645157, 0.0488244670342, 0.0728410054812,\n 0.108671172505, 0.162126039523, 0.241875118171, 0.360852414347, 0.538354113993,\n 0.8031681112, 1.19824293728, 1.78765331532, 2.66699204005, 3.9788735773])\n Fu_fit = np.array([0.105791883098, 0.146072921739, 0.197712237681, 0.260742123281, 0.332502406069,\n 0.406558248946, 0.47290167654, 0.520191810197, 0.539347512083, 0.526518073323,\n 0.484089036307, 0.420301445225, 0.347791585792, 0.278950554163, 0.220213310142,\n 0.172281158258, 0.133847652859, 0.103416441602, 0.0795951241049, 0.0611154974287])\n\n u = velocity_series\n umean = np.mean(velocity_series)\n # calculate the respective length measure\n # based upon length of time series and the mean velocity\n lx = (time_series[-1] - time_series[0]) * umean\n\n utau = 0.41 * umean / np.log(z / z0)\n\n nx = len(u)\n Fu = np.zeros(int(nx / 2) + 1)\n\n kx = np.array([2.0 * np.pi * i / lx for i in range(int(nx / 2) + 1)])\n kxz = kx * z / (2.0 * np.pi)\n\n Fu = Fu + kx * abs(np.fft.fft(u)[:int(nx / 2) + 1]\n )**2 * lx / nx**2 / (2.0 * np.pi) / utau**2\n\n results = {}\n results['kxz'] = kxz\n results['Fu'] = Fu\n results['kxz_fit'] = kxz_fit\n results['Fu_fit'] = Fu_fit\n results['Fu_exact'] = Fu_exact(kxz)\n\n return results\n","sub_path":"utilities/statistic_utilities.py","file_name":"statistic_utilities.py","file_ext":"py","file_size_in_byte":11504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57854843","text":"\"\"\"\n1530. Number of Good Leaf Nodes Pairs\n\"\"\"\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\nclass Solution:\n def countPairs(self, root: TreeNode, distance: int) -> int:\n count = 0\n \n def helper(node): \n nonlocal count\n if node is None: \n return []\n if node.left is None and node.right is None: \n return [1]\n \n left = helper(node.left)\n right = helper(node.right)\n \n retArr = []\n \n for i in range(len(left)): \n for j in range(len(right)):\n if left[i] + right[j] <= distance:\n count += 1\n\n for n in left + right: \n if n + 1 < distance: \n retArr.append(n+1)\n return retArr\n \n \n helper(root)\n return count","sub_path":"Leetcode Medium/ countPairs.py","file_name":" countPairs.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"479159592","text":"# # 29. Divide Two Integers\n# class Solution:\n# def divide(self, dividend, divisor):\n# if dividend == 0:\n# return 0\n# if (dividend > 0) == (divisor > 0):\n# flag = 1\n# else:\n# flag = -1\n# if abs(divisor) == 1 and abs(dividend) != 1:\n# if flag == -1:\n# return flag*abs(dividend)\n# else:\n# if divisor <0 :\n# return abs(dividend)-1\n# else:\n# return abs(dividend)\n# dividend = abs(dividend)\n# divisor = abs(divisor)\n# i = 1\n# temp = divisor\n# minus = dividend - divisor\n# while temp < dividend:\n# i += 1\n# temp += divisor\n# minus = dividend - temp\n# if temp > 2**31-1 or temp < -2**31:·\n# return 2**31-1\n# if temp - dividend <= minus:\n# return flag*i\n# else:\n# return flag*(i-1)\n\nclass Solution:\n def divide(self, dividend, divisor):\n \"\"\"\n :type dividend: int\n :type divisor: int\n :rtype: int\n \"\"\"\n result = 0\n temp = 0\n sign = -1 if (dividend>0) ^ (divisor>0) else 1\n if dividend == 0 or divisor == 0:\n return 0\n dividend = abs(dividend)\n divisor = abs(divisor)\n\n for i in range(32, -1, -1):\n if temp + (divisor << i) <= dividend:\n temp += divisor << i\n result |= 1 << i\n\n result = sign*result\n\n return 2**31 -1 if result < -2**31 or result > (2**31) -1 else result\n","sub_path":"LEETCODE/TOPICS/MATH/029.py","file_name":"029.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"161122322","text":"from collections import deque\nf, s, g, u, d=map(int, input().split())\nvisit=[0]*(f+1)\nvisit[s]=1\nq=deque()\nq.append([s, 0])\nwhile q:\n q_pop=q.popleft()\n now=q_pop[0]\n cost=q_pop[1]\n if now+u<=f and visit[now+u]==0:\n q.append([now+u, cost+1])\n visit[now+u]=visit[now]+1\n if now-d>0 and visit[now-d]==0:\n q.append([now-d, cost+1])\n visit[now-d]=visit[now]+1\nif visit[g]: print(visit[g]-1)\nelse: print(\"use the stairs\")\n","sub_path":"BOJ/5014.py","file_name":"5014.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"40843514","text":"from unittest import mock, TestCase\nfrom rooms.main import RoomNumberCalculator\n\n\nclass ReservationValidatorTest(TestCase):\n\n def setUp(self):\n self.room_num_cal = RoomNumberCalculator()\n\n def test_success(self):\n reservation_list = [(1, 2), (3, 5), (0, 2), (10, 23)]\n result = self.room_num_cal.is_reservations_list_valid(reservation_list)\n self.assertTrue(result)\n\n def test_not_tuple(self):\n reservation_list = [(1, 2), [3, 5], (0, 2), (10, 23)]\n result = self.room_num_cal.is_reservations_list_valid(reservation_list)\n self.assertFalse(result)\n\n def test_not_int(self):\n reservation_list = [(1, 'a'), (3, 5), (0, 2), (10, 23)]\n result = self.room_num_cal.is_reservations_list_valid(reservation_list)\n self.assertFalse(result)\n\n def test_out_of_range(self):\n reservation_list = [(1, 2), (3, 5), (0, 24), (10, 23)]\n result = self.room_num_cal.is_reservations_list_valid(reservation_list)\n self.assertFalse(result)\n\n\nclass RoomNumberCalculatorTest(TestCase):\n\n def setUp(self):\n self.room_num_cal = RoomNumberCalculator()\n\n def test_unfold_reservations(self):\n result = self.room_num_cal._unfold_hours([(0,1), (1,3)])\n self.assertEqual([0, 1, 1, 2, 3], result)\n\n result = self.room_num_cal._unfold_hours([(0, 1)])\n self.assertEqual([0, 1], result)\n\n result = self.room_num_cal._unfold_hours([(3, 3)])\n self.assertEqual([3], result)\n\n result = self.room_num_cal._unfold_hours([(1,1), (3, 3)])\n self.assertEqual([1, 3], result)\n\n result = self.room_num_cal._unfold_hours([(1, 1), (1, 1), (1, 1)])\n self.assertEqual([1, 1, 1], result)\n\n result = self.room_num_cal._unfold_hours([(1, 1), (1, 6)])\n self.assertEqual([1, 1, 2, 3, 4, 5, 6], result)\n\n def test_get_rooms_num_from_exercise(self):\n reservations = [\n (3, 5), # reservation A\n (2, 9), # reservation B\n (0, 4), # reservation C\n (0, 2), # reservation D\n (8, 8), # reservation E\n (3, 7), # reservation F\n (6, 9), # reservation G\n (5, 6), # reservation H\n (7, 9), # reservation I\n (1, 2) # reservation J\n ]\n\n expected = 4 # as in the task\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, expected)\n\n def test_get_rooms_num_one_reservation_one_room(self):\n reservations = [\n (3, 5)\n ]\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 1)\n\n reservations = [\n (8, 8)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 1)\n\n def test_get_rooms_num_two_reservations_one_room(self):\n reservations = [\n (3, 5),\n (6, 10)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 1)\n\n reservations = [\n (3, 3),\n (6, 6)\n ]\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 1)\n\n def test_get_rooms_num_two_reservations_two_rooms(self):\n reservations = [\n (5, 10),\n (3, 5)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 2)\n\n reservations = [\n (7, 7),\n (7, 7)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 2)\n\n def test_get_rooms_no_reservations_no_rooms(self):\n reservations = [\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 0)\n\n def test_get_rooms_all_day_reservation(self):\n reservations = [\n (0, 23),\n (0, 23)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 2)\n\n reservations = [\n (0, 23),\n (4, 10),\n (11, 23)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 2)\n\n reservations = [\n (0, 23),\n (4, 10),\n (10, 23)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 3)\n\n def test_get_rooms_edge_reservations(self):\n reservations = [\n (0, 0),\n (23, 23)\n ]\n\n result = self.room_num_cal.get_rooms_num(reservations)\n self.assertEqual(result, 1)\n\n def test_get_rooms_invalid_reservations(self):\n self.room_num_cal.is_reservations_list_valid.return_value = False\n\n with self.assertRaises(ValueError) as err:\n result = self.room_num_cal.get_rooms_num(['dummy invalid list'])\n self.assertTrue(\"Invalid reservation list\" in str(err.exception))\n","sub_path":"src/rooms/test/test_main.py","file_name":"test_main.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"31844150","text":"from util import RequestBody\nfrom schemas import TallySheetVersionCE201Schema\nfrom orm.entities.Submission import TallySheet\nfrom orm.entities.SubmissionVersion.TallySheetVersion import TallySheetVersionCE201\nfrom exception import NotFoundException\n\n\ndef get_by_id(tallySheetId, tallySheetVersionId):\n result = TallySheetVersionCE201.get_by_id(\n tallySheetId=tallySheetId,\n tallySheetVersionId=tallySheetVersionId\n )\n\n return TallySheetVersionCE201Schema().dump(result).data\n\n\ndef get_all(tallySheetId):\n tallySheet = TallySheet.get_by_id(tallySheetId=tallySheetId)\n if tallySheet is None:\n raise NotFoundException(\"Tally sheet not found. (tallySheetId=%d)\" % tallySheetId)\n\n result = TallySheetVersionCE201.get_all(\n tallySheetId=tallySheetId\n )\n\n return TallySheetVersionCE201Schema(many=True).dump(result).data\n\n\ndef create(tallySheetId, body):\n request_body = RequestBody(body)\n tallySheetVersion = TallySheetVersionCE201.create(\n tallySheetId=tallySheetId\n )\n\n tally_sheet_content = request_body.get(\"content\")\n if tally_sheet_content is not None:\n for party_count_body in tally_sheet_content:\n party_count_body = RequestBody(party_count_body)\n tallySheetVersionRow = tallySheetVersion.add_row(\n areaId=party_count_body.get(\"areaId\"),\n ballotsIssued=party_count_body.get(\"ballotsIssued\"),\n ballotsReceived=party_count_body.get(\"ballotsReceived\"),\n ballotsSpoilt=party_count_body.get(\"ballotsSpoilt\"),\n ballotsUnused=party_count_body.get(\"ballotsUnused\"),\n boxCountOrdinary=party_count_body.get(\"boxCountOrdinary\"),\n boxCountTendered=party_count_body.get(\"boxCountTendered\"),\n ballotPaperAccountOrdinary=party_count_body.get(\"ballotPaperAccountOrdinary\"),\n ballotPaperAccountTendered=party_count_body.get(\"ballotPaperAccountTendered\")\n )\n\n for issued_ballot_body in party_count_body.get(\"issuedBallots\"):\n issued_ballot_body = RequestBody(issued_ballot_body)\n tallySheetVersionRow.add_issued_ballot_box(issued_ballot_body.get(\"stationaryItemId\"))\n\n for received_ballot_body in party_count_body.get(\"receivedBallots\"):\n received_ballot_body = RequestBody(received_ballot_body)\n tallySheetVersionRow.add_received_ballot_box(received_ballot_body.get(\"stationaryItemId\"))\n\n return TallySheetVersionCE201Schema().dump(tallySheetVersion).data\n","sub_path":"api/TallySheetVersionApi/TallySheetVersionCE201Api.py","file_name":"TallySheetVersionCE201Api.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"188543635","text":"# -*- coding: utf-8 -*-\nfrom Crypto.Cipher import AES\nfrom Crypto import Random\nfrom Crypto.Hash import SHA256\nimport codecs\n\n\nclass Crypt:\n def __init__(self, key):\n key = codecs.encode(key, 'utf-8')\n self.key = SHA256.new(key).digest()\n self.mode = AES.MODE_CFB\n\n def encrypt(self, plaintext):\n iv = Random.new().read(16)\n aes = AES.new(self.key, self.mode, IV=iv)\n ciphertext = aes.encrypt(plaintext)\n cipherIV = b\"\".join([ciphertext,iv])\n return codecs.encode(cipherIV, 'hex_codec')\n\n def decrypt(self, cipherIV):\n cipherIV = codecs.decode(cipherIV, 'hex_codec')\n ciphertext = cipherIV[:-16]\n iv = cipherIV[-16:]\n aes = AES.new(self.key, self.mode, IV=iv)\n plaintext = aes.decrypt(ciphertext)\n return codecs.encode(plaintext, \"utf-8\")\n\n# unit test\nif __name__ == '__main__':\n crypt = Crypt(\"password\")\n ciphertext= crypt.encrypt(\"If you can read this, the class is working\")\n plaintext = crypt.decrypt(ciphertext)\n print(plaintext)\n","sub_path":"Crypt.py","file_name":"Crypt.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"373335012","text":"#!/usr/bin/env python\n# For Python 3\n\nimport sys\nimport MachineConf as Config\n\nConf = Config.Conf\n\ndef convertNumToFieldSpec(num):\n Left = int(num / 8)\n Right = num % 8\n return (Left, Right)\n\ndef parseFieldSpec(field):\n Field = None\n Left = None\n Right = None\n\n # See if `field' is 2-tuple\n try:\n Left, Right = field\n except TypeError:\n # Or is it a intable\n try:\n Field = int(field)\n except TypeError:\n raise TypeError(\"`field_spec' must be a 2-tupel or an integer! ({} given)\"\\\n .format(field))\n else:\n Left, Right = convertNumToFieldSpec(Field)\n \n return (Left, Right)\n\nclass Byte(object):\n Data = 0\n\n def __init__(self, value=0):\n self.setValue(value)\n \n def check(self, value=None):\n \"\"\"Checks the validity of `value'. If `value' < 0 or `value'\n >= 100, returns False. Otherwise returns True. If `value' is\n None, check self.Data.\n\n This function may be moved to the \"machine\" class later.\n\n Ref: TAOCP I 1.3.1 p.125.\n \"\"\"\n if value == None:\n return (self.Data >=0 and self.Data < Conf.ByteValues)\n else:\n return (value >=0 and value < Conf.ByteValues)\n\n def value(self):\n return self.Data\n\n def setValue(self, value):\n # Value = None\n # try:\n # self.setValue(value.value())\n # except(AttributeError):\n # # Is it a number?\n # try:\n # value + 1\n # except(TypeError):\n # raise TypeError(\"A byte can only take number values!\")\n # else:\n # self.Data = int(value)\n \n if not self.check(value):\n raise ValueError(\"Value {} is too large for a bite~~\".format(value))\n return\n\n self.Data = value\n return\n\n def __str__(self):\n Length = len(str(Conf.ByteValues - 1))\n return ''.join([\"{:#0\", str(Length), \"d}\"]).format(self.value())\n\n def __repr__(self):\n return \"Byte({})\".format(str(self))\n\nclass Sign(object):\n \"\"\"A `Sign' can either be \"+\" or \"-\". It is a \"+\" if it is one of\n 1, \"+\", or True. It is a \"-\" if it is one of 0, \"-\", or False.\n\n We use True or False internally to represent these two states,\n while they are printed out as \"+\" or \"-\".\n \"\"\"\n \n Value = True\n\n def __init__(self, value=True):\n self.setValue(value)\n\n def isPlus(self, value=None):\n ToBeChecked = value\n if value == None:\n ToBeChecked = self.Value\n\n if ToBeChecked in (1, '+', True):\n return True\n else:\n try:\n return self.isPlus(ToBeChecked.value())\n except(AttributeError):\n return False\n\n def isMinus(self, value=None):\n ToBeChecked = value\n if value == None:\n ToBeChecked = self.Value\n\n if ToBeChecked in (0, '-', False):\n return True\n else:\n try:\n return self.isMinus(ToBeChecked.value())\n except(AttributeError):\n return False\n\n def value(self):\n return self.Value\n\n def setValue(self, value):\n if self.isPlus(value):\n self.Value = True\n elif self.isMinus(value):\n self.Value = False\n else:\n raise TypeError(\"{} is not a sign!\".format(value))\n\n def flip(self):\n \"\"\"Flips the sign.\"\"\"\n if self.Value:\n self.Value = False\n else:\n self.Value = True\n\n def __str__(self):\n if self.isPlus():\n return '+'\n elif self.isMinus():\n return '-'\n else:\n return None\n\n def __repr__(self):\n return \"Sign({})\".format(str(self))\n\nclass ArbitraryWord(object):\n def __init__(self, n_of_bytes=5, sign='+'):\n self.Bytes = []\n self.Sign = Sign(sign)\n \n for i in range(n_of_bytes):\n self.Bytes.append(Byte())\n\n def __len__(self):\n \"\"\"Returns the number of bytes in this word, without the sign.\n \"\"\"\n return len(self.Bytes)\n\n def __getitem__(self, key):\n \"\"\"Follows the field specification of words. See field().\n The third slicing index is always ignored.\n \"\"\"\n Indices = key\n try:\n Indices = key.indices(len(self) + 1)\n except(AttributeError):\n # `key' is a number.\n return self.field((Indices, Indices))\n else: # `key' is a slice.\n return self.field((Indices[0], Indices[1]))\n\n def __setitem__(self, key, value):\n Indices = key\n try:\n Indices = key.indices(len(self) + 1)\n except(AttributeError):\n # `key' is a number.\n self.checkFieldSpecAndRaise(Indices, Indices)\n if Indices == 0:\n self.setSign(value)\n else:\n self.Bytes[Indices - 1] = Byte(value)\n \n else: # `key' is a slice\n # `value' has to be a sequence of stuff whose elements can\n # be values of bytes.\n Left = Indices[0]\n Right = Indices[1]\n self.field((Left, Right), value)\n return value\n \n def __str__(self):\n return ' '.join(map(str, [self.Sign] + self.Bytes))\n\n def checkFieldSpec(self, left, right=None):\n \"\"\"Check a if a field specification is legal. Returns an\n error message if illegal. Returns True if legal.\n \"\"\"\n Left = left\n Right = right\n\n if right == None:\n # Then the specfication means 8*left + right.\n # Ref: TAOCP I 1.3.1 p.127.\n Field = convertNumToFieldSpec(left)\n Left = Field[0]\n Right = Field[1]\n \n if Left < 0 or Left > len(self):\n return \"{0} as a Left of a field spec is illegal for word with {1} bytes!\"\\\n .format(Left, len(self))\n if Right < 0 or Right > len(self):\n return \"{0} as a Right of a field spec is illegal for word with {1} bytes!\"\\\n .format(Right, len(self))\n if Left > Right:\n return \"Left must be equal or smaller than the Right of a field spec!\"\n return True\n\n def checkFieldSpecAndRaise(self, left, right=None):\n \"\"\"Check a if a field specification is legal. Raise an\n IndexError if illegal. Returns True if legal.\n \"\"\"\n IsLegal = self.checkFieldSpec(left, right)\n if IsLegal != True:\n raise IndexError(IsLegal)\n\n def field(self, field_spec, value=None):\n \"\"\"Accepts one or two arguments.\n\n If the `value' is not given, returns a partial field of word.\n `field_spec' is a tuple, (left, right). The return value is\n always a list.\n\n If two arguments are given, assign `value' to a partial field\n of word. Returns `value'.\n\n Ref: TAOCP I 1.3.1 p.126.\n \"\"\"\n\n Left, Right = field_spec\n self.checkFieldSpecAndRaise(Left, Right)\n\n if value != None: # Assign a field\n if Left == Right:\n try:\n self.Bytes[Left-1] = Byte(value)\n except (TypeError, ValueError):\n pass\n else:\n return value\n else:\n try:\n len(value)\n except TypeError:\n raise TypeError(\"Cannot assign a non-sequence type to a field with multiple bytes!\")\n \n if Left == 0: # Set the sign.\n if len(value) != Right - Left:\n raise ValueError(\"The length of the value to be assigned must match the slice in a slice assignment to ArbitraryWord!\")\n self.setSign(value.sign())\n self.Bytes[0:Right] = value.Bytes\n else: # ...Yep\n if len(value) != Right - Left + 1:\n raise ValueError(\"The length of the value to be assigned must match the slice in a slice assignment to ArbitraryWord!\")\n self.Bytes[Left-1:Right] = map(Byte, value)\n return value\n\n else: # Extract a field\n if Left == Right:\n # Returns the sign, or one byte.\n if Left == 0:\n return [self.Sign]\n else:\n return [self.Bytes[Left-1].value()]\n \n if Left == 0:\n ByteSlice = self.Bytes[Left:Right]\n # Returns a word.\n NewWord = ArbitraryWord()\n NewWord.setSign(self.Sign)\n NewWord.Bytes = ByteSlice\n return NewWord\n\n ByteSlice = self.Bytes[Left-1:Right]\n # Returns a list of integers...\n return [b.value() for b in ByteSlice]\n\n def sign(self):\n return self.Sign\n\n def setSign(self, sign):\n self.Sign = Sign(sign)\n\n def flipSign(self):\n self.Sign.flip()\n\n def unpack(self, field):\n \"\"\"Extracts a single integer from a field, and returns it.\n The format of field follows that of the field() method.\n\n Ref: TAOCP I 1.3.1 p.128.\n \"\"\"\n\n def formInt(raw):\n # Returns the number corresponding to `raw', a list of digits\n Result = 0\n if Conf.BigEndian:\n for Digit in range(len(raw)):\n Result += raw[Digit] * Conf.ByteValues ** (len(raw) - Digit - 1)\n else:\n for Digit in range(len(raw)):\n Result += raw[Digit] * Conf.ByteValues ** Digit\n\n return Result\n\n if field == 0 or field == (0, 0):\n return self.sign()\n \n RawValues = self.field(field)\n # RawValues may be a Word or a list of integers.\n try:\n RawValues.sign()\n except AttributeError:\n # It's a list of integers.\n return formInt(RawValues)\n else:\n # It's a word.\n Result = RawValues.unpack((1, len(RawValues)))\n if RawValues.sign().isMinus():\n return -1 * Result\n else:\n return Result\n\n def pack(self, field, value):\n \"\"\"Stores a single integer in a field, and returns the word\n instance in question. The format of field follows that of the\n field() method.\n\n Ref: TAOCP I 1.3.1 p.128.\n \"\"\"\n def serializeInt(num, count):\n # Convert int `num' into a list of `count' number of ints.\n Result = []\n\n if Conf.BigEndian:\n for Digit in range(count):\n Result.insert(0, int(num / (Conf.ByteValues ** Digit)) % Conf.ByteValues)\n else:\n for Digit in range(count):\n Result.append(int(num / (Conf.ByteValues ** Digit)) % Conf.ByteValues)\n\n return Result\n\n Left, Right = field\n if Left == 0: # Packing a int with sign\n if Right == 0: # Packing only a sign\n try:\n Sig = Sign(value)\n except TypeError:\n raise TypeError(\"You are trying to pack a non-sign into (0:0) of a word!\")\n else:\n self.Sign = Sig\n else:\n if value < 0:\n self.setSign('-')\n self.pack((1, Right), -value)\n else:\n self.setSign('+')\n self.pack((1, Right), value)\n else: # Packing only an int\n if(value < 0):\n raise ValueError(\"Packing a negative number into a field not starting from 0!\")\n if value >= Conf.ByteValues ** (Right - Left + 1):\n raise ValueError(\"{} is too big to be packed into {}!\".format(value, (Left, Right)))\n\n self.field((Left, Right), serializeInt(value, Right - Left + 1))\n\n return self\n \n\nclass Word(ArbitraryWord):\n \"\"\"A sign and five bytes, as stated in TAOCP.\n\n Ref: TAOCP I 1.3.1 p.125.\n \"\"\"\n def __init__(self, init_vals=None, sign='+'):\n \"\"\"Creates an instance of Word. If two arguments are issued,\n the second one must has a length of 5.\n \"\"\"\n ArbitraryWord.__init__(self, 5, sign)\n if init_vals:\n if len(init_vals) != 5:\n raise ValueError(\"A Word must be initialized with a seqence of length 5!\")\n else:\n self[1:5] = init_vals\n","sub_path":"Word.py","file_name":"Word.py","file_ext":"py","file_size_in_byte":12876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"273483171","text":"\"\"\"Code generation utilities\"\"\"\nfrom .utils import SchemaInfo, is_valid_identifier\n\n\nclass CodeSnippet(object):\n \"\"\"Object whose repr() is a string of code\"\"\"\n def __init__(self, code):\n self.code = code\n\n def __repr__(self):\n return self.code\n\n\nSCHEMA_CLASS_TEMPLATE = '''\nclass {classname}({basename}):\n \"\"\"{docstring}\"\"\"\n _schema = {schema!r}\n _rootschema = {rootschema!r}\n\n {init_code}\n'''\n\n\ndef schema_class(classname, schema, rootschema=None, basename='SchemaBase',\n schemarepr=None, rootschemarepr=None):\n \"\"\"Generate code for a schema class\n\n Parameters\n ----------\n classname : string\n The name of the class to generate\n schema : dict\n The dictionary defining the schema class\n rootschema : dict (optional)\n The root schema for the class\n basename : string (default: \"SchemaBase\")\n The name of the base class to use in the class definition\n schemarepr : CodeSnippet or object, optional\n An object whose repr will be used in the place of the explicit schema.\n This can be useful, for example, when the generated code should reference\n a predefined schema object. The user must ensure that the schema within\n the evaluated code is identical to the schema used to generate the code.\n rootschemarepr : CodeSnippet or object, optional\n An object whose repr will be used in the place of the explicit root\n schema.\n \"\"\"\n rootschema = rootschema if rootschema is not None else schema\n schemarepr = schemarepr if schemarepr is not None else schema\n if rootschemarepr is None:\n if rootschema is schema:\n rootschemarepr = CodeSnippet('_schema')\n else:\n rootschemarepr = rootschema\n return SCHEMA_CLASS_TEMPLATE.format(\n classname=classname,\n basename=basename,\n schema=schemarepr,\n rootschema=rootschemarepr,\n docstring=docstring(classname=classname, schema=schema,\n rootschema=rootschema, indent=4),\n init_code=init_code(classname=classname, schema=schema,\n rootschema=rootschema, indent=4)\n )\n\n\ndef docstring(classname, schema, rootschema=None, indent=4):\n # TODO: add a general description at the top, derived from the schema.\n # for example, a non-object definition should list valid type, enum\n # values, etc.\n info = SchemaInfo(schema, rootschema)\n doc = [\"{0} schema wrapper\".format(classname)]\n if info.description:\n doc += ['', info.description]\n if info.properties:\n doc += ['',\n 'Attributes',\n '----------']\n for prop, propinfo in info.properties.items():\n doc += [\"{0} : {1}\".format(prop, propinfo.short_description),\n \" {0}\".format(propinfo.description.replace('\\n', ' '))]\n if len(doc) > 1:\n doc += ['']\n return (\"\\n\" + indent * \" \").join(doc)\n\n\nINIT_DEF = \"\"\"\ndef __init__({arglist}):\n super({classname}, self).__init__({super_arglist})\n\"\"\".lstrip()\n\n\ndef init_code(classname, schema, rootschema=None, indent=0):\n \"\"\"Return code suitablde for the __init__ function of a Schema class\"\"\"\n info = SchemaInfo(schema, rootschema=rootschema)\n\n args = ['self']\n super_args = []\n\n if info.is_empty() or info.is_compound():\n args.extend(['*args', '**kwds'])\n super_args.extend(['*args', '**kwds'])\n elif info.is_value():\n args.extend(['*args'])\n super_args.extend(['*args'])\n elif info.is_object():\n required = {p for p in info.required if is_valid_identifier(p)}\n props = {p for p in info.properties if is_valid_identifier(p)}\n props -= required\n\n args.extend('{0}=Undefined'.format(p)\n for p in sorted(required) + sorted(props))\n args.append('**kwds')\n\n super_args.extend('{0}={0}'.format(p)\n for p in sorted(required) + sorted(props))\n super_args.append('**kwds')\n else:\n raise ValueError(\"Schema object not understood\")\n\n code = INIT_DEF.format(classname=classname,\n arglist=', '.join(args),\n super_arglist=', '.join(super_args))\n if indent:\n code = code.replace('\\n', '\\n' + indent * ' ')\n return code\n","sub_path":"tools/schemapi/codegen.py","file_name":"codegen.py","file_ext":"py","file_size_in_byte":4362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"322943695","text":"import numpy as np\r\nimport cv2\r\n\r\n\r\ndef make_bitmap(p, nelx, nely, alpha):\r\n\t\"\"\"\r\n\tMAKE BITMAP IMAGE OF MULTIPHASE TOPOLOGY\r\n\tParameters\r\n\t----------\r\n\t'p' is a penalization factor used for SIMP model\r\n\t'nelx' and 'nely' are the number of elements along the two axis\r\n\t'alpha' is a nely-by-nelx matrix representing the density field\r\n\t----------\r\n\tReturns\r\n\t-------\r\n\t'I' bitmap image values\r\n\t-------\r\n\t\"\"\"\r\n\tcolor = np.array([[0, 0, 0], [0.392, 0.584, 0.929], [1, 1, 1]], dtype=float)\t\t# steel + aluminium; black, blue, white\r\n\t# color = np.array([[0, 0, 0], [0.4627, 0.933, 0.776], [1, 1, 1]], dtype=float)\t\t# steel + titanium; black, green, white\r\n\tI = np.zeros((nelx*nely, 3), dtype=float)\r\n\tfor j in range(p):\r\n\t\tI[:, 0:3] = I[:, 0:3] + np.reshape(alpha[:, j], (nelx*nely, 1))*color[j, 0:3]\r\n\t# __ bilinear interpolation\r\n\tIre = np.reshape(I, (nely, nelx, 3), order='F')\r\n\tdim = (10*nelx, 10*nely)\r\n\tI = cv2.resize(Ire, dim, interpolation=cv2.INTER_LINEAR)\r\n\treturn I\r\n","sub_path":"multitop/static/make_bitmap.py","file_name":"make_bitmap.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"375134602","text":"#!/usr/bin/env python3\n\n\nimport pifacedigitalio as pf\nimport os\nimport picamera\nimport time\nfrom datetime import datetime\nfrom time import sleep\nimport subprocess\nimport sys\n\n###config\nLogFile='../log/log.txt'\nsensitivy=10\nVideoPath='/var/www/securitycenter/app/webroot/files/videos/'\n\ndef main():\n\n c0=0\n c1=0\n c0c=0\n c1c=0\n \n f=open(LogFile,'a')\n f.write(str(datetime.now()))\n f.write('@System initialisiert\\n')\n f.close()\n \n while True:\n sleep(0.1)\n \n #print(\"S0 = \" + str(c0c) + \" / S1= \" + str(c1c)) \n\n if (pf.digital_read(0) == 0):\n c0 += 1\n #print(\"Sensor 0 triggert \" + str(c0))\n\n\n if (pf.digital_read(1) == 0):\n c1 += 1\n #print(\"Sensor 1 triggert \" + str(c1))\n\n\n #if (pf.digital_read(3)):\n #print(\"sensor 0 was triggert \" + str(c0c) + \" times.\")\n #print(\"Sensor 1 was triggert \" + str(c1c) + \" times.\")\n #print(\"Exit Script\")\n #pf.digital_write(3, 0)\n #pf.digital_write(7, 0)\n #pf.deinit()\n #exit(0)\n\n if (c0 > sensitivy or c1 > sensitivy):\n if (c0 > 100):\n c0c += 1\n #pf.digital_write(2, 1)\n #pf.digital_write(7, 0)\n else:\n c1c += 1\n #pf.digital_write(7, 1)\n #pf.digital_write(2, 0)\n c0 = 0\n c1 = 0\n \n #Motion detected\n f=open(LogFile,'a')\n f.write(str(datetime.now()))\n f.write('@Bewegung erkannt@System startet Aufzeichnung\\n')\n ts = datetime.strptime(str(datetime.now()), \"%Y-%m-%d %H:%M:%S.%f\")\n filename = str(ts.strftime('%Y-%m-%d_%H-%M-%S')) + '.h264'\n pf.digital_write(3, 1)\n cam.start_recording(VideoPath + filename)\n sleep(60)\n cam.stop_recording()\n pf.digital_write(3, 0)\n #cam.start_preview()\n #cam.capture('foo.jpg')\n #cam.stop_preview()\n \n f.write(str(datetime.now()))\n f.write('@Aufzeichnung beendet.@Sytsem wieder aktiv\\n')\n sleep(0.1)\n f.close()\n #if (c0c > c1c):\n #pf.digital_write(3, 1)\n #pf.digital_write(6, 0)\n #elif (c0c == c1c):\n #pf.digital_write(6, 1)\n #pf.digital_write(3, 1) \n #else: \n #pf.digital_write(6, 1)\n #pf.digital_write(3, 0)\nif __name__ == '__main__':\n # init PiFace\n pf.init()\n cam = picamera.PiCamera()\n \n s0 = pf.digital_read(0)\n s1 = pf.digital_read(1)\n #timestamp = datetime.strptime(str(datetime.now()), \"%Y-%m-%d %H:%M:%S.%f\")\n #print(str(timestamp.strftime('%Y-%m-%d_%H-%M-%S')))\n #print(\"Status: S0 = \" + str(s0) + \" / S1= \" + str(s1)) \n #exit(0)\n \n # call main function\n main()\n","sub_path":"system/piface/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"426709905","text":"import argparse\nimport json\nimport torch\nfrom torch.nn import functional as F\nfrom preprocessing import *\nfrom modelcheckpoint import *\n\ndef predict(image_path, model, topk=5, device=None):\n ''' Predict the class (or classes) of an image using a trained deep learning model.\n '''\n if device is None:\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n \n np_image = process_image(image_path)\n np_image = np.array([np_image])\n model,_,_ = load_checkpoint(model, device=device)\n model.eval()\n model.to(device)\n output = model.forward(torch.from_numpy(np_image.astype(np.float32)).to(device) )\n output = F.softmax(output, dim=1)\n top_classes = torch.topk(output, topk)\n probs = top_classes[0].data.numpy()[0]\n indices = top_classes[1].data.numpy()[0]\n classes = np.array([model.output_model.idx_to_class[i] for i in indices])\n return probs,classes\n\nif __name__ == '__main__':\n #PARSE ARGUMENTS\n parser = argparse.ArgumentParser(description='Predict label for an image using given model.')\n parser.add_argument('input', help='image to classify.')\n parser.add_argument('checkpoint', help='saved model.')\n parser.add_argument('--top_k', default=1, type=int, help='output k most probable classes.')\n parser.add_argument('--category_names', help='JSON file mapping categories to class names.')\n parser.add_argument('--gpu', action='store_const', const='cuda', default='cpu')\n args = parser.parse_args()\n \n probs,classes = predict(args.input, args.checkpoint, topk=args.top_k, device=args.gpu)\n \n if args.category_names is not None:\n try:\n with open(args.category_names, 'r') as f:\n cat_to_name = json.load(f)\n classes = [cat_to_name[i] for i in classes]\n except:\n print(\"unable to parse file with category names\")\n \n print('Most likely classes: ',classes)\n print('Probabilities: ',probs)\n \n ","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"363689787","text":"import sys\nimport logging\nimport base64\nimport io\nimport os\n\nfrom services.onmt_utils import stanford_ptb_detokenizer, stanford_ptb_tokenizer, summary\n\nfrom aiohttp import web\nfrom jsonrpcserver.aio import methods\nfrom jsonrpcserver.exceptions import InvalidParams\n\nimport services.common\n\n\nlog = logging.getLogger(__package__ + \".\" + __name__)\n\n\ndef summarise_text(text):\n tokens = stanford_ptb_tokenizer(text)\n score, p = summary(tokens)\n result = p[0].replace(' ', '').replace(' ', '').replace('', '')\n return stanford_ptb_detokenizer(result)\n\n\n@methods.add\nasync def ping():\n return 'pong'\n\n\n@methods.add\nasync def summarise(**kwargs):\n text = kwargs.get(\"text\", None)\n\n if text is None:\n raise InvalidParams(\"text is required\")\n\n from multiprocessing import Pool\n global config\n with Pool(1) as p:\n result = p.apply(summarise_text, (text,))\n\n return {'summary': result}\n\n\nasync def handle(request):\n request = await request.text()\n response = await methods.dispatch(request, trim_log_values=True)\n if response.is_notification:\n return web.Response()\n else:\n return web.json_response(response, status=response.http_status)\n\n\nif __name__ == '__main__':\n parser = services.common.common_parser(__file__)\n args = parser.parse_args(sys.argv[1:])\n services.common.main_loop(None, None, handle, args)","sub_path":"text-summarization/services/summary_server.py","file_name":"summary_server.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"293290090","text":"from django.db import models\nfrom django.conf import settings\n\nfrom taggit.managers import TaggableManager\n\n\nclass Thread(models.Model):\n\n title = models.CharField('Título', max_length=100)\n body = models.TextField('Mensagem')\n author = models.ForeignKey(\n settings.AUTH_USER_MODEL, verbose_name='Autor', related_name='threads')\n views = models.IntegerField('Visualizações', blank=True, default=0)\n answers = models.IntegerField('Respostas', blank=True, default=0)\n\n tags = TaggableManager()\n\n created = models.DateTimeField('Criado em', auto_now_add=True)\n modified = models.DateTimeField('Modificado em', auto_now=True)\n\n def __str__(self):\n return self.title\n\n class Meta:\n verbose_name = 'Tópico'\n verbose_name_plural = 'Tópicos'\n ordering = ['-modified']\n\n\nclass Reply(models.Model):\n\n thread = models.ForeignKey(\n Thread, verbose_name='Tópico', related_name='replies')\n reply = models.TextField('Resposta')\n author = models.ForeignKey(\n settings.AUTH_USER_MODEL, verbose_name='Autor', related_name='replies')\n correct = models.BooleanField('Correta?', blank=True, default=False)\n\n created = models.DateTimeField('Criado em', auto_now_add=True)\n modified = models.DateTimeField('Modificado em', auto_now=True)\n\n def __str__(self):\n return self.reply[:100]\n\n class Meta:\n verbose_name = 'Resposta'\n verbose_name_plural = 'Respostas'\n ordering = ['-correct', 'created']\n","sub_path":"forum/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"367806707","text":"#!/usr/bin/env python3\n# --- Builtin imports ------------------------------------------------------------------------------\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport glob\nimport json\nimport logging\nimport os\n\n# --- 3rd-party imports ----------------------------------------------------------------------------\nfrom sklearn.externals import joblib\n\n# --- PipelineAI imports ---------------------------------------------------------------------------\n# TODO: implement pipeline_bandit Model logic\nfrom pipeline_bandit import Model\nfrom pipeline_monitor import prometheus_monitor as monitor\nfrom pipeline_logger import log\n\n_logger = logging.getLogger('pipeline-logger')\n_logger.setLevel(logging.INFO)\n_logger_stream_handler = logging.StreamHandler()\n_logger_stream_handler.setLevel(logging.INFO)\n_logger.addHandler(_logger_stream_handler)\n\n__all__ = ['invoke']\n\n\n_labels = {\n 'name': 'echo',\n 'tag': 'v1',\n 'runtime': 'python',\n 'chip': 'cpu',\n 'resource_type': 'model',\n 'resource_subtype': 'autoroute',\n}\n\ndef _initialize_upon_import(file_name: str=None, train_datetime: str=None) -> Model:\n \"\"\"\n Load artifact into memory from a pickled binary archive.\n\n The most recently created artifact is returned\n when file_name or train_datetime is not supplied.\n\n :param str file_name: The name of the pickled estimator binary artifact, not including path\n :param str train_datetime: The date and time the training session that created the estimator\n in the format: YmdHMS\n\n :return: object: un-pickled artifact\n \"\"\"\n\n if not file_name:\n\n compressor = 'bz2'\n\n if train_datetime:\n file_name = os.path.join(os.path.dirname(os.path.abspath(__file__)),\n '{}_model.pkl.{}'.format(train_datetime, compressor))\n else:\n file_mask = '*model.pkl.{}'.format(compressor)\n pathname = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_mask)\n file_list = glob.glob(pathname)\n latest_file = max(file_list, key=os.path.getctime)\n file_name = os.path.basename(latest_file)\n\n path = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_name)\n _logger.info('path: {}'.format(path))\n\n with open(path, 'rb') as f:\n artifact = joblib.load(f)\n\n return artifact\n\n\n_model = _initialize_upon_import()\n\n\n@log(labels=_labels, logger=_logger)\ndef invoke(request: bytes) -> str:\n \"\"\"\n Transform bytes posted to the api into a python dictionary containing the\n existing resource routes by tag and weight.\n Predict least expensive routes and adjust higher weights to lower cost routes.\n Transform the model prediction output from python dictionary to a JSON formatted str\n containing the new resource routes by tag and weight\n\n :param bytes request: bytes containing the payload to supply to the predict method\n\n :return: Response obj serialized to a JSON formatted str\n containing the new resource routes by tag and weight\n \"\"\"\n with monitor(labels=_labels, name='transform_request'):\n transformed_request = _transform_request(request)\n\n with monitor(labels=_labels, name='invoke'):\n response = _model.invoke(transformed_request)\n\n with monitor(labels=_labels, name='transform_response'):\n transformed_response = _transform_response(response)\n\n return transformed_response\n\n\ndef _transform_request(request: bytes) -> dict:\n \"\"\"\n Transform bytes posted to the api into a python dictionary containing\n the resource routes by tag and weight\n\n :param bytes request: containing the payload to supply to the predict method\n\n :return: dict containing the resource routes by tag and weight\n \"\"\"\n return dict(json.loads(request.decode('utf-8'))['resource_split_tag_and_weight_dict'])\n\n\ndef _transform_response(response: dict) -> str:\n \"\"\"\n Transform response from a python dictionary to a JSON formatted string\n\n :param dict response: dict containing the new resource routes by tag and weight\n\n :return: Response obj serialized to a JSON formatted str\n \"\"\"\n\n return json.dumps({\n 'resource_split_tag_and_weight_dict': response\n })\n\n\nif __name__ == '__main__':\n with open('pipeline_test_request.json', 'rb') as fb:\n request_bytes = fb.read()\n response_bytes = invoke(request_bytes)\n print(response_bytes)\n","sub_path":"autoroute/cost-v1/model/pipeline_invoke_python.py","file_name":"pipeline_invoke_python.py","file_ext":"py","file_size_in_byte":4617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60272225","text":"# -*- coding: utf-8 -*-\n\nimport cv2\nimport os\n\n#file_dirにオリジナルディレクトリのpath指定\n#resize_dirに出力先ディレクトリのpath指定\n\nfile_dir = '/Users/kiriyamakeisuke/practiceTensorFlow/fruits_discrimination/image_for_prediction'\nfiles = os.listdir(file_dir)\nfor i, file in enumerate(files):\n #if file.split('.')[0].split('_')[-1] == '0':\n if i > 0:\n file_path = file_dir + '/' + file\n print(file_path)\n\n img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n\n size = (56, 56)\n resize_img = cv2.resize(img, size)\n os.remove(file_path)\n cv2.imwrite(file_path, resize_img)\n","sub_path":"recognition_image/resize_image.py","file_name":"resize_image.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"395316815","text":"\n# Source; greatly informative and helpful\n# https://projecteuler.net/thread=1;post=105728\n\n\ndef sum3and5(N):\n num3Multiples = (N - 1) // 3\n num5Multiples = (N - 1) // 5\n num15Multiples = (N - 1) // 15\n sum3Multiples = 3 * num3Multiples\n sum5Multiples = 5 * num5Multiples\n sum15Multiples = 15 * num15Multiples\n # Note that bitwise right shifts are used instead of simply dividing by 2, as this would create a rounding error\n return (sum3Multiples * (num3Multiples + 1) >> 1) + (sum5Multiples * (num5Multiples + 1) >> 1) - (sum15Multiples * (num15Multiples + 1) >> 1)\n\n\nc = int(input())\n\nfor i in range(c):\n\n n = int(input())\n\n print(int(sum3and5(n)))\n","sub_path":"Project Euler Plus/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"309308376","text":"import numpy as np\nimport wfdb as wf\nimport pandas as pd\nfrom biosppy.signals import ecg\nfrom toolkit import splitData\nfrom toolkit import openFile\nfrom numba import jit\nECG_folder_path = '/home/hsiehch/dataset/'\ntable_path = 'labels.csv'\n\nclass RpeakData():\n \n newData = []\n newLabel = []\n ONE_HOT_ENCODE_LABEL = {'A':0, '~':1, 'N':2, 'O':3}\n LABEL_TOTAL_COUNT = []\n MAX = 0\n \n def __init__(self):\n self.table = self.openTable()\n \n def checkMax(self, R_Peak_Array):\n \n for i in range(1, len(R_Peak_Array)):\n length = R_Peak_Array[i] - R_Peak_Array[i-1]\n if length > self.MAX:\n self.MAX = length\n @jit(parallel=True) \n def getRPosition(self):\n data_total = self.table.count(axis = 0)[0]\n \n for i in range(data_total):\n data = openFile.openData(ECG_folder_path, self.table.iloc[i,0])\n peaks = ecg.christov_segmenter(data, 300)\n print(peaks)\n \n self.checkMax(peaks)\n tmp = []\n \n for index in range(1, len(peaks)):\n tmp.append([data[peaks[index-1] : peaks[index]+1]])\n \n self.newData.append(tmp)\n self.newLabel.append([self.ONE_HOT_ENCODE_LABEL[self.table.iloc[i,1]]])\n \n print(self.MAX)\n \n for i in range(self.newData):\n \n datum = []\n for wave in self.newData[i]:\n tmp = np.pad(wave, (0, self.MAX - len(wave)), 'constant')\n datum = datum+tmp\n \n self.newData[i] = datum\n \n print(self.newData[i])\n \n return self.newData, self.newLabel\n \n \n def openTable(self):\n dataFromCSV = pd.read_csv(table_path,dtype='str',header=None)\n return dataFromCSV","sub_path":"R_peak_detection.py","file_name":"R_peak_detection.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"294013387","text":"\"\"\"absolute URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url, include\n\nfrom shop.views import ShopViewSet, CategoryViewSet, TeaViewSetx, comment_view_sets\nfrom common.api import NestedDefaultRouter\n\n\nrouter = NestedDefaultRouter()\nshop_router = router.register('', ShopViewSet)\ncategory_router = shop_router.register(\n 'category', CategoryViewSet,\n base_name='category',\n parents_query_lookups=['shop']\n)\ntea_router = shop_router.register(\n 'tea', TeaViewSet,\n base_name='tea',\n parents_query_lookups=['category']\n)\nmerch_router = shop_router.register(\n 'merch', TeaViewSet,\n base_name='merch',\n parents_query_lookups=['category']\n)\ncomment_view_sets['shop'].route(shop_router)\ncomment_view_sets['tea'].route(tea_router, 'category')\ncomment_view_sets['merch'].route(merch_router, 'category')\n\nurlpatterns = [\n url(r'^', include(router.urls)),\n]\n","sub_path":"shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"240248123","text":"import logging\n\nimport discord\nfrom discord.ext import commands\nfrom discord.ext import tasks\n\nfrom luhack_bot import constants\nfrom luhack_bot import email_tools\nfrom luhack_bot import secrets\nfrom luhack_bot import token_tools\nfrom luhack_bot.db.models import User\nfrom luhack_bot.utils.checks import in_channel\nfrom luhack_bot.utils.checks import is_admin\nfrom luhack_bot.utils.checks import is_in_luhack\n\nlogger = logging.getLogger(__name__)\n\n\nclass Verification(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n if not constants.is_test_mode:\n self.fix_missing_roles.start()\n self.update_members.start()\n\n #: members that have left the discord but are in the database, we keep\n # track here so we can remove them after they've been away for more than\n # a day\n self.members_flagged_as_left = set()\n\n def get_member_in_luhack(self, user_id: int) -> discord.Member:\n \"\"\"Try and fetch a member in the luhack guild.\"\"\"\n return self.bot.luhack_guild().get_member(user_id)\n\n def bot_check_once(self, ctx):\n return is_in_luhack(ctx)\n\n async def apply_roles(self, member: discord.Member):\n user = await User.get(member.id)\n if user is not None:\n await member.add_roles(self.bot.verified_role())\n await member.remove_roles(\n self.bot.potential_role(), self.bot.prospective_role()\n )\n else:\n await member.add_roles(self.bot.potential_role())\n\n @commands.Cog.listener()\n async def on_member_join(self, member):\n # if the user is already in the db, then they're verified\n await self.apply_roles(member)\n\n @tasks.loop(hours=1)\n async def fix_missing_roles(self):\n \"\"\"Apply missing roles.\"\"\"\n for member in self.bot.luhack_guild().members:\n try:\n await self.apply_roles(member)\n except discord.errors.NotFound:\n continue\n\n @tasks.loop(hours=24)\n async def update_members(self):\n users = await User.query.gino.all()\n for user in users:\n member = self.bot.luhack_guild().get_member(user.discord_id)\n if member is None:\n if user.discord_id in self.members_flagged_as_left:\n await user.delete()\n self.members_flagged_as_left.discard(user.discord_id)\n else:\n self.members_flagged_as_left.add(user.discord_id)\n else:\n is_disciple = (\n discord.utils.get(member.roles, id=constants.disciple_role_id)\n is not None\n )\n is_admin = member.guild_permissions.administrator or is_disciple\n\n await user.update(username=member.name, is_admin=is_admin).apply()\n\n @commands.command()\n async def become_prospective(self, ctx, token: str):\n \"\"\"Become a prospective luhacker.\"\"\"\n if token != secrets.prospective_token:\n raise commands.CheckFailure(\"Not a valid prospective token\")\n\n member = self.get_member_in_luhack(ctx.author.id)\n\n await member.remove_roles(self.bot.potential_role())\n await member.add_roles(self.bot.prospective_role())\n await ctx.send(\"Prospective luhacker granted, congrats!\")\n await self.bot.log_message(f\"made member prospective {member} ({member.id})\")\n\n @commands.command(\n name=\"token\",\n aliases=[\"gib_token\", \"i_wanna_be_wizard_too\", \"generate_token\", \"gen_token\"],\n )\n async def generate_token(self, ctx, email: email_tools.lancs_email):\n \"\"\"Generates an authentication token, then emails it to the provided email.\n You must provide a valid lancaster email address or you will not get an\n authentication token.\n\n First step on the path to Grand Master Cyber Wizard\n\n \"\"\"\n existing_user = await User.query.where((User.discord_id == ctx.author.id) | (User.email == email)).gino.first()\n\n if existing_user and existing_user.discord_id != ctx.author.id:\n await ctx.send(\"Looks like you're already registered with this email address\")\n return\n\n is_flagged = (\n existing_user is not None and existing_user.flagged_for_deletion is not None\n )\n\n if existing_user is not None and not is_flagged:\n raise commands.CheckFailure(\"It seems you've already registered.\")\n\n auth_token = token_tools.generate_auth_token(ctx.author.id, email)\n\n logger.info(\"Generated token for user: %s, %s\", ctx.author, auth_token)\n\n await email_tools.send_verify_email(email, auth_token)\n\n await ctx.send(f\"Okay, I've sent an email to: `{email}` with your token!\")\n\n @commands.command(\n name=\"verify\", aliases=[\"auth_plz\", \"i_really_wanna_be_wizard\", \"verify_token\"]\n )\n async def verify_token(self, ctx, auth_token: str):\n \"\"\"Takes an authentication token and elevates you to Verified LUHacker.\n Note that tokens expire after 30 minutes.\n\n Second step on the path to Grand Master Cyber Wizard.\n \"\"\"\n existing_user = await User.get(ctx.author.id)\n is_flagged = (\n existing_user is not None and existing_user.flagged_for_deletion is not None\n )\n\n if existing_user is not None and not is_flagged:\n raise commands.CheckFailure(\"It seems you've already registered.\")\n\n user = token_tools.decode_auth_token(auth_token)\n\n if user is None:\n raise commands.CheckFailure(\n \"That token is invalid or is older than 30 minutes and expired.\"\n )\n\n user_id, user_email = user\n\n if user_id != ctx.author.id:\n raise commands.CheckFailure(\n \"Seems you're not the same person that generated the token, go away.\"\n )\n\n member: discord.Member = self.get_member_in_luhack(ctx.author.id)\n\n assert member is not None\n\n logger.info(\"Verifying member: %s\", ctx.author)\n\n if is_flagged:\n await existing_user.update(flagged_for_deletion=None).apply()\n await ctx.send(\"Congrats, you've been re-verified!\")\n await self.bot.log_message(f\"re-verified member {member} ({member.id})\")\n return\n\n user = User(discord_id=user_id, username=member.name, email=user_email)\n await user.create()\n\n await member.remove_roles(\n self.bot.potential_role(), self.bot.prospective_role()\n )\n await member.add_roles(self.bot.verified_role())\n\n await ctx.send(\n \"Permissions granted, you can now access all of the discord channels. You are now on the path to Grand Master Cyber Wizard!\"\n )\n await self.bot.log_message(f\"verified member {member} ({member.id})\")\n\n @commands.check(is_admin)\n @commands.command()\n async def add_user_manually(self, ctx, member: discord.Member, email: str):\n \"\"\"Manually auth a member.\"\"\"\n logger.info(\"Verifying member: %s\", member)\n\n user = User(discord_id=member.id, username=member.name, email=email)\n await user.create()\n\n await member.remove_roles(\n self.bot.potential_role(), self.bot.prospective_role()\n )\n await member.add_roles(self.bot.verified_role())\n\n await member.send(\n \"Permissions granted, you can now access all of the discord channels. You are now on the path to Grand Master Cyber Wizard!\"\n )\n await ctx.send(f\"Manually verified {member}\")\n await self.bot.log_message(f\"verified member {member} ({member.id})\")\n\n @commands.check(is_admin)\n @commands.check(in_channel(constants.inner_magic_circle_id))\n @commands.command()\n async def user_info(self, ctx, member: discord.Member):\n \"\"\"Get info for a user.\"\"\"\n user = await User.get(member.id)\n\n if user is None:\n await ctx.send(\"No info for that user ;_;\")\n return\n\n await ctx.send(\n f\"User: {user.username} ({user.discord_id}) <{user.email}>. Joined at: {user.joined_at}, Last talked: {user.last_talked}\"\n )\n\n @commands.check(is_admin)\n @commands.check(in_channel(constants.inner_magic_circle_id))\n @commands.command()\n async def check_email(self, ctx, name: str):\n \"\"\"See what user an email belongs to.\"\"\"\n users = await User.query.gino.all()\n\n for user in users:\n if name in user.email:\n await ctx.send(\n f\"User: {user.username} ({user.discord_id}). Joined at: {user.joined_at}, Last talked: {user.last_talked}\"\n )\n break\n else:\n await ctx.send(\"No user with that email exists.\")\n\ndef setup(bot):\n bot.add_cog(Verification(bot))\n","sub_path":"luhack_bot/cogs/verification.py","file_name":"verification.py","file_ext":"py","file_size_in_byte":8854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"92063472","text":"import sys\nsys.stdin = open('input.txt')\n\ndef card_change(card, time, count):\n global max_money\n global found\n count += 1\n for i in range(0, len(card)-1):\n for j in range(i+1, len(card)):\n if found:\n return\n card[i], card[j] = card[j], card[i]\n if card == max_money_list: # 최대값과 같으면\n if (time - count)%2 == 0:\n max_money = int(\"\".join(map(str,card)))\n else:\n card[-1], card[-2] = card[-2], card[-1]\n max_money = int(\"\".join(map(str, card)))\n found = True\n return\n elif count == time:\n max_money = max(max_money, int(\"\".join(map(str, card))))\n else:\n card_change(card, time, count)\n card[i], card[j] = card[j], card[i]\n return\n\nT = int(input())\nfor tc in range(T):\n info, max_time = map(int, input().split())\n card = []\n info = str(info)\n for char in info:\n card.append(int(char))\n max_money_list = sorted(card, reverse=True) # 최대 값 찾기\n max_money = 0\n found = False\n\n card_change(card, max_time, 0)\n print(\"#{} {}\".format(tc+1, max_money))","sub_path":"SWEA/1244_최대상금/s1.py","file_name":"s1.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"163118350","text":"def get_gpu(pid, pref):\n\timport gpu_lock\n\timport sys\n\tboard = str(gpu_lock.obtain_lock_id(pid, pref))\n\n\tif int(board) != int(pref):\n sys.stdout.write(\"GPU:\"+str(pref)+\" is locked!\\n\")\n #sys.stderr.write(\"No GPUs available!\\n\")\n exit()\n\tsys.stdout.write(\"Using GPU:\"+board+\"\\n\")\n\treturn board\n","sub_path":"gpu_access.py","file_name":"gpu_access.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"151037218","text":"from tkinter import *\r\nimport popup\r\n\r\npopup.inicial()\r\n\r\nforca_main = Tk()\r\nforca_main.title(\"Jogo da Forca\")\r\nforca_main.resizable(width = False, height = False)\r\nforca_main.geometry(\"704x500\")\r\n\r\npopup.centrar(forca_main)\r\n\r\n\r\n\r\nlista_palavra_para_adivinhar = [] #em cada index vai estar a letra da palavra que o user dá\r\nlista_underscores = [] #em cada index vai estar um underscore (e tem o mesmo tamanho da lista_palavra_para_adivinhar - mesmo numero de underscores que as letras na palavra dada)\r\ntentativas = [] #letras que o jogador ja usou\r\npalavra_underscores = \"\" #string que vai ter os underscores e letras, para poder ser posto numa label\r\nletra = \"\"\r\n\r\n\r\n\r\nentrada_tentativa = Entry(forca_main, width = 22) #retangulo para meter a letra\r\n\r\nerro = Label(forca_main, text = \"\", font= \"Arial 8 bold\", width = 20, height = 3) #sitio dos erros/notifications\r\n\r\nletras_usadas = Label(forca_main, text = \"\", width = 20, height = 4)\r\n\r\npalavra_escondida = Label(forca_main, text = palavra_underscores) #palavra com underscores \r\n\r\n\r\nimagem_localizacao = \"C:\\\\Users\\\\gonca\\Desktop\\\\tkinter\\\\Jogo da Forca\\\\Jogo da Forca - mods\\\\0.gif\"\r\nphoto = PhotoImage(file = imagem_localizacao) #imagem por default\r\nboneco = Label(image = photo, bg = \"white\")\r\nboneco.image = photo\r\nboneco.grid(column = 0, rowspan = 100)\r\n\r\n\r\n\r\np1 = popup.palavra_para_adivinhar #vai buscar a palavra dada no popup e mete na variavel p1, para mais facil acesso\r\n\r\ndef criar_listas(): #corrido no inicio do programa, serve para fazer as listas com os underscores e as letras \r\n\tglobal p1, lista_palavra_para_adivinhar, lista_underscores\r\n\tfor c in p1:\r\n\t\tlista_palavra_para_adivinhar.append(c)\r\n\r\n\tfor n in range(len(lista_palavra_para_adivinhar)):\r\n\t\tif lista_palavra_para_adivinhar[n] == \" \":\r\n\t\t\tlista_underscores.append(\" \")\r\n\t\telse:\r\n\t\t\tlista_underscores.append(\"_\")\r\n\r\ncriar_listas()\r\n\r\n\r\n\r\ndef criar_palavra_escondida(): #cria a string com underscores e letras que o jogador acertou\r\n\tglobal palavra_underscores, palavra_escondida\r\n\t\r\n\tpalavra_underscores = \"\"\r\n\t\r\n\tfor i in range(len(lista_underscores)):\r\n\t\tpalavra_underscores += lista_underscores[i] + \" \"\r\n\r\n\tpalavra_escondida.configure(text = palavra_underscores)\t\r\n\r\ncriar_palavra_escondida()\r\n\r\n\r\n\r\ndef print_boneco(e): #vai mostrar a imagem correspondente ao erro\r\n\tpath = \"C:\\\\Users\\\\gonca\\\\Desktop\\\\tkinter\\\\Jogo da Forca\\\\Jogo da Forca - mods\\\\\" + str(e)+ \".gif\"\r\n\tphoto = PhotoImage(file = path)\r\n\tboneco.configure(image = photo)\r\n\tboneco.image = photo\r\n\r\n\r\n\r\nabc = popup.abc #vai buscar a variavel abc do ficheiro popup\r\n\r\ndef letra_val(letra): #vai ser corrida para ter a certeza se a letra dada pode ser aceite ou nao. Se houver um erro, da return false; se passar os casos todos, da return de True\r\n\tglobal p1, abc, notificação, lista_palavra_para_adivinhar\r\n\tif letra in tentativas:\r\n\t\tnotificação = \"ERRO: Letra já inserida.\"\r\n\t\treturn False\r\n\t\r\n\telif len(letra) > 1: #o programa aceita mais que uma letra, mas apenas se essa string tiver o mesmo comprimento que a palavra a ser descoberta\r\n\t\tif len(letra) == len(lista_underscores): \r\n\t\t\treturn True\t\t\r\n\t\telse: \r\n\t\t\tnotificação = \"ERRO: Insira uma \\n letra apenas.\"\r\n\t\t\treturn False \r\n\r\n\r\n\telif ( (not (letra in abc)) or (letra == \" \") or (letra == \"\")):\r\n\t\tnotificação = \"ERRO: Insira uma letra \\n minúscula do alfabeto, \\n sem acentos.\"\r\n\t\treturn False\r\n\r\n\telse: #se a string inserida satisfazer todos os requesitos\r\n\t\treturn True\r\n\r\n\r\n\r\n\r\n#Começa o jogo:\r\nnumero_de_enganos = 0 #numero de letras erradas que o jogador ja deu\r\ndef jogo(*args):\r\n\tglobal entrada_tentativa, letra, tentativas, erro, notificação, lista_underscores, lista_palavra_para_adivinhar,numero_de_enganos, p1#palavra para adivinhar\r\n\r\n\tletra = (entrada_tentativa).get()\r\n\r\n\tif letra == p1: #p1 - palavra que o jogador tem que adivinhar, dada no popup\r\n\t\tforca_main.destroy()\r\n\t\tpopup.final(p1, \"Acertou!\", numero_de_enganos)\r\n\t\r\n\telse:\r\n\t\tif letra_val(letra): #se da true, entao n houve nenhum problema, e a letra pode ser utilizada para o resto da logica\r\n\t\t\ttentativas.append(letra)\r\n\t\t\tstring_tentativas = \"\"\r\n\r\n\t\t\t#palavras com mais de 29 letras vão ficar desformatadas - para evitar isto teriamos que fazer a janela maior\r\n\t\t\tr = 25\r\n\t\t\tfor x in range(len(tentativas)):\r\n\t\t\t\tstring_tentativas += tentativas[x] + \", \"\r\n\t\t\t\tif( len(string_tentativas) > r): #se cada linha tiver mais que 29 carateres, fazer parágrafo para evitar desformataçõess\r\n\t\t\t\t\tstring_tentativas += \"\\n\"\r\n\t\t\t\t\tr += 25\r\n\r\n\t\t\tletras_usadas.configure(text = string_tentativas)\r\n\r\n\t\t\tfor x in range(len(lista_palavra_para_adivinhar)):\r\n\t\t\t\tif letra == lista_palavra_para_adivinhar[x]:\r\n\t\t\t\t\tlista_underscores[x] = letra #substitui o underscore correspondente à letra certa inserida\r\n\r\n\t\t\tif letra not in lista_palavra_para_adivinhar:\r\n\t\t\t\tnumero_de_enganos += 1\r\n\t\t\t\tprint_boneco(numero_de_enganos)\r\n\r\n\t\t\tcriar_palavra_escondida()\r\n\r\n\t\telse: #se da false, é pq houve um erro, que será mostrado\r\n\t\t\terro.configure(text = notificação, bg = \"gold\")\t\r\n\r\n\t\r\n\tif numero_de_enganos == 6: \r\n\t\tforca_main.destroy()\r\n\t\tpopup.final(p1, \"Perdeu!\", 6)\r\n\r\n\telif(lista_palavra_para_adivinhar == lista_underscores):\r\n\t\tforca_main.destroy()\r\n\t\tpopup.final(p1, \"Acertou!\", numero_de_enganos)\r\n\r\nforca_main.bind(\"\", jogo)\r\n\r\n\r\n\r\ntentativa_titulo = Label(forca_main, text = \"Escreve aqui:\", height = 1, width = 20, bg = \"gray54\", anchor=W).grid(row = 0, column = 1, sticky = NW)\r\ncompletar_espaço_vazio = Label(forca_main, text = \"\", height = 1, width = 4, bg = \"gray54\").grid(row = 0, column = 2, sticky = NW)\r\nentrada_tentativa.grid(row = 1, column = 1, sticky = NW, ipady=4)\r\nentrada_tentativa.focus_set()\r\n\r\nbotao_confirmar_tentativa = Button(forca_main, text = \"OK\", command = jogo, height = 1).grid(row = 1, column = 2, sticky = NW)\r\nerro.grid(row = 2,column = 1)\r\n\r\nletras_usadas_titulo = Label(forca_main, text = \"Tentativas:\", height = 1, width = 20, bg = \"gray54\", anchor=W).grid(row = 3, column = 1, sticky = NW)\r\ncompletar_espaço_vazio2 = Label(forca_main, text = \"\", height = 1, width = 4, bg = \"gray54\").grid(row = 3, column = 2, sticky = NW)\r\nletras_usadas.grid(row = 4, column = 1, sticky = NW)\r\n\r\npalavra_escondida_titulo = Label(forca_main, text = \"Palavra:\", height = 1, width = 20, bg = \"gray54\", anchor=W).grid(row = 5, column = 1, sticky = NW)\r\ncompletar_espaço_vazio3 = Label(forca_main, text = \"\", height = 1, width = 4, bg = \"gray54\").grid(row = 5, column = 2, sticky = NW)\r\npalavra_escondida.grid(row = 6, column = 1, sticky = NW) \r\n\r\n\r\n\r\nforca_main.mainloop()\r\n","sub_path":"jogo da forca - main.py","file_name":"jogo da forca - main.py","file_ext":"py","file_size_in_byte":6552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"610528235","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse\n\nfrom .models import MonthImgGallery\n\n\n@login_required\ndef root_url(request):\n return redirect(reverse('home'))\n\n\n@login_required\ndef home(request):\n context = {'nav_home_active': True}\n response = render(request, 'site_core/home.html', context)\n return response\n\n\n@login_required\ndef photos(request):\n # Years we have galleries for\n years = []\n for _ in MonthImgGallery.objects.all().order_by('-year'):\n if _.year not in years:\n years.append(_.year)\n\n context = {\n 'nav_photos_active': True,\n 'years': years,\n }\n\n response = render(request, 'site_core/photos.html', context)\n return response\n\n@login_required\ndef ph_gallery(request, gallery_slug):\n title = MonthImgGallery.objects.get(slug=gallery_slug).title\n\n context = {\n 'nav_photos_active': True,\n 'slug' : gallery_slug,\n 'title' : title,\n }\n\n response = render(request, 'site_core/ph_gallery.html', context)\n return response\n\n@login_required\ndef ph_detail(request, gallery_slug, photo_number):\n gallery = MonthImgGallery.objects.get(slug=gallery_slug)\n title = \"{} ({})\".format(gallery.title,\n photo_number)\n\n photo = gallery.get_photo_number(photo_number)\n\n display_image = photo.display_img.url\n\n prev_photo_number = int(photo_number) - 1 if gallery.get_photo_number(int(photo_number) - 1) else None\n next_photo_number = int(photo_number) + 1 if gallery.get_photo_number(int(photo_number) + 1) else None\n\n context = {\n 'nav_photos_active': True,\n 'title' : title,\n 'display_image' : display_image,\n 'gallery_slug' : gallery_slug,\n 'prev_photo_number' : prev_photo_number,\n 'next_photo_number' : next_photo_number,\n }\n\n response = render(request, 'site_core/ph_detail.html', context)\n return response\n\n@login_required\ndef videos(request):\n context = {'nav_videos_active': True}\n response = render(request, 'site_core/videos.html', context)\n return response\n","sub_path":"site_core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"139163103","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3350)\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/reddit2ebook/ebooklib_patched/plugins/tidyhtml.py\n# Compiled at: 2016-05-13 06:16:04\n# Size of source mod 2**32: 2242 bytes\nimport six, subprocess\nfrom ebooklib.plugins.base import BasePlugin\nfrom ebooklib.utils import parse_html_string\n\ndef tidy_cleanup(content, **extra):\n cmd = []\n for k, v in six.iteritems(extra):\n cmd.append('--%s' % k)\n if v:\n cmd.append(v)\n\n try:\n p = subprocess.Popen(['tidy'] + cmd, shell=False, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True)\n except OSError:\n return (3, None)\n\n p.stdin.write(content)\n cont, p_err = p.communicate()\n return (\n p.returncode, cont)\n\n\nclass TidyPlugin(BasePlugin):\n NAME = 'Tidy HTML'\n OPTIONS = {'utf8': None, \n 'tidy-mark': 'no'}\n\n def __init__(self, extra={}):\n self.options = dict(self.OPTIONS)\n self.options.update(extra)\n\n def html_before_write(self, book, chapter):\n if not chapter.content:\n return\n _, chapter.content = tidy_cleanup(chapter.content, **self.options)\n return chapter.content\n\n def html_after_read(self, book, chapter):\n if not chapter.content:\n return\n _, chapter.content = tidy_cleanup(chapter.content, **self.options)\n return chapter.content","sub_path":"pycfiles/reddit2ebook-2.0.0-py3.5/tidyhtml.cpython-35.py","file_name":"tidyhtml.cpython-35.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"149821873","text":"has_view = {}\nstart1, start2, line = input().split(' ')\npre2next = {}\nflag = 1\nfor i in range(int(line)):\n start, _, end = input().split(' ')\n pre2next[start] = end\n has_view[start] = False\n has_view[end] = False\npre = start1\nwhile pre != '-1':\n has_view[pre] = True\n next_addr = pre2next[pre]\n pre = next_addr\npre = start2\nwhile pre != '-1':\n if has_view[pre]:\n print(pre)\n flag=0\n break\n next_addr = pre2next[pre]\n pre = next_addr\nif flag:\n print(-1)","sub_path":"pat1032/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"288974966","text":"# Example code from \"Deep Learning with Python\"\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.model_selection import train_test_split\nimport numpy\n\n\n# fix random seed\nseed = numpy.random.seed(7)\n\n# load pima indians dataset\ndataset = numpy.loadtxt(\"E://!Weiterbildung//!DeepLearning//datasets//pima-indians-diabetes.csv\", delimiter=\",\")\n\n# split into input and output variables\nX = dataset[:,0:8]\nY = dataset[:,8]\n\n# split into 67% for train and 33% for test\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)\n\n# define NN model with Keras\nmodel = Sequential()\nmodel.add(Dense(12, input_dim=8, activation='relu')) # input layer and first hidden layer\nmodel.add(Dense(8, activation= 'relu')) # second hidden layer\nmodel.add(Dense(1, activation= 'sigmoid')) # output layer\n\n# Compile model\nmodel.compile(loss= 'binary_crossentropy' , optimizer= 'adam' , metrics=[ 'accuracy' ])\n\n# Fit the model\n#model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10)\nmodel.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=150, batch_size=10)\n\n# evaluate the model\nscores = model.evaluate(X, Y)\nprint(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))","sub_path":"MLM/170921_test_Keras.py","file_name":"170921_test_Keras.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"599631685","text":"'''\nInput: a List of integers\nReturns: a List of integers\n'''\ndef moving_zeroes(arr: list):\n\t# since beginning must be non zero, we \n index: int = 0\n # iterate thru list, if we find a non zero element\n # we swap it to index, which is the beginning then increment index\n for i in range(len(arr)):\n \telem = arr[i]\n \tif elem != 0:\n \t\tarr[i], arr[index] = arr[index], arr[i]\n \t\tindex += 1 \n\n\n return arr \n\nif __name__ == '__main__':\n # Use the main function here to test out your implementation\n arr = [0, 0, 0, 0, 3, 2, 1]\n # [0, 3, 1, 0, -2]\n \n\n print(f\"The resulting of moving_zeroes is: {moving_zeroes(arr)}\")","sub_path":"moving_zeroes/moving_zeroes.py","file_name":"moving_zeroes.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"122295000","text":"# Copyright (c) 2013, TeamPRO and contributors\n# For license information, please see license.txt\n\nfrom __future__ import unicode_literals\nfrom six import string_types\nimport frappe\nimport json\nfrom frappe.utils import (getdate, cint, add_months, date_diff, add_days,\n nowdate, get_datetime_str, cstr, get_datetime, now_datetime, format_datetime)\nfrom datetime import datetime\nfrom calendar import monthrange\nfrom frappe import _, msgprint\nfrom frappe.utils import flt\nfrom frappe.utils import cstr, cint, getdate\nfrom itertools import count\n\ndef execute(filters=None):\n if not filters:\n filters = {}\n columns = get_columns()\n data = []\n row = []\n conditions, filters = get_conditions(filters)\n summary = get_summary(conditions,filters)\n for sum_list in summary:\n data.append(sum_list)\n return columns, data\n\ndef get_columns():\n columns = [\n _(\"Start Date\") + \":Date:120\",\n _(\"End Date\") + \":Date:120\",\n _(\"Name\") + \":Data:200\",\n _(\"Employee PF\") + \":Currency:200\",\n _(\"Employer PF\") + \":Currency:120\",\n _(\"Service Charge\") + \":Currancy:120\",\n _(\"Total\") + \":Currancy:120\",\n ]\n return columns\n\ndef get_summary(conditions,filters):\n salary_slips_epf = frappe.db.sql(\"\"\"select ss.start_date as start_date,ss.end_date as end_date, \n sd.salary_component as salary_component, sum(sd.amount) as amount from `tabSalary Slip` ss \n left join `tabSalary Detail` sd on sd.parent = ss.name where %s and sd.salary_component = 'EPF' group by start_date\"\"\" % conditions, filters, as_dict=1)\n salary_slips_esi = frappe.db.sql(\"\"\"select ss.start_date as start_date,ss.end_date as end_date, \n sd.salary_component as salary_component, sum(sd.amount) as amount from `tabSalary Slip` ss \n left join `tabSalary Detail` sd on sd.parent = ss.name where %s and sd.salary_component = 'ESI' group by start_date\"\"\" % conditions, filters, as_dict=1)\n ss_list=[]\n for ss in salary_slips_epf:\n Service_charge = round((ss.amount/100)*1)\n total = ss.amount+ss.amount+Service_charge\n ss_list.append([str(ss.start_date),str(ss.end_date),ss.salary_component,ss.amount,ss.amount,Service_charge,total])\n if salary_slips_esi:\n for ss_esi in salary_slips_esi:\n esi_amount = (ss_esi.amount/0.75)*100\n employer_esi = round((esi_amount/100)*3.25)\n Service_charge = 0\n total = ss_esi.amount+employer_esi+Service_charge\n ss_list.append([str(ss_esi.start_date),str(ss_esi.end_date),ss_esi.salary_component,ss_esi.amount,employer_esi,Service_charge,total])\n return ss_list\n\ndef get_conditions(filters):\n if not (filters.get(\"month\") and filters.get(\"year\")):\n msgprint(_(\"Please select month and year\"), raise_exception=1)\n\n filters[\"total_days_in_month\"] = monthrange(cint(filters.year), cint(filters.month))[1]\n\n conditions = \"month(ss.start_date) = %(month)s and year(ss.start_date) = %(year)s\"\n if filters.get(\"company\"): conditions += \" and company = %(company)s\"\n return conditions, filters\n\n@frappe.whitelist()\ndef get_years():\n year_list = frappe.db.sql_list(\"\"\"select distinct YEAR(start_date) from `tabSalary Slip` ORDER BY YEAR(start_date) DESC\"\"\")\n if not year_list:\n year_list = [getdate().year]\n\n return \"\\n\".join(str(year) for year in year_list)","sub_path":"hrpro/hrpro/report/pf_and_esi_summary_report/pf_and_esi_summary_report.py","file_name":"pf_and_esi_summary_report.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"549339076","text":"# coding=utf-8\n\"\"\" 168. Burst Ballons\nDescription\nGiven n balloons, indexed from 0 to n-1. Each balloon is painted with a number on it represented by array nums. You are asked to burst all the balloons. If the you burst balloon i you will get nums[left] * nums[i] * nums[right] coins. Here left and right are adjacent indices of i. After the burst, the left and right then becomes adjacent.\n\nFind the maximum coins you can collect by bursting the balloons wisely.\n\nYou may imagine nums[-1] = nums[n] = 1. They are not real therefore you can not burst them.\n0 ≤ n ≤ 500, 0 ≤ nums[i] ≤ 100\n\nExample\nGiven [4, 1, 5, 10]\nReturn 270\n\nnums = [4, 1, 5, 10] burst 1, get coins 4 * 1 * 5 = 20\nnums = [4, 5, 10] burst 5, get coins 4 * 5 * 10 = 200 \nnums = [4, 10] burst 4, get coins 1 * 4 * 10 = 40\nnums = [10] burst 10, get coins 1 * 10 * 1 = 10\n\nTotal coins 20 + 200 + 40 + 10 = 270\n \"\"\"\n\n\nclass Solution:\n \"\"\"\n @param nums: A list of integer\n @return: An integer, maximum coins\n \"\"\"\n\n def maxCoins(self, nums):\n # write your code here\n if not nums:\n return 0\n\n nums = [1, *nums, 1]\n memo = {}\n return self.memo_search(nums, 0, len(nums)-1, memo)\n\n def memo_search(self, nums, i, j, memo):\n if i == j:\n return 0\n if (i, j) in memo:\n return memo[(i, j)]\n\n best = 0\n for k in range(i+1, j):\n left = self.memo_search(nums, i, k, memo)\n right = self.memo_search(nums, k, j, memo)\n best = max(best, left + nums[i] * nums[k] * nums[j] + right)\n\n memo[(i, j)] = best\n return best\n\n\ndef main():\n nums = [4, 1, 5, 10]\n ans = Solution().maxCoins(nums)\n print(ans)\n\n\nmain()\n","sub_path":"lint-168-burst-balloons/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"232552577","text":"# Импортируем все необходимые библиотеки:\nimport numpy as numpy\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nfrom PIL import Image as Image\nimport sys\nimport math\n\n# Объявляем все глобальные переменные\nfrom PIL import Image\n\nglobal xrot # Величина вращения по оси x\nglobal yrot # Величина вращения по оси y\nglobal ambient # рассеянное освещение\nglobal greencolor # Цвет зеленый\nglobal redcolor # Цвет елочного стебля\nglobal bluecolor\nglobal lightpos # Положение источника освещения\nglobal drowmode # для перемещение куба и цилиндра\nglobal drowmode2 # для перемещения сферы и конуса\nglobal laba1 # выблор какой лабы показывать чайник\nglobal laba2 #\nglobal xlig\nglobal ylig\nglobal zlig\n\nglobal step\n\nglobal x1\nglobal y1\nglobal z1\n\nglobal rotat\n\nglobal qobj\n\n# Процедура инициализации\ndef init():\n global xrot # Величина вращения по оси x\n global yrot # Величина вращения по оси y\n global ambient # Рассеянное освещение\n global greencolor # Цвет зеленый\n global redcolor # Цвет красный\n global lightpos # Положение источника освещения\n global xlig\n global ylig\n global zlig\n global drowmode\n global drowmode2\n global laba1\n global laba2\n global bluecolor\n global yy\n global xx\n global step\n global x1\n global y1\n global z1\n global rotat\n\n global qobj\n\n step = 0\n xx = 3\n yy = 0\n x1 = 0\n y1 = 0\n z1 = 0\n rotat = 0\n\n\n laba1 = True # первоначальная инициализация отрисовывающейся лабы\n laba2 = False\n drowmode = False\n drowmode2 = False\n\n xlig = 1.0\n ylig = 1.0\n zlig = 1.0\n\n xrot = 0.0 # Величина вращения по оси x = 0\n yrot = 0.0 # Величина вращения по оси y = 0\n ambient = (0.9, 0.9, 0.9, 1) # Первые три числа цвет в формате RGB, а последнее - яркость\n bluecolor = (0, 0.3, 0.6, 0) # Зеленый цвет\n greencolor = (0.2, 0.8, 0.0, 0.8) # Зеленый цвет\n redcolor = (0.9, 0.1, 0.1, 1) # красный цвет\n lightpos = (1.0, 1.0, 1.0) # Положение источника освещения по осям xyz\n\n glClearColor(0.7, 0.7, 0.7, 1.0) # Серый цвет для первоначальной закраски\n gluOrtho2D(-6.0, 6.0, -6.0, 6.0) # Определяем границы рисования по горизонтали и вертикали\n # glRotatef(-90, 0.0, 0.0, 0.0) # Сместимся по оси Х на 90 градусов\n # glRotatef(-90, -0.0, 0.0, 0.0)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient) # Определяем текущую модель освещения\n glEnable(GL_LIGHTING) # Включаем освещение\n glEnable(GL_LIGHT0) # Включаем один источник света\n glLightfv(GL_LIGHT0, GL_POSITION, lightpos) # Определяем положение источника света\n\n tex = read_texture('pp.jpg') # текстурная сфера\n qobj = gluNewQuadric()\n gluQuadricTexture(qobj, GL_TRUE)\n glEnable(GL_TEXTURE_2D)\n glBindTexture(GL_TEXTURE_2D, tex)\n glTexEnvi(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_MODULATE)\n\n\n\n\n# Процедура обработки специальных клавиш\ndef specialkeys(key, x, y):\n global ambient\n global xrot\n global yrot\n global laba1\n global laba2\n global drowmode\n global drowmode2\n global lightpos\n global xlig\n global ylig\n global zlig\n\n global yy\n global xx\n global step\n global x1\n global y1\n global z1\n global rotat\n\n # Обработчики для клавиш со стрелками\n\n if key == GLUT_KEY_UP: # Клавиша вверх\n xrot -= 2.0 # Уменьшаем угол вращения по оси Х\n if key == GLUT_KEY_DOWN: # Клавиша вниз\n xrot += 2.0 # Увеличиваем угол вращения по оси Х\n if key == GLUT_KEY_LEFT: # Клавиша влево\n yrot -= 2.0 # Уменьшаем угол вращения по оси Y\n if key == GLUT_KEY_RIGHT: # Клавиша вправо\n yrot += 2.0 # Увеличиваем угол вращения по оси Y\n if key == GLUT_KEY_HOME: # если нажата кнопка home будет отриосываться чаник и цилиндр\n drowmode = True\n laba1 = True\n laba2 = False\n if key == GLUT_KEY_INSERT: # если нажата кнопка insert чайник отрисуется на цилиндре\n drowmode = False\n laba1=True\n laba2=False\n if key == GLUT_KEY_PAGE_UP: # если нажата кнопка page up будет отрисован тор и конус\n drowmode2 = True\n laba1 = False\n laba2 = True\n if key == GLUT_KEY_PAGE_DOWN: # если нажата кнопка page up будет отрисован тор и конус\n drowmode2 = False\n laba1 = False\n laba2 = True\n\n if key == 1: # Клавиша вверх\n xlig += 1.0\n if key == 2: # Клавиша вверх\n xlig -= 1.0\n if key == 3: # Клавиша вверх\n ylig += 1.0\n if key == 4: # Клавиша вверх\n ylig -= 1.0\n if key == 5: # Клавиша вверх\n zlig += 1.0\n if key == 6: # Клавиша вверх\n zlig -= 1.0\n if key == 7: # Клавиша вверх\n ambient = (0.0, 0.0, 0.0, 1)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n if key == 8: # Клавиша вверх\n ambient = (1.0, 1.0, 1.0, 1)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n if key == 9: # Клавиша вверх\n ambient = (1.0, 1.0, 1.0, 0)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n if key == 10: # Клавиша вверх\n ambient = (1.0, 0.0, 0.0, 1)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n if key == 11: # Клавиша вверх\n ambient = (0.0, 1.0, 0.0, 1)\n glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n if key == 12: # Клавиша вверх\n\n # xx = 3*math.cos(45)\n # yy = 3*math.sin(45)\n # xx += math.pi/180\n step += 1\n rotat += 1\n if step == 361: step = 0\n # yy += math.pi/180\n # ambient = (0.0, 0.0, 1.0, 1)\n # glLightModelfv(GL_LIGHT_MODEL_AMBIENT, ambient)\n\n xxx = xx\n yyy = yy\n\n # if step>=1 and step<=45:\n # # xx = 6/180\n # xx = 3 * math.cos(step * math.pi / 180) - 3\n # else:\n # if step >= 46 and step <= 225:\n # xx = 3 * math.cos(step * math.pi / 180) - 3\n # else:\n # if step >= 226 and step <= 360:\n # xx = 3 * math.cos(step * math.pi / 180) - 3\n #\n # if step>=1 and step<=135:\n # # yy = 6/180\n # yy = 3 * math.sin(step * math.pi / 180)\n # else:\n # if step >= 136 and step <= 315:\n # yy = 3 * math.sin(step * math.pi / 180)\n # else:\n # if step >= 316 and step <= 360:\n # yy = 3 * math.sin(step * math.pi / 180)\n\n xx = 3 * math.cos(step * math.pi / 180)\n yy = 3 * math.sin(step * math.pi / 180)\n\n # x1 = xx\n # y1 = yy\n\n\n # print(\"x = \" + str(x1) + \" y = \" + str(y1))\n # print(step)\n\n # glTranslatef(xx - xxx, yy - yyy, 0.0)\n # glRotatef(1,10,0.1,0)\n\n\n\n lightpos = (xlig, ylig, zlig)\n\n glutPostRedisplay() # Вызываем процедуру перерисовки\n\ndef read_texture(filename):\n img = Image.open(filename)\n img_data = numpy.array(list(img.getdata()), numpy.int8)\n textID = glGenTextures(1)\n glBindTexture(GL_TEXTURE_2D, textID) # This is what's missing\n glPixelStorei(GL_UNPACK_ALIGNMENT, 1)\n glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)\n glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)\n glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_DECAL)\n glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, img.size[0], img.size[1], 0, GL_RGB, GL_UNSIGNED_BYTE, img_data)\n return textID\n\n# Процедура перерисовки\ndef draw():\n global xrot\n global yrot\n global laba1\n global laba2\n global lightpos\n global greencolor\n global redcolor\n global drowmode\n global drowmode2\n global xlig\n global ylig\n global zlig\n global bluecolor\n\n global xx\n global yy\n\n global step\n global x1\n global y1\n global z1\n global rotat\n global qobj\n\n\n glClear(GL_COLOR_BUFFER_BIT) # Очищаем экран и заливаем серым цветом\n glPushMatrix() # Сохраняем текущее положение \"камеры\"\n glRotatef(xrot, 1.0, 0.0, 0.0) # Вращаем по оси X на величину xrot\n glRotatef(yrot, 0.0, 1.0, 0.0) # Вращаем по оси Y на величину yrot\n glLightfv(GL_LIGHT0, GL_POSITION, lightpos) # Источник света вращаем вместе c о\n glLightModeli(GL_LIGHT_MODEL_TWO_SIDE, GL_TRUE);\n\n color = (0, 0.4, 0.4, 0)\n # glMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, color)\n\n # glutSolidSphere(1, 20, 20)\n # glEnable(GL_COLOR_MATERIAL)\n # glColor4f(1,1,0,1)\n # glBegin(GL_QUADS)\n # glVertex3f(5,5,1)\n # glVertex3f(-5,5,1)\n # glVertex3f(-5, -5, 1)\n # glVertex3f(5, -5, 1)\n # glEnd()\n # glDisable(GL_COLOR_MATERIAL)\n\n gluSphere(qobj, 1, 50, 50)\n # gluDeleteQuadric(qobj)\n # glDisable(GL_TEXTURE_2D) # текстурная сфера\n\n # print(\"x = \" + str(x1) + \" y = \" + str(y1))\n\n glTranslatef(xx, yy, 0.0)\n\n print(step)\n\n glRotatef(rotat + 90, 1, 0, 0)\n glRotatef(rotat - 270, 0, 1, 0)\n\n glutSolidOctahedron()\n\n # gluDeleteQuadric(qobj)\n # glDisable(GL_TEXTURE_2D) # текстурная сфера\n\n #\n # if(laba1):\n # if(drowmode):\n # xx = 3\n # yy = 0\n # color = (1, 0, 0, 0.0) # 1 прозрачный\n # glutSolidSphere(1, 20,20)\n # glTranslatef(xx, yy, 0.0)\n # glutSolidOctahedron()\n #\n # else:\n # glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) # прозрачный куб\n # glEnable(GL_BLEND)\n # color = (1, 0, 0, 0.5)\n # glTranslatef(-0.6, 0.0, 0.0)\n # glMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, color)\n # glutSolidCube(0.3)\n # glDisable(GL_BLEND) # прозрачный куб\n############\n\n # glTranslatef(0.6, 0.0, 0.0) # текстурная сфера\n # tex = read_texture('pp.jpg')\n # qobj = gluNewQuadric()\n # gluQuadricTexture(qobj, GL_TRUE)\n # glEnable(GL_TEXTURE_2D)\n # glBindTexture(GL_TEXTURE_2D, tex)\n #\n # glTexEnvi(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_MODULATE)\n # gluSphere(qobj, 0.3, 50, 50)\n # gluDeleteQuadric(qobj)\n # glDisable(GL_TEXTURE_2D) # текстурная сфера\n\n############\n # mat_diffuse = (0.2, 1.0, 0.8, 1.0) # отражающийся цилиндр\n # mat_specular = (1.0, 1.0, 1.0, 1.0)\n # mat_shininess = 128.0\n # light_position = (1.1, 1.1, 1.0, 1.0)\n #\n # glLightfv(GL_LIGHT1, GL_POSITION, light_position);\n # glLightfv(GL_LIGHT1, GL_SPECULAR, mat_specular);\n #\n # glMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, mat_diffuse);\n # glMaterialfv(GL_FRONT_AND_BACK, GL_SPECULAR, mat_specular);\n # glMaterialfv(GL_FRONT_AND_BACK, GL_SHININESS, mat_shininess);\n\n # glTranslatef(0.6, 0.0, -0.25)\n # glutSolidCylinder(0.2, 0.5, 20, 20) # отражающийся цилиндр\n # glutSolidSphere(0.4, 20, 20)\n\n glPopMatrix()\n glutSwapBuffers()\n\n\n# Здесь начинается выполнение программы\n# Использовать двойную буферизацию и цвета в формате RGB (Красный, Зеленый, Синий)\nglutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH)\n# Указываем начальный размер окна (ширина, высота)\nglutInitWindowSize(500, 500)\n# Указываем начальное положение окна относительно левого верхнего угла экрана\nglutInitWindowPosition(100, 100)\n# Инициализация OpenGl\nglutInit(sys.argv)\n# Создаем окно с заголовком\nglutCreateWindow(b\"cg2\")\n# Определяем процедуру, отвечающую за перерисовку\nglClear(GL_COLOR_BUFFER_BIT)\n# Очищаем экран и заливаем серым цветом\n\n\n\nglutDisplayFunc(draw)\n\n# Определяем процедуру, отвечающую за обработку клавиш\nglutSpecialFunc(specialkeys)\n# Вызываем нашу функцию инициализации\ninit()\n# Запускаем основной цикл\nglutMainLoop()\n\n","sub_path":"3 course/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":14177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481268646","text":"#coding:utf-8\n\ntry:\n from algo import make_qr as mq\nexcept ModuleNotFoundError:\n from . import make_qr as mq\n print('imported using old dir struct')\n\ndef robot_statement(reading_DONE, boke, basic_result_confidence, med_result_confidence,within_expiration_date,acc_alert_shresh=0.5):\n '''処方箋認識が完了したか否かのboolを受け、それに応じたmediRobotの\n メッセージを返す処理'''\n \n basic_info_confidence = judge_basic_confidence(basic_result_confidence)\n med_info_confidence = judge_med_confidence(med_result_confidence)\n\n print('basic_info_confidence,med_info_confidence',basic_info_confidence,med_info_confidence)\n\n #PATCH\n boke = False\n\n if not reading_DONE:\n msg = '認識中にエラーが発生いたしました...\\n お手数ですが, 再度撮影, 不具合ご報告をお願い出来れば幸いです...'\n\n elif basic_info_confidence=0:\n l_vals.append(chouzai_confidence)\n\n total += len(l_vals)\n cumsum_conf += sum(l_vals)\n\n if total==0:\n return 0\n else:\n return cumsum_conf/total\n\ndef get_basic_confidence(dict_):\n '''患者基本情報のconfidenceがNoneで渡された場合、一番自身のない形の情報構造を\n 返す処理'''\n\n if dict_ is not None:\n return dict_\n \n else:\n basic_confidence, _ = mq.create_dummy_results()\n for key in basic_confidence.keys():\n basic_confidence[key] = 0\n\n return basic_confidence.copy()\n\ndef get_med_confidence(list_):\n '''医薬品情報のconfidenceがNoneで渡された場合、一番自身のない形の情報構造を\n 返す処理'''\n\n if list_ is not None:\n return list_\n\n else:\n _, med_confidence = mq.create_dummy_results()\n for key in mq.l_target_keys:\n med_confidence[0][key] = 0\n \n\n return med_confidence\n","sub_path":"docker/back/django_project/service/ocr/algo/create_msg.py","file_name":"create_msg.py","file_ext":"py","file_size_in_byte":4264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"461690020","text":"#!/usr/bin/env python\n\"\"\"\nThis is the main program for the pre processor. It reads and prepares the Mesh and\ncalculation cases. (radiation and diffraction; set of body conditions).\n\nChanges in version 1.1:\n Added possibility to run the code with custom settings\n\nChanges in version 1.2 (Code Acceleration of the Calculation of Influence Coefficients of Nemoh):\n Added switch influence to ode hdf5 settings\n\nChanges in version 1.3 (Implementation of Higher Order Panel Methods):\n Added logic to store USE_HIGHER_ORDER, NUM_PANEL_HIGHER_ORDER, B_SPLINE_ORDER settings\n in hdf5 file.\n\nChanges in version 1.4 (Dipoles Implementation in NEMOH):\n Added logic to store USE_DIPOLES_IMPLEMENTATION and THIN_PANELS settings\n in hdf5 file.\n\nChanges in version 1.5 (Hydrodynamic Data Exporter Assembly v1.0)\n Added parameters controlling wether or not to compute the drift forces or yaw moment\n\nChanges in version 1.6 (Irregular Frequencies Assembly)\n Added logic to discretize the interior of the free surface when the newly added settings\n to remove irregular frequencies is on.\n Applied some bug fixes to allow the shape of hdf5 file dataset \n to be automatically resized.\n\"\"\"\n\nimport utility\nimport numpy as np\nimport math\nimport sys\nimport h5py\nimport structure\n\nfrom models import TMesh\nfrom models import TCase\nfrom utility import cih\nfrom utility import sih\nimport settings\nimport os\nfrom scipy.spatial import Delaunay\n\n\n__author__ = \"yedtoss, TCSASSEMBLER\"\n__copyright__ = \"Copyright (C) 2014-2015 TopCoder Inc. All rights reserved.\"\n__version__ = \"1.6\"\n\n\ndef read_mesh(hdf5_data, custom_config):\n \"\"\"\n Read the mesh data from the hdf5 file\n Args:\n hdf5_data: object, the hdf5 opened file\n\n Return:\n the mesh data\n \"\"\"\n n_points=0\n n_panels=0\n bodies = hdf5_data.get(structure.H5_BODIES).values()\n n_bodies = len(bodies)\n\n interior_mesh_points = np.empty((3, 0))\n interior_mesh_panels = np.empty((4, 0))\n interior_c_panels = np.empty((0))\n interior_n_points = 0\n interior_n_panels = 0\n remove_irregular_frequencies = utility.get_setting(settings.REMOVE_IRREGULAR_FREQUENCIES, custom_config,\n 'REMOVE_IRREGULAR_FREQUENCIES')\n for c in range(n_bodies):\n body = bodies[c]\n n_points += body.get(structure.H5_BODY_NUM_POINTS)[0]\n n_panels += body.get(structure.H5_BODY_NUM_PANELS)[0]\n\n mesh = TMesh(n_points=n_points, n_panels=n_panels, n_bodies=n_bodies)\n\n n_points = 0\n n_panels = 0\n\n for c in range(n_bodies):\n body = bodies[c]\n\n mesh_arr = body.get(structure.H5_BODY_MESH)\n\n n = mesh_arr[0, 1]\n\n if c > 0 and (n != mesh.i_sym):\n print(' Error: there is an inconsistency in the mesh files regarding the xOz symmetries')\n sys.exit()\n else:\n mesh.i_sym = int(n)\n\n m = body.get(structure.H5_BODY_NUM_POINTS)[0]\n n = body.get(structure.H5_BODY_NUM_PANELS)[0]\n\n for i in range(m):\n mesh.x[:, n_points + i] = np.array(mesh_arr[i + 1, 1:4])\n\n if remove_irregular_frequencies:\n # If we have to remove frequencies, then we need to discretize the free surface\n int_mesh = generate_mesh(np.asarray(mesh_arr[1:m, 1:4]))\n interior_mesh_points = np.concatenate((interior_mesh_points, int_mesh[\"x\"]), axis=1)\n interior_mesh_panels = np.concatenate((interior_mesh_panels, int_mesh[\"p\"]+mesh.n_points+interior_n_points), axis=1)\n interior_c_panels = np.concatenate((interior_c_panels, c*np.ones(int_mesh[\"n_panels\"])), axis=0)\n interior_n_points += int_mesh[\"n_points\"]\n interior_n_panels += int_mesh[\"n_panels\"]\n\n for i in range(m, m+n):\n mesh.p[:, n_panels+i-m] = np.array(mesh_arr[i + 1, 0:4]) - 1\n for j in range(4):\n mesh.p[j, n_panels + i-m] += n_points\n mesh.c_panel[n_panels+i-m] = c\n\n n_points += m\n n_panels += n\n mesh.last_panel[c] = n_panels\n\n if remove_irregular_frequencies:\n # If we have to remove frequencies, then we need to extend the mesh so\n # that it contains the panels of the free surface too\n mesh_interior = TMesh(n_points=n_points +interior_n_points , n_panels=n_panels + interior_n_panels, n_bodies=n_bodies)\n mesh_interior.x[:, 0:n_points] = mesh.x\n mesh_interior.x[:, n_points:] = interior_mesh_points\n mesh_interior.p[:, 0:n_panels] = mesh.p\n mesh_interior.p[:, n_panels:] = interior_mesh_panels\n mesh_interior.last_panel = mesh.last_panel\n mesh_interior.c_panel[0:n_panels] = mesh.c_panel\n mesh_interior.c_panel[n_panels: ] = interior_c_panels\n mesh_interior.i_sym = mesh.i_sym\n mesh = mesh_interior\n\n\n is_interior_domain = np.zeros((n_panels + interior_n_panels))\n is_interior_domain[n_panels:] = 1\n\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_IS_INTERIOR_DOMAIN, is_interior_domain.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_IS_INTERIOR_DOMAIN_ATTR)\n dset[:] = is_interior_domain\n\n n_panels += interior_n_panels\n n_points += interior_n_points\n\n\n\n\n for i in range(mesh.n_panels):\n u = mesh.x[:, mesh.p[1, i]] - mesh.x[:, mesh.p[0, i]]\n v = mesh.x[:, mesh.p[3, i]] - mesh.x[:, mesh.p[1, i]]\n w1 = np.cross(u, v)\n a1 = 0.5*np.linalg.norm(w1)\n\n u = mesh.x[:, mesh.p[3, i]] - mesh.x[:, mesh.p[2, i]]\n v = mesh.x[:, mesh.p[1, i]] - mesh.x[:, mesh.p[2, i]]\n w2 = np.cross(u, v)\n a2 = 0.5*np.linalg.norm(w2)\n\n mesh.a[i]= a1+a2\n\n if mesh.a[i] < utility.EPS:\n print('Error: surface of panel ' + str(i) + ' is too small (' + str(mesh.a[i]) + ')')\n sys.exit()\n\n mesh.xm[:, i] = (1./3)*(mesh.x[:, mesh.p[0, i]] + mesh.x[:, mesh.p[1, i]] + mesh.x[:, mesh.p[3, i]])*a1/mesh.a[i]\n\n mesh.xm[:, i] += (1./3)*(mesh.x[:, mesh.p[1, i]] + mesh.x[:, mesh.p[2, i]] + mesh.x[:, mesh.p[3, i]])*a2/mesh.a[i]\n\n u = w1 + w2\n\n mesh.n[:, i] = u/np.linalg.norm(u)\n\n return mesh\n\n\ndef generate_mesh(raw_points):\n \"\"\"\n Given a list of points corresponding to the discretization of the body domain,\n determine the points belonging to the plan where the body touches the water (or \n free surface); then use the free surface to generate triangle meshing.\n\n Args:\n raw_points: The 2D array containing the list of points of shape\n (n_points, 3)\n\n Return:\n A dictionary containing the points and triangles panel.\n \"\"\"\n # Get points in the waterline plane. The water plane is z=0 and we allow a tolerance of 1e-3\n points = raw_points[np.abs(raw_points[:, 2]) < 1e-3]\n\n # Generate a triangle mesh from the waterline segments such that each triangle angle is not\n # too small\n tri_mesh = Delaunay(points[:, 0:2])\n\n n_panels = tri_mesh.simplices.shape[0]\n\n # Get the points of the interior of the free surface\n x = points[:, :]\n\n x[:, 2] = 0\n\n # Get the meshing connectivity\n p = np.zeros((n_panels, 4))\n\n p[:, 0:3] = tri_mesh.simplices\n\n p[:, 3] = tri_mesh.simplices[:, 0]\n\n return {\"n_points\" : x.shape[0],\n \"n_panels\": n_panels,\n \"x\": x.transpose(),\n \"p\": p.transpose()\n }\n\n\n \n\n\ndef write_mesh_l12(mesh, hdf5_data):\n \"\"\"\n Write the l12 data to hdf5 from the mesh\n Args:\n mesh: object, the mesh\n hdf5_data: object, the hdf5 opened file\n \"\"\"\n dset = utility.require_dataset(hdf5_data, structure.H5_L12_COUNT, (2, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_L12_COUNT_ATTR)\n dset[0] = 2\n dset[1] = int(mesh.i_sym)\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L12_X, mesh.x.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_L12_X_ATTR)\n dset[:, :] = mesh.x\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L12_P, mesh.p.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_L12_P_ATTR)\n dset[:, :] = mesh.p + 1\n\n\ndef write_mesh_l10(mesh, hdf5_data):\n \"\"\"\n Write the l10 data to hdf5 from the mesh\n Args:\n mesh: object, the mesh\n hdf5_data: object, the hdf5 opened file\n \"\"\"\n dset = utility.require_dataset(hdf5_data, structure.H5_L10_COUNT, (4, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_L10_COUNT_ATTR)\n\n dset[0] = mesh.i_sym\n dset[1] = mesh.n_points\n dset[2] = mesh.n_panels\n dset[3] = mesh.n_bodies\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L10_CPANEL, mesh.c_panel.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_L10_CPANEL_ATTR)\n dset[:] = mesh.c_panel + 1\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L10_XM, mesh.xm.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_L10_XM_ATTR)\n dset[:, :] = mesh.xm\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L10_N, mesh.n.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_L10_N_ATTR)\n dset[:, :] = mesh.n\n\n dset = utility.require_dataset(hdf5_data, structure.H5_L10_A, mesh.a.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_L10_A_ATTR)\n dset[:] = mesh.a\n\n\ndef write_mesh_tec(mesh, mesh_tec_file):\n \"\"\"\n Export the mesh to tec file\n Args:\n mesh: object, the mesh\n mesh_tec_file: string, the path to the mesh tec file to save\n \"\"\"\n utility.mkdir_p(os.path.abspath(os.path.dirname(mesh_tec_file)))\n with open(mesh_tec_file, 'w') as inp:\n inp.write('VARIABLES=\"X\" \"Y\" \"Z\" \"NX\" \"NY\" \"NZ\" \"A\"\\n')\n inp.write('ZONE N=\\t' + str(mesh.n_points) + '\\t, E=\\t' + str(mesh.n_panels) + '\\t, F=FEPOINT,ET=QUADRILATERAL\\n')\n for i in range(mesh.n_points):\n s = str(mesh.x[0, i]) + '\\t' + str(mesh.x[1, i]) + '\\t' + str(mesh.x[2, i]) + '\\t0.\\t0.\\t0.\\t0.\\n'\n inp.write(s)\n\n for i in range(mesh.n_panels):\n s = str(mesh.p[0, i] + 1) + '\\t' + str(mesh.p[1, i] + 1) + '\\t' + str(mesh.p[2, i] + 1) + '\\t' + str(mesh.p[3, i] + 1) + '\\n'\n inp.write(s)\n\n inp.write('ZONE t=\"normales\", F=POINT, I=\\t' + str(mesh.n_panels) + '\\n')\n\n for i in range(mesh.n_panels):\n s = str(mesh.xm[0, i]) + '\\t' + str(mesh.xm[1, i]) + '\\t' + str(mesh.xm[2, i]) + '\\t'\n s += str(mesh.n[0, i]) + '\\t' + str(mesh.n[1, i]) + '\\t' + str(mesh.n[2, i]) + '\\t'\n s += str(mesh.a[i]) + '\\n'\n inp.write(s)\n\n\ndef write_fk_force_tec(int_case, fk_force, w, beta, filename):\n \"\"\"\n Writes the froude krylov forces to .tec format\n Args:\n int_case: 1D array, the integration cases\n fk_forces: 3D array, the froudkrylov forces\n w: 1D array, represents the wave frequencies omega\n beta: 1D array, represents the wave directions beta\n filename: string, the path to the file where to save the forces\n \"\"\"\n utility.mkdir_p(os.path.abspath(os.path.dirname(filename)))\n n_integration = len(int_case)\n n_beta = len(beta)\n n_w = len(w)\n with open(filename, 'w') as inp:\n inp.write('VARIABLES=\"w (rad/s)\"\\n')\n for k in range(n_integration):\n s = '\"abs(F\\t' + str(int_case[k].body + 1) + '\\t' + str(k+1) + ')\" \"angle(F\\t'\n s += str(int_case[k].body + 1) + '\\t' + str(k+1) + ')\"\\n'\n inp.write(s)\n\n for c in range(n_beta):\n inp.write('Zone t=\"FKforce - beta =\\t' + str(beta[c]*180./np.pi) + '\",I=\\t' + str(n_w) + ',F=POINT\\n')\n for i in range(n_w):\n s = str(w[i]) + '\\t'\n for k in range(n_integration):\n val = str(np.arctan2(np.imag(fk_force[i, c, k]), np.real(fk_force[i, c, k])))\n s += str(np.abs(fk_force[i, c, k])) + '\\t' + val + '\\t'\n inp.write(s)\n inp.write('\\n')\n\n\ndef compute_nds(mesh, c, i_case, direction, axis):\n \"\"\"\n Compute the integration nds\n Args:\n mesh: object The mesh\n c: int, the panel index\n i_case: int, the integration case\n direction 1D array of length 3: The direction (x, y or z)\n axis 1D array of length 3: The axis coordinate\n\n Returns:\n the integration array nds\n \"\"\"\n nds = np.zeros(mesh.n_panels*2**mesh.i_sym, settings.NEMOH_FLOAT)\n vel = np.copy(direction[0:3])\n if i_case == 1:\n for i in range(mesh.n_panels):\n if mesh.c_panel[i] == c:\n #vel = np.copy(direction[0:3])\n nds[i] = - mesh.a[i] * (mesh.n[0, i] *vel[0] + mesh.n[1, i] *vel[1] + mesh.n[2, i] *vel[2])\n else:\n nds[i]=0.\n\n if mesh.i_sym == 1:\n if mesh.c_panel[i] == c:\n #vel = np.copy(direction[0:3])\n nds[i+ mesh.n_panels] = -mesh.a[i]*(mesh.n[0, i]*vel[0]-mesh.n[1, i]*vel[1] + mesh.n[2, i]*vel[2])\n else:\n nds[i+ mesh.n_panels] = 0.\n\n elif i_case == 2:\n for i in range(mesh.n_panels):\n if mesh.c_panel[i] == c:\n vel[0] = direction[1]*(mesh.xm[2, i] - axis[2]) - direction[2]*(mesh.xm[1, i] - axis[1])\n vel[1] = direction[2]*(mesh.xm[0, i] - axis[0]) - direction[0]*(mesh.xm[2, i] - axis[2])\n vel[2] = direction[0]*(mesh.xm[1, i] - axis[1]) - direction[1]*(mesh.xm[0, i] - axis[0])\n nds[i] = - mesh.a[i] * (mesh.n[0, i] *vel[0] + mesh.n[1, i] *vel[1] + mesh.n[2, i] *vel[2])\n else:\n nds[i]=0.\n\n if mesh.i_sym == 1:\n if mesh.c_panel[i] == c:\n vel[0] = direction[1]*(mesh.xm[2, i] - axis[2]) - direction[2]*(-mesh.xm[1, i] - axis[1])\n vel[1] = direction[2]*(mesh.xm[0, i] - axis[0]) - direction[0]*(mesh.xm[2, i] - axis[2])\n vel[2] = direction[0]*(-mesh.xm[1, i] - axis[1]) - direction[1]*(mesh.xm[0, i] - axis[0])\n nds[i+ mesh.n_panels] = -mesh.a[i]*(mesh.n[0, i]*vel[0] - mesh.n[1, i]*vel[1] + mesh.n[2, i]*vel[2])\n else:\n nds[i+ mesh.n_panels] = 0.\n\n elif i_case == 3:\n print('Error: force case 3 not implemented yet')\n sys.exit()\n else:\n print('Error: unknown radiation case')\n sys.exit()\n\n return nds\n\n\ndef compute_radiation_condition(mesh, c, i_case, direction, axis):\n \"\"\"\n Compute the radiation condition\n Args:\n mesh: object The mesh\n c: int, the panel index\n i_case: int, the integration case\n direction 1D array of length 3: The direction (x, y or z)\n axis 1D array of length 3: The axis coordinate\n\n Returns:\n the radiation condition array n_vel\n \"\"\"\n n_vel = np.zeros(mesh.n_panels*2**mesh.i_sym, settings.NEMOH_COMPLEX)\n vel = np.copy(direction[0:3])\n if i_case == 1:\n for i in range(mesh.n_panels):\n if mesh.c_panel[i] == c:\n vel = np.copy(direction[0:3])\n n_vel[i] = complex(np.sum(mesh.n[:, i].flatten()*vel.flatten()), 0)\n else:\n n_vel[i] = complex(0, 0)\n\n if mesh.i_sym == 1:\n if mesh.c_panel[i] == c:\n vel = np.copy(direction[0:3])\n #nn = mesh.n[:, i]\n #nn[1] *= -1\n #n_vel[i + mesh.n_panels] = complex(np.sum(nn.flatten()*vel.flatten()), 0)\n n_vel[i + mesh.n_panels] = complex(mesh.n[0,i]*vel[0]-mesh.n[1,i]*vel[1]+ mesh.n[2,i]*vel[2], 0)\n else:\n n_vel[i+ mesh.n_panels] = complex(0, 0)\n\n elif i_case == 2:\n for i in range(mesh.n_panels):\n if mesh.c_panel[i] == c:\n vel[0] = direction[1]*(mesh.xm[2, i] - axis[2]) - direction[2]*(mesh.xm[1, i] - axis[1])\n vel[1] = direction[2]*(mesh.xm[0, i] - axis[0]) - direction[0]*(mesh.xm[2, i] - axis[2])\n vel[2] = direction[0]*(mesh.xm[1, i] - axis[1]) - direction[1]*(mesh.xm[0, i] - axis[0])\n n_vel[i] = complex(np.sum(mesh.n[:, i].flatten()*vel.flatten()), 0)\n else:\n n_vel[i] = complex(0, 0)\n\n if mesh.i_sym == 1:\n if mesh.c_panel[i] == c:\n vel[0] = direction[1]*(mesh.xm[2, i] - axis[2]) - direction[2]*(-mesh.xm[1, i] - axis[1])\n vel[1] = direction[2]*(mesh.xm[0, i] - axis[0]) - direction[0]*(mesh.xm[2, i] - axis[2])\n vel[2] = direction[0]*(-mesh.xm[1, i] - axis[1]) - direction[1]*(mesh.xm[0, i] - axis[0])\n #nn = mesh.n[:, i]\n #nn[1] *= -1\n #n_vel[i+ mesh.n_panels] = complex(np.sum(nn.flatten()*vel.flatten()), 0)\n n_vel[i + mesh.n_panels] = complex(mesh.n[0,i]*vel[0]-mesh.n[1,i]*vel[1]+ mesh.n[2,i]*vel[2], 0)\n else:\n n_vel[i + mesh.n_panels] = complex(0, 0)\n\n elif i_case == 3:\n print('Error: radiation case 3 not implemented yet')\n sys.exit()\n else:\n print('Error: unknown radiation case')\n sys.exit()\n\n return n_vel\n\n\ndef compute_one_wave(k, w, beta, wt, environment):\n \"\"\"\n Calculate the complex potential, pressure and fluid velocities for a regular wave eta=sin(k*wbar-wt)\n Args:\n k: float, the wave number\n w: float, the wave frequency\n beta: float, the wave direction\n wt: 1D array of length 3, the wave position\n environment: object, the environment\n Returns\n A dictionary containing the potential, pressure and fluid velocities\n \"\"\"\n\n x = wt[0]\n y = wt[1]\n z = wt[2]\n\n w_bar = (x-environment.x_eff)*np.cos(beta)+(y-environment.y_eff)*np.sin(beta)\n phi = -environment.g/w*cih(k, z, environment.depth)*np.exp(utility.II*k*w_bar)\n p = -environment.rho*environment.g*utility.II*cih(k, z, environment.depth)*np.exp(utility.II*k*w_bar)\n vx = -environment.g/w*utility.II*k*np.cos(beta)*cih(k, z, environment.depth)*np.exp(utility.II*k*w_bar)\n vy = -environment.g/w*utility.II*k*np.sin(beta)*cih(k, z, environment.depth)*np.exp(utility.II*k*w_bar)\n vz = -environment.g/w*k*sih(k, z, environment.depth)*np.exp(utility.II*k*w_bar)\n\n return {\"phi\": phi, \"p\": p, \"vx\": vx, \"vy\": vy, \"vz\": vz, \"v\": np.array([vx, vy, vz])}\n\n\ndef compute_wave(mesh, w, beta, environment):\n \"\"\"\n Calculate the array of complex potential, pressure and fluid velocities for a wave\n Args:\n mesh: object, the mesh\n w: float, the wave frequency\n beta: float, the wave direction\n environment: object, the environment\n Returns\n A dictionary containing the pressure and fluid velocities\n \"\"\"\n\n n_vel = np.zeros(mesh.n_panels*2**mesh.i_sym, settings.NEMOH_COMPLEX)\n pressure = np.zeros(mesh.n_panels*2**mesh.i_sym, settings.NEMOH_COMPLEX)\n\n k_wave = utility.compute_wave_number(w, environment)\n\n for i in range(2**mesh.i_sym*mesh.n_panels):\n if i < mesh.n_panels:\n wbar = (mesh.xm[0, i] - environment.x_eff)*np.cos(beta) + (mesh.xm[1, i] - environment.y_eff)*np.sin(beta)\n\n pressure[i] = -environment.g/w*np.exp(utility.II*k_wave*wbar)\n\n n_vel[i] = pressure[i]*(utility.II*k_wave*(np.cos(beta)*mesh.n[0,i]+ \\\n np.sin(beta)*mesh.n[1,i])*cih(k_wave,mesh.xm[2, i], environment.depth)+ \\\n k_wave*mesh.n[2,i]*sih(k_wave,mesh.xm[2,i],environment.depth))\n pressure[i] *= cih(k_wave,mesh.xm[2,i], environment.depth)\n one_wave = compute_one_wave(k_wave, w, beta, mesh.xm[0:3, i], environment)\n # This makes previous pressure[i] statement useless\n pressure[i] = one_wave[\"p\"]\n n_vel[i] = np.sum(one_wave[\"v\"].flatten()*mesh.n[:, i].flatten())\n\n else:\n wbar=(mesh.xm[0, i-mesh.n_panels]-environment.x_eff)*np.cos(beta)+ \\\n (-mesh.xm[1, i-mesh.n_panels]-environment.y_eff)*np.sin(beta)\n pressure[i] = -environment.g/w*np.exp(utility.II*k_wave*wbar)\n n_vel[i] = pressure[i]*(utility.II*k_wave*(np.cos(beta)*mesh.n[0,i-mesh.n_panels]+\\\n np.sin(beta)*(-1.*mesh.n[1,i-mesh.n_panels]))*cih(k_wave, mesh.xm[2, i-mesh.n_panels],\\\n environment.depth)+k_wave*mesh.n[2,i-mesh.n_panels]*sih(k_wave,mesh.xm[2,i-mesh.n_panels],\\\n environment.depth))\n pressure[i] *= cih(k_wave, mesh.xm[2, i-mesh.n_panels], environment.depth)\n #one_wave = compute_one_wave(k_wave, w, beta, mesh.xm[0:3, i-mesh.n_panels], environment)\n #xm = mesh.xm[0:3, i-mesh.n_panels]\n #xm[1] = - xm[1]\n\n xm = np.array([mesh.xm[0, i-mesh.n_panels], -mesh.xm[1, i-mesh.n_panels], mesh.xm[2, i-mesh.n_panels]])\n one_wave = compute_one_wave(k_wave, w, beta, xm, environment)\n pressure[i] = one_wave[\"p\"]\n #one_wave[\"v\"][1] *= -1\n #n_vel[i] = np.sum(one_wave[\"v\"]*mesh.n[:, i-mesh.n_panels])\n vx = one_wave[\"v\"][0]\n vy = one_wave[\"v\"][1]\n vz = one_wave[\"v\"][2]\n n_vel[i] = vx*mesh.n[0, i-mesh.n_panels] - vy*mesh.n[1, i-mesh.n_panels] + vz*mesh.n[2, i-mesh.n_panels]\n\n return {\"n_vel\": n_vel, \"pressure\": pressure}\n\n\ndef run(hdf5_data, custom_config):\n \"\"\"\n This function run the preprocessor\n Args:\n hdf5_data: object, the hdf5 opened file\n custom_config, dict The custom configuration dictionary\n \"\"\"\n n_radiation = 0\n n_integration = 0\n\n bodies = hdf5_data.get(structure.H5_BODIES)\n\n if not bodies:\n print('The bodies dataset is not found. It looks like your hdf5 file is not correct. Please set ',\n 'NEMOH_CALCULATIONS_FILE and NEMOH_INPUT_FILE to a valid value prior to running the preprocessor ',\n 'Alternatively, you could manually add the input')\n sys.exit(1)\n bodies = bodies.values()\n\n for body in bodies:\n n_radiation += body.get(structure.H5_FREEDOM_DEGREE).shape[0]\n n_integration += body.get(structure.H5_GENERALISED_FORCES).shape[0]\n\n n_w = hdf5_data.get(structure.H5_NUM_WAVE_FREQUENCIES)[0]\n w_min = hdf5_data.get(structure.H5_MIN_WAVE_FREQUENCIES)[0]\n w_max = hdf5_data.get(structure.H5_MAX_WAVE_FREQUENCIES)[0]\n w = np.zeros(n_w, settings.NEMOH_FLOAT)\n if n_w > 1:\n for j in range(n_w):\n w[j] = w_min+(w_max-w_min)*j/(n_w-1)\n else:\n w[0] = w_min\n\n n_beta = hdf5_data.get(structure.H5_NUM_WAVE_DIRECTIONS)[0]\n beta_min = hdf5_data.get(structure.H5_MIN_WAVE_DIRECTIONS)[0]\n beta_max = hdf5_data.get(structure.H5_MAX_WAVE_DIRECTIONS)[0]\n\n beta = np.zeros(n_beta, settings.NEMOH_FLOAT)\n\n if n_beta > 1:\n for j in range(n_beta):\n beta[j] = (beta_min+(beta_max-beta_min)*j/(n_beta-1))*math.pi/180.\n else:\n beta[0] = beta_min * math.pi/180.\n\n switch_potential = hdf5_data.get(structure.H5_SHOW_PRESSURE)[0] >= 1\n n_theta = hdf5_data.get(structure.H5_KOCHIN_NUMBER)[0]\n theta_min = hdf5_data.get(structure.H5_KOCHIN_MIN)[0]\n theta_max = hdf5_data.get(structure.H5_KOCHIN_MAX)[0]\n switch_kochin = n_theta > 0\n\n n_x = hdf5_data.get(structure.H5_FREE_SURFACE_POINTS_X)[0]\n n_y = hdf5_data.get(structure.H5_FREE_SURFACE_POINTS_Y)[0]\n l_x = hdf5_data.get(structure.H5_FREE_SURFACE_DIMENSION_X)[0]\n l_y = hdf5_data.get(structure.H5_FREE_SURFACE_DIMENSION_Y)[0]\n\n switch_free_surface = n_x > 0\n\n rad_case = [TCase() for x in range(n_radiation)]\n int_case = [TCase() for x in range(n_integration)]\n j_rad = 0\n j_int = 0\n\n for c in range(len(bodies)):\n body = bodies[c]\n freedom_degree = body.get(structure.H5_FREEDOM_DEGREE)\n m = freedom_degree.len()\n for i in range(m):\n case = TCase()\n case.i_case = freedom_degree[i, 0]\n case.direction = np.array(freedom_degree[i, 1:4])\n case.axis = np.array(freedom_degree[i, 4:7])\n case.i_body = c\n case.mode = i\n rad_case[j_rad + i] = case\n j_rad += m\n\n generalised_forces = body.get(structure.H5_GENERALISED_FORCES)\n m = generalised_forces.len()\n for i in range(m):\n case = TCase()\n case.i_case = generalised_forces[i, 0]\n case.direction = np.array(generalised_forces[i, 1:4])\n case.axis = np.array(generalised_forces[i, 4:7])\n case.i_body = c\n case.mode = i\n int_case[j_int + i] = case\n\n j_int += m\n\n print('')\n print('Summary of calculation')\n\n depth = hdf5_data.get(structure.H5_ENV_DEPTH)[0]\n if depth > 0:\n print(' -> Water depth = ' + str(depth) + ' m')\n else:\n print(' -> Infinite water depth')\n\n print(' -> ' + str(n_w) + ' wave frequencies from ' + str(w[0]) + ' to ' + str(w[n_w-1]))\n print(' -> ' + str(n_beta) + str(' wave directions from ') + str(beta[0]) + ' to ' + str(beta[n_beta-1]))\n print(' -> ' + str(n_radiation) + ' radiation problems')\n print(' -> ' + str(n_integration) + ' forces')\n print('')\n\n mesh = read_mesh(hdf5_data, custom_config)\n write_mesh_l12(mesh, hdf5_data)\n write_mesh_l10(mesh, hdf5_data)\n\n mesh_tec_file = utility.get_setting(settings.MESH_TEC_FILE, custom_config, 'MESH_TEC_FILE')\n\n if mesh_tec_file:\n write_mesh_tec(mesh, mesh_tec_file)\n\n fnds = np.zeros((n_integration, mesh.n_panels*2**mesh.i_sym), settings.NEMOH_FLOAT)\n\n for j in range(n_integration):\n fnds[j, :] = compute_nds(mesh, int_case[j].body, int_case[j].i_case, int_case[j].direction, int_case[j].axis)\n\n dset = utility.require_dataset(hdf5_data, structure.H5_MESH_INTEGRATION, fnds.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_MESH_INTEGRATION_ATTR)\n dset[:, :] = fnds\n\n environment = utility.read_environment(hdf5_data)\n\n normal_velocity = np.zeros((mesh.n_panels*2**mesh.i_sym, (n_beta+n_radiation)*n_w), settings.NEMOH_COMPLEX)\n fk_force = np.zeros((n_w, n_beta, n_integration), settings.NEMOH_COMPLEX)\n\n for i in range(n_w):\n for j in range(n_beta):\n\n result = compute_wave(mesh, w[i], beta[j], environment)\n pressure = result[\"pressure\"]\n n_vel = result[\"n_vel\"]\n normal_velocity[:, j+ i*(n_beta+n_radiation)] = n_vel\n # Calculate the corresponding FK forces\n for k in range(n_integration):\n #for c in range(mesh.n_panels*2**mesh.i_sym):\n #fk_force[i, j, k] += pressure[c]*fnds[k, c]\n\n fk_force[i, j, k] = np.sum(pressure.flatten()*fnds[k, :].flatten())\n\n for j in range(n_radiation):\n n_vel = compute_radiation_condition(mesh, rad_case[j].body, rad_case[j].i_case, rad_case[j].direction,\n rad_case[j].axis)\n\n normal_velocity[:, j + n_beta + i*(n_beta+n_radiation)] = n_vel\n\n # Save body conditions\n n_problems = n_w*(n_radiation+n_beta)\n bc_omega = w.repeat(n_beta + n_radiation)\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_W, bc_omega.shape, dtype='f', maxshape=(None))\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_W_ATTR)\n dset[:] = bc_omega\n\n bc_switch_type = -np.ones(n_problems, dtype='f')\n bc_switch_type[0:bc_switch_type.shape[0]:n_beta + n_radiation] = beta\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_BETA, bc_switch_type.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_BETA_ATTR)\n dset[:] = bc_switch_type\n\n\n temp = int(switch_potential)*np.ones(n_problems, dtype='i')\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_SWITCH_POTENTIAL, temp.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_SWITCH_POTENTIAL_ATTR)\n dset[:] = temp\n\n temp = int(switch_free_surface)*np.ones(n_problems, dtype='i')\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_SWITCH_FREE_SURFACE, temp.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_SWITCH_FREE_SURFACE_ATTR)\n dset[:] = temp\n\n temp = int(switch_kochin)*np.ones(n_problems, dtype='i')\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_SWITCH_KOCHIN, temp.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_SWITCH_KOCHIN_ATTR)\n dset[:] = temp\n\n dset = utility.require_dataset(hdf5_data, structure.H5_NORMAL_VELOCITY_VELOCITIES, normal_velocity.shape, dtype='F')\n utility.set_hdf5_attributes(dset, structure.H5_NORMAL_VELOCITY_VELOCITIES_ATTR)\n dset[:, :] = normal_velocity\n\n\n #fk_force_f = fk_force.flatten()\n #fk_force_o = np.vstack((np.abs(fk_force_f), np.arctan2(np.imag(fk_force_f), np.real(fk_force_f)))).transpose()\n fk_force_o = np.zeros((n_integration*n_w, 2*n_beta+2*n_radiation), dtype='f')\n idx = 0\n for k in range(n_integration):\n for i in range(n_w):\n for c in range(n_beta):\n fk_force_o[idx, 2*c] = np.abs(fk_force[i, c, k])\n fk_force_o[idx, 2*c+1] = np.arctan2(np.imag(fk_force[i, c, k]), np.real(fk_force[i, c, k]))\n\n for c in range(2*n_radiation):\n fk_force_o[idx, 2*n_beta + c] = 0\n idx += 1\n\n\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_FK_FORCES, fk_force_o.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_FK_FORCES_ATTR)\n dset[:, :] = fk_force_o\n\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_FK_FORCES_RAW, fk_force.shape, dtype='F')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_FK_FORCES_RAW_ATTR)\n dset[:, :, :] = fk_force\n\n fk_force_tec_file = utility.get_setting(settings.FK_FORCE_TEC_FILE, custom_config, 'FK_FORCE_TEC_FILE')\n if fk_force_tec_file:\n write_fk_force_tec(int_case, fk_force, w, beta, fk_force_tec_file)\n\n #free_surface_v = [[-0.5*l_x+l_x*i/(n_x-1), -0.5*l_y+l_y*j/(n_y-1), 0.] for i in range(n_x) for j in range(\n # n_y)]\n free_surface_v = np.zeros((3, n_x*n_y))\n k = 0\n for i in range(n_x):\n for j in range(n_y):\n free_surface_v[0, k] = -0.5*l_x+l_x*i/(n_x-1)\n free_surface_v[1, k] = -0.5*l_y+l_y*j/(n_y-1)\n free_surface_v[2, k] = 0.\n k += 1\n\n #free_surface_v = np.array(free_surface_v)\n dset = utility.require_dataset(hdf5_data, structure.H5_MESH_FREE_SURFACE_VECTORS, free_surface_v.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_MESH_FREE_SURFACE_VECTORS_ATTR)\n dset[:, :] = free_surface_v\n\n free_surface_v = np.zeros((0, 0))\n\n if (n_x-1) > 0 and (n_y-1) >0:\n #free_surface_v = [[j+i*n_y, j+1+i*n_y, j+1+(i+1)*n_y, j+(i+1)*n_y] for i in range(n_x-1) for j in\n #range(n_y-1)]\n free_surface_v = np.zeros((4, (n_x-1)*(n_y-1)))\n k = 0\n for i in range(n_x-1):\n for j in range(n_y-1):\n free_surface_v[0, k] = j+i*n_y\n free_surface_v[1, k] = j+1+i*n_y\n free_surface_v[2, k] = j+1+(i+1)*n_y\n free_surface_v[3, k] = j+(i+1)*n_y\n k += 1\n #free_surface_v = np.array(free_surface_v)\n dset = utility.require_dataset(hdf5_data, structure.H5_MESH_FREE_SURFACE_INDEX, free_surface_v.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_MESH_FREE_SURFACE_INDEX_ATTR)\n dset[:, :] = free_surface_v\n\n\n # Generate Kochin\n kochin = np.array([])\n if n_theta > 0:\n if n_theta > 1:\n kochin = [(theta_min+(theta_max-theta_min)*j/(n_theta-1))*np.pi/180. for j in range(n_theta)]\n else:\n kochin = [theta_min*np.pi/180.]\n\n\n kochin = np.array(kochin)\n dset = utility.require_dataset(hdf5_data, structure.H5_MESH_KOCHIN, kochin.shape, dtype='f', maxshape=(None, ))\n utility.set_hdf5_attributes(dset, structure.H5_MESH_KOCHIN_ATTR)\n dset[:] = kochin\n\n # Save index of cases\n\n out = np.array([[k+1, int_case[k].body+1, int_case[k].mode+1] for k in range(n_integration)])\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_FORCE, out.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_FORCE_ATTR)\n dset[:, :] = out\n\n out = np.array([[k+1, rad_case[k].body+1, rad_case[k].mode+1] for k in range(n_radiation)])\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_MOTION, out.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_MOTION_ATTR)\n dset[:, :] = out\n\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_BETA, beta.shape, dtype='f', maxshape=(None))\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_BETA_ATTR)\n dset[:] = beta\n\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_W, w.shape, dtype='f', maxshape=(None))\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_W_ATTR)\n dset[:] = w\n\n out = np.array([(theta_min+(theta_max-theta_min)*k/(n_theta-1))*np.pi/180. for k in range(n_theta)])\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_THETA, out.shape, dtype='f', maxshape=(None))\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_THETA_ATTR)\n dset[:] = out\n\n # Save radiation cases\n\n out = np.array([[rad_case[k].body+1, rad_case[k].i_case+1, rad_case[k].direction[0], rad_case[k].direction[1], rad_case[k].direction[2], rad_case[k].axis[0], rad_case[k].axis[1] , rad_case[k].axis[2]] for k in range(n_radiation)])\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_RADIATION, out.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_RADIATION_ATTR)\n dset[:, :] = out\n\n dset = utility.require_dataset(hdf5_data, structure.H5_RESULTS_CASE_BETA, beta.shape, dtype='f')\n utility.set_hdf5_attributes(dset, structure.H5_RESULTS_CASE_BETA_ATTR)\n dset[:] = beta\n\n switch_ode_influence = utility.get_setting(settings.USE_ODE_INFLUENCE_COEFFICIENTS, custom_config,\n 'USE_ODE_INFLUENCE_COEFFICIENTS')\n\n use_higher_order = utility.get_setting(settings.USE_HIGHER_ORDER, custom_config,\n 'USE_HIGHER_ORDER')\n\n num_panel_higher_order = utility.get_setting(settings.NUM_PANEL_HIGHER_ORDER, custom_config,\n 'NUM_PANEL_HIGHER_ORDER')\n\n b_spline_order = utility.get_setting(settings.B_SPLINE_ORDER, custom_config,\n 'B_SPLINE_ORDER')\n\n use_dipoles_implementation = utility.get_setting(settings.USE_DIPOLES_IMPLEMENTATION, custom_config,\n 'USE_DIPOLES_IMPLEMENTATION')\n\n compute_yaw_moment = utility.get_setting(settings.COMPUTE_YAW_MOMENT, custom_config,\n 'COMPUTE_YAW_MOMENT')\n\n compute_drift_forces = utility.get_setting(settings.COMPUTE_DRIFT_FORCES, custom_config,\n 'COMPUTE_DRIFT_FORCES')\n\n thin_panels = utility.get_setting(settings.THIN_PANELS, custom_config,\n 'THIN_PANELS')\n\n if num_panel_higher_order is not None and num_panel_higher_order > 0:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_NUM_PANEL_HIGHER_ORDER, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_NUM_PANEL_HIGHER_ORDER_ATTR)\n dset[:] = int(num_panel_higher_order)\n\n if b_spline_order is not None and b_spline_order > 0:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_B_SPLINE_ORDER, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_B_SPLINE_ORDER_ATTR)\n dset[:] = int(b_spline_order)\n\n if use_higher_order is not None:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_USE_HIGHER_ORDER, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_USE_HIGHER_ORDER_ATTR)\n dset[:] = int(use_higher_order)\n\n\n if switch_ode_influence is not None:\n temp = int(switch_ode_influence)*np.ones(n_problems, dtype='i')\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_SWITCH_ODE_INFLUENCE, temp.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_SWITCH_ODE_INFLUENCE_ATTR)\n dset[:] = temp\n\n if use_dipoles_implementation is not None:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_USE_DIPOLES_IMPLEMENTATION, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_USE_DIPOLES_IMPLEMENTATION_ATTR)\n dset[:] = int(use_dipoles_implementation)\n\n if compute_yaw_moment is not None:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_COMPUTE_YAW_MOMENT, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_COMPUTE_YAW_MOMENT_ATTR)\n dset[:] = int(compute_yaw_moment)\n\n if compute_drift_forces is not None:\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_COMPUTE_DRIFT_FORCES, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_COMPUTE_DRIFT_FORCES_ATTR)\n dset[:] = int(compute_drift_forces)\n\n if thin_panels is not None:\n temp = np.zeros(mesh.n_panels, dtype='i')\n for idx in thin_panels:\n if idx == -1:\n temp = np.ones(mesh.n_panels, dtype='i')\n break\n elif idx >= 0:\n temp[idx] = 1\n dset = utility.require_dataset(hdf5_data, structure.H5_SOLVER_THIN_PANELS, temp.shape, dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_THIN_PANELS_ATTR)\n dset[:] = temp\n\n\ndef preprocess(custom_config):\n \"\"\"\n Configure and then run the preprocessor\n\n Args:\n custom_config, dict The custom configuration dictionary\n \"\"\"\n\n if not custom_config:\n custom_config = {}\n\n hdf5_file = utility.get_setting(settings.HDF5_FILE, custom_config, 'HDF5_FILE')\n nemoh_cal = utility.get_setting(settings.NEMOH_CALCULATIONS_FILE, custom_config, 'NEMOH_CALCULATIONS_FILE')\n input_file = utility.get_setting(settings.NEMOH_INPUT_FILE, custom_config, 'NEMOH_INPUT_FILE')\n utility.validate_string(hdf5_file, 'HDF5_FILE')\n if not nemoh_cal and not input_file:\n utility.validate_file(hdf5_file, 'HDF5_FILE')\n\n utility.mkdir_p(os.path.abspath(os.path.dirname(hdf5_file)))\n\n with h5py.File(hdf5_file, \"a\") as hdf5_db:\n if nemoh_cal:\n utility.convert_calculations(nemoh_cal, hdf5_db)\n\n if input_file:\n utility.convert_input(input_file, hdf5_db)\n\n remove_irregular_frequencies = utility.get_setting(settings.REMOVE_IRREGULAR_FREQUENCIES, custom_config,\n 'REMOVE_IRREGULAR_FREQUENCIES')\n if remove_irregular_frequencies is not None:\n dset = utility.require_dataset(hdf5_db, structure.H5_SOLVER_REMOVE_IRREGULAR_FREQUENCIES, (1, ), dtype='i')\n utility.set_hdf5_attributes(dset, structure.H5_SOLVER_REMOVE_IRREGULAR_FREQUENCIES_ATTR)\n dset[:] = int(remove_irregular_frequencies)\n else:\n settings.REMOVE_IRREGULAR_FREQUENCIES = hdf5_db.get(structure.H5_SOLVER_REMOVE_IRREGULAR_FREQUENCIES)[0]\n\n\n\n run(hdf5_db, custom_config)\n\n\nif __name__ == '__main__':\n\n preprocess({})\n\n\n","sub_path":"source/NemohImproved/NemohPython/nemoh/preprocessor.py","file_name":"preprocessor.py","file_ext":"py","file_size_in_byte":39742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"35180105","text":"import giffer\nimport counter\n\n\nclass Main:\n def __init__(self):\n self.giffer = giffer.Giffer()\n\n def main(self, name, countdown):\n numbers = counter.Counter(1, name, countdown)\n frames = self.giffer.create_frames(400, 200, numbers.minutes)\n self.giffer.save_gif(name, frames)\n\n\nif __name__ == \"__main__\":\n main = Main()\n main.main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"221734234","text":"n_l = 5\nn_short = names[:5]\nsamples = df.ix[:,n_short].sample(replace=False, frac=0.2)\nsamples = samples.reset_index()\n#samples = samples.drop('index',axis=1)\nss = samples.ix[:,1:]\n\nfor xx in ss.columns:\n print(xx)\n ss.ix[:,xx] = pd.to_numeric(ss.ix[:,xx])\n\naxs = pd.tools.plotting.scatter_matrix(ss,alpha=0.2, diagonal ='kde')\n#samples = samples.reset_index()\n#df_corr = samples.corr().as_matrix()\n\n#for i,j in zip(*plt.np.triu_indices_from(axs,k=1)):\n# axs[i,j].annotate( \"%.3f\" %df_corr[i,j], (0.8, 0.8), xycords = 'axes fraction', ha = 'center', va='center')\n'''\nn = len(samples.columns)\nfor i in range(n):\n v = axs[i,0]\n v.yaxis.label.set_rotation(0)\n v.yaxis.label.set_ha('right')\n v.set_yticks(())\n h = axs[n-1,i]\n h.xaxis.label.set_rotation(90)\n h.set_xticks(())\n'''\nplt.show()\n\n\n","sub_path":"code/toolset/test/corr.py","file_name":"corr.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"512404908","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#Python2.7 support\n\nimport os\nimport sys\nimport Tkinter\nimport ImageTk\nimport qrcode\nimport Image\nimport ConfigParser\n\nif len(sys.argv) == 1:\n if os.path.isfile('CreateQR.ini') == False:\n print('Configuration file not exist!')\n sys.exit(1)\n\n else:\n conf = ConfigParser.ConfigParser()\n conf.read('CreateQR.ini')\n logfile = conf.get('Config','logfile')\n imgsize = conf.getint('Config','imgsize')\n imgadd = conf.get('Config','imgadd')\n\n #get QR message and generate image\n if os.path.isfile(logfile) == True:\n f = open(logfile)\n log = f.readlines(80)\n else:\n print('Result file not exist, please check configuration file!')\n sys.exit(2)\n\nelse:\n log = sys.argv[1]\n imgsize = 200\n if len(sys.argv) >= 3:\n imgsize = int(sys.argv[2])\n \n#change size and output image\nimg = qrcode.make(''.join(log))\n(x,y) = img.size\nimg = img.resize((imgsize,imgsize), Image.ANTIALIAS)\n\n#create window\nuisize = str(imgsize)+\"x\"+str(imgsize)\n\nroot = Tkinter.Tk()\n#set window top\nroot.wm_attributes('-topmost',1)\n#hide the title bar\nroot.overrideredirect(True)\n\nimglb = ImageTk.PhotoImage(img)\nTkinter.Label(root, image=imglb).pack(side=\"top\")\n\nroot.geometry(uisize+\"+10+10\")\n\nroot.mainloop()\n\nsys.exit(0)\n","sub_path":"CreateQR-1.1.py","file_name":"CreateQR-1.1.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"64262386","text":"import numpy as np\nimport logbook\nimport pandas as pd\n\nfrom zipline.lib.adjusted_array import AdjustedArray\nfrom zipline.pipeline.loaders.base import PipelineLoader\nfrom zipline.utils.calendars import get_calendar\nfrom zipline.errors import NoFurtherDataError\n\nfrom pipeline_live.data.sources import alpaca\n\n\nlog = logbook.Logger(__name__)\n\n\nclass USEquityPricingLoader(PipelineLoader):\n \"\"\"\n PipelineLoader for US Equity Pricing data\n \"\"\"\n\n def __init__(self):\n cal = get_calendar('NYSE')\n\n self._all_sessions = cal.all_sessions\n\n def load_adjusted_array(self, columns, dates, symbols, mask):\n # load_adjusted_array is called with dates on which the user's algo\n # will be shown data, which means we need to return the data that would\n # be known at the start of each date. We assume that the latest data\n # known on day N is the data from day (N - 1), so we shift all query\n # dates back by a day.\n start_date, end_date = _shift_dates(\n self._all_sessions, dates[0], dates[-1], shift=1,\n )\n\n sessions = self._all_sessions\n sessions = sessions[(sessions >= start_date) & (sessions <= end_date)]\n\n timedelta = pd.Timestamp.utcnow() - start_date\n chart_range = timedelta.days + 1\n log.info('chart_range={}'.format(chart_range))\n prices = alpaca.get_stockprices(chart_range)\n\n dfs = []\n for symbol in symbols:\n if symbol not in prices:\n df = pd.DataFrame(\n {c.name: c.missing_value for c in columns},\n index=sessions\n )\n else:\n df = prices[symbol]\n df = df.reindex(sessions, method='ffill')\n dfs.append(df)\n\n raw_arrays = {}\n for c in columns:\n colname = c.name\n parsed_values = []\n for df in dfs:\n if not df.empty:\n value = df[colname].values\n else:\n value = np.empty(shape=(len(sessions)))\n value.fill(np.nan)\n\n parsed_values.append(value)\n\n raw_arrays[colname] = np.stack(\n parsed_values,\n axis=-1\n )\n out = {}\n for c in columns:\n c_raw = raw_arrays[c.name]\n out[c] = AdjustedArray(\n c_raw.astype(c.dtype),\n {},\n c.missing_value\n )\n return out\n\n\ndef _shift_dates(dates, start_date, end_date, shift):\n try:\n start = dates.get_loc(start_date)\n except KeyError:\n if start_date < dates[0]:\n raise NoFurtherDataError(\n msg=(\n \"Pipeline Query requested data starting on {query_start}, \"\n \"but first known date is {calendar_start}\"\n ).format(\n query_start=str(start_date),\n calendar_start=str(dates[0]),\n )\n )\n else:\n raise ValueError(\"Query start %s not in calendar\" % start_date)\n\n # Make sure that shifting doesn't push us out of the calendar.\n if start < shift:\n raise NoFurtherDataError(\n msg=(\n \"Pipeline Query requested data from {shift}\"\n \" days before {query_start}, but first known date is only \"\n \"{start} days earlier.\"\n ).format(shift=shift, query_start=start_date, start=start),\n )\n\n try:\n end = dates.get_loc(end_date)\n except KeyError:\n if end_date > dates[-1]:\n raise NoFurtherDataError(\n msg=(\n \"Pipeline Query requesting data up to {query_end}, \"\n \"but last known date is {calendar_end}\"\n ).format(\n query_end=end_date,\n calendar_end=dates[-1],\n )\n )\n else:\n raise ValueError(\"Query end %s not in calendar\" % end_date)\n return dates[start - shift], dates[end - shift]\n","sub_path":"pipeline_live/data/alpaca/pricing_loader.py","file_name":"pricing_loader.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"30033235","text":"#\n# @lc app=leetcode.cn id=329 lang=python3\n#\n# [329] 矩阵中的最长递增路径\n#\n# https://leetcode-cn.com/problems/longest-increasing-path-in-a-matrix/description/\n#\n# algorithms\n# Hard (41.17%)\n# Likes: 219\n# Dislikes: 0\n# Total Accepted: 16.9K\n# Total Submissions: 40.6K\n# Testcase Example: '[[9,9,4],[6,6,8],[2,1,1]]'\n#\n# 给定一个整数矩阵，找出最长递增路径的长度。\n# \n# 对于每个单元格，你可以往上，下，左，右四个方向移动。 你不能在对角线方向上移动或移动到边界外（即不允许环绕）。\n# \n# 示例 1:\n# \n# 输入: nums = \n# [\n# ⁠ [9,9,4],\n# ⁠ [6,6,8],\n# ⁠ [2,1,1]\n# ] \n# 输出: 4 \n# 解释: 最长递增路径为 [1, 2, 6, 9]。\n# \n# 示例 2:\n# \n# 输入: nums = \n# [\n# ⁠ [3,4,5],\n# ⁠ [3,2,6],\n# ⁠ [2,2,1]\n# ] \n# 输出: 4 \n# 解释: 最长递增路径是 [3, 4, 5, 6]。注意不允许在对角线方向上移动。\n# \n# \n#\n\n# @lc code=start\nfrom functools import lru_cache\nfrom typing import List\n\n\nclass Solution:\n def longestIncreasingPath(self, matrix: List[List[int]]) -> int:\n\n if not matrix:\n return 0\n\n rs, cs = len(matrix), len(matrix[0])\n d = {(-1, 0), (1, 0), (0, -1), (0, 1)}\n\n @lru_cache(None)\n def dfs(row: int, column: int) -> int:\n b = 1\n for dx, dy in d:\n c, r = row + dx, column + dy\n if 0 <= c < rs and 0 <= r < cs and matrix[c][r] > matrix[row][column]:\n b = max(b, dfs(c, r) + 1)\n return b\n\n res = 0\n for i in range(rs):\n for j in range(cs):\n res = max(res, dfs(i, j))\n\n return res\n\n# @lc code=end\n","sub_path":"hard/329.矩阵中的最长递增路径.py","file_name":"329.矩阵中的最长递增路径.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"400134241","text":"# -*- coding: utf-8 -*-\n\n\nCOLORES = {\n \"NEGRO\": 0,\n \"MARRON\": 1,\n \"ROJO\": 2,\n \"NARANJA\": 3,\n \"AMARILLO\": 4,\n \"VERDE\": 5,\n \"AZUL\": 6,\n \"VIOLETA\": 7,\n \"GRIS\": 8,\n \"BLANCO\": 9,\n \"DORADO\": -1,\n \"PLATEADO\": -2\n}\n\n\nTOLERANCIAS = {\n \"DORADO\": 5,\n \"PLATEADO\": 10\n}\n\n\nPPM = {\n \"MARRON\": 100,\n \"ROJO\": 50,\n \"NARANJA\": 15,\n \"AMARILLO\": 25,\n \"AZUL\": 10,\n \"VIOLETA\": 5,\n \"BLANCO\": 1,\n}\n\n\ndef menu():\n print(\"*************************\")\n print(\"1 - Resistencias 4 bandas\")\n print(\"2 - Resistencias 5 bandas\")\n print(\"3 - Resistencias 6 bandas\")\n print(\"*************************\")\n\n\ndef cuatroBandas():\n c1 = raw_input(\"Primer color: \")\n c2 = raw_input(\"Segundo color: \")\n c3 = raw_input(\"Tercer color: \")\n t = raw_input(\"Tolerancia: \")\n\n valor = (10*COLORES[c1] + COLORES[c2])*(10**COLORES[c3])\n\n if valor >= 10**6:\n valor /= 10.0**6\n print(\"==> Resistencia = {0}MΩ {1}%\".format(valor, TOLERANCIAS[t]))\n elif valor >= 10**3:\n valor /= 10.0**3\n print(\"==> Resistencia = {0}KΩ {1}%\".format(valor, TOLERANCIAS[t]))\n else:\n print(\"==> Resistencia = {0}Ω {1}%\".format(valor, TOLERANCIAS[t]))\n\n\ndef cincoBandas():\n c1 = raw_input(\"Primer color: \")\n c2 = raw_input(\"Segundo color: \")\n c3 = raw_input(\"Tercer color: \")\n c4 = raw_input(\"Cuarto color: \")\n t = raw_input(\"Tolerancia: \")\n\n valor = (100*COLORES[c1] + 10*COLORES[c2] + COLORES[c3])*(10**COLORES[c4])\n\n if valor >= 10**6:\n valor /= 10.0**6\n print(\"==> Resistencia = {0}MΩ {1}%\".format(valor, TOLERANCIAS[t]))\n elif valor >= 10**3:\n valor /= 10.0**3\n print(\"==> Resistencia = {0}KΩ {1}%\".format(valor, TOLERANCIAS[t]))\n else:\n print(\"==> Resistencia = {0}Ω {1}%\".format(valor, TOLERANCIAS[t]))\n\n\ndef seisBandas():\n c1 = raw_input(\"Primer color: \")\n c2 = raw_input(\"Segundo color: \")\n c3 = raw_input(\"Tercer color: \")\n c4 = raw_input(\"Cuarto color: \")\n t = raw_input(\"Tolerancia: \")\n ppm = raw_input(\"PPM: \")\n\n valor = (100*COLORES[c1] + 10*COLORES[c2] + COLORES[c3])*(10**COLORES[c4])\n\n if valor >= 10**6:\n valor /= 10.0**6\n print(\"==> Resistencia = {0}MΩ {1}% {2}PPM\".format(valor, TOLERANCIAS[t], PPM[ppm]))\n elif valor >= 10**3:\n valor /= 10.0**3\n print(\"==> Resistencia = {0}KΩ {1}% {2}PPM\".format(valor, TOLERANCIAS[t], PPM[ppm]))\n else:\n print(\"==> Resistencia = {0}Ω {1}% {2}PPM\".format(valor, TOLERANCIAS[t], PPM[ppm]))\n\n\ndef main():\n menu()\n\n opcion_correcta = True\n\n while opcion_correcta:\n opcion = int(raw_input(\"Ingrese una de las opciones: \"))\n if opcion == 1:\n opcion_correcta = False\n cuatroBandas()\n elif opcion == 2:\n opcion_correcta = False\n cincoBandas()\n elif opcion == 3:\n opcion_correcta = False\n seisBandas()\n else:\n print(\"\\nOpcion no valida, porfavor ingrese una opcion valida.\")\n menu()\n\nif __name__ == '__main__':\n main()\n","sub_path":"Version_Consola/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"82068168","text":"import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use(\"ggplot\")\n\ntotal_time = 1 #year\ndt = 1e-3\ntime_steps = int(total_time/dt)\n\npositions = np.zeros((time_steps, 2))\nvelocities = np.zeros((time_steps, 2))\naccelerations = np.zeros((time_steps, 2))\n\ndef get_acceleration(t):\n GM = 4 * np.pi ** 2 # [AU^3/yr^2]\n r_vec = np.array([0, 0]) - positions[t, :]\n r = np.sqrt(r_vec[0] ** 2 + r_vec[1] ** 2)\n # r_unit_vec = r_vec/r\n acc = GM * r_vec / r ** 3\n return acc\n\ndef forward_euler():\n for t in range(time_steps-1):\n positions[t+1, :] = positions[t, :] + velocities[t, :]*dt\n velocities[t+1, :] = velocities[t, :] + get_acceleration(t)*dt\n\ndef velocity_verlet():\n for t in range(time_steps-1):\n positions[t+1, :] = positions[t, :] + velocities[t, :]*dt + 0.5*accelerations[t, :]*dt**2\n accelerations[t+1, :] = get_acceleration(t+1)\n velocities[t+1, :] = velocities[t, :] + 0.5*(accelerations[t, :] + accelerations[t+1, :])*dt\n\n\npositions[0, :] = [1, 0]\nvelocities[0, :] = [0, 2*np.pi]\naccelerations[0, :] = get_acceleration(0)\n\n#forward_euler()\nvelocity_verlet()\n\nplt.plot(positions[:, 0], positions[:, 1])\nplt.show()","sub_path":"project_3/earth_sun.py","file_name":"earth_sun.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"628162289","text":"import re\n\nimport attr\n\nfrom common import bounds\nfrom common import read_lines\n\n\n@attr.s(auto_attribs=True, frozen=True)\nclass Point:\n x: int\n y: int\n dx: int\n dy: int\n\n @property\n def coord(self):\n return self.x, self.y\n\n def step(self):\n return type(self)(self.x + self.dx, self.y + self.dy, self.dx, self.dy)\n\n\ndef parse(lines):\n return [\n Point(*values)\n for values in ((int(x) for x in re.findall(r\"-?\\d+\", line)) for line in lines)\n ]\n\n\ndef step(points):\n return [point.step() for point in points]\n\n\ndef score_area(points):\n left, top, right, bottom = bounds(tuple(point.coord for point in points))\n return abs(right - left) * abs(bottom - top)\n\n\ndef find_message(points):\n best_score = 2 ** 64\n best_points = None\n seconds = 0\n\n for i in range(100_000):\n score = score_area(points)\n\n if score < best_score:\n best_score = score\n best_points = points\n seconds = i\n elif score >= best_score:\n break\n\n points = step(points)\n\n return seconds, best_points\n\n\ndef draw_message(seconds, points):\n coords = {point.coord for point in points}\n left, top, right, bottom = bounds(coords)\n lines = []\n\n for y in range(top, bottom + 1):\n for x in range(left, right + 1):\n lines.append(\"X\" if (x, y) in coords else \".\")\n\n lines.append(\"\\n\")\n\n print(\"\".join(lines))\n print(f\"{seconds} seconds\")\n\n\ndraw_message(*find_message(parse(read_lines())))\n","sub_path":"year2018/day10/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"232281087","text":"import click\nimport inspect\nimport gncgym.scenarios.example_scenarios as scenarios\nfrom gncgym.base_env.base import BaseShipScenario\nfrom gncgym.play import play_scenario\n\n@click.group()\ndef cli():\n \"\"\"\n Entry point for the package when called from the command line. Defined as a Click command group, to enable\n the addition of new commands as the need arises.\n \"\"\"\n pass\n\n\n@cli.command()\n@click.option('--model',\n default='supply_ship_3DOF',\n help='The name of the model that you want to simulate. This must refer to a '\n 'module in the models directory.')\n\n@click.option('--scenario',\n default='ExampleScenario',\n help='The scenario that you want to run.')\n\n@click.option('--controller',\n default='human',\n help='Name of module that defines a control module for the model. This module '\n 'uses the measured state to generate inputs to the model')\n\n@click.option('--objective',\n default='path-following',\n help='A control objective for the model.')\n\n@click.option('--disturbances',\n default='none',\n help='Name of module that defines disturbances to add to the simulation.'\n 'Support for disturbances is model dependent.')\n\n@click.option('--observer',\n default='none',\n help='Measurement nodule. If you want to simulate noisy measurements or'\n 'experiment with different oberservers, this is the place to do it.')\n\ndef play(model, scenario, controller, objective, disturbances, observer):\n \"\"\"\n\n :param model:\n :param scenario:\n :return:\n \"\"\"\n play_scenario(scenario)\n\n\n@cli.command()\ndef make():\n import gym\n\n env = gym.make('shipExampleScenario-v0')\n","sub_path":"src/gncgym/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"302497488","text":"# -*- coding: utf-8 -*-\n# Copyright 2023 Google LLC\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#\nfrom __future__ import annotations\n\nfrom typing import MutableMapping, MutableSequence\n\nimport proto # type: ignore\n\n\n__protobuf__ = proto.module(\n package=\"google.cloud.aiplatform.v1.schema.predict.prediction\",\n manifest={\n \"TextSentimentPredictionResult\",\n },\n)\n\n\nclass TextSentimentPredictionResult(proto.Message):\n r\"\"\"Prediction output format for Text Sentiment\n\n Attributes:\n sentiment (int):\n The integer sentiment labels between 0\n (inclusive) and sentimentMax label (inclusive),\n while 0 maps to the least positive sentiment and\n sentimentMax maps to the most positive one. The\n higher the score is, the more positive the\n sentiment in the text snippet is. Note:\n sentimentMax is an integer value between 1\n (inclusive) and 10 (inclusive).\n \"\"\"\n\n sentiment: int = proto.Field(\n proto.INT32,\n number=1,\n )\n\n\n__all__ = tuple(sorted(__protobuf__.manifest))\n","sub_path":"google/cloud/aiplatform/v1/schema/predict/prediction_v1/types/text_sentiment.py","file_name":"text_sentiment.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"611236352","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom pathlib import Path\nimport unittest\n\nfrom shared.utils import get_input\nfrom . import solution1, solution2\n\n\nSOLUTION_DIR = Path(__file__).parent\n\n\nclass TestSolution(unittest.TestCase):\n module = None\n input_filename = \"test_input.txt\"\n expected = None\n\n def setUp(self):\n if self.module is None:\n raise NotImplementedError(\n \"subclasses of TestSolution must provide module to test\"\n )\n if self.expected is None:\n raise NotImplementedError(\n \"subclasses of TestSolution must provide expected value\"\n )\n self.input_path = SOLUTION_DIR.joinpath(self.input_filename)\n self.input_text = get_input(self.input_path)\n\n\nclass TestSolution1(TestSolution):\n module = solution1\n expected = 6\n\n def test_solver(self):\n solution = self.module.solve(self.input_text)\n self.assertEqual(self.expected, solution)\n\n\nclass TestSolution2(TestSolution):\n module = solution2\n expected = 2047\n\n def test_solver(self):\n solution = self.module.solve(self.input_text)\n self.assertEqual(self.expected, solution)\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"2018/day19/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"578642642","text":"import argparse\nimport os\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nfrom transformers import BertTokenizer, BertConfig\n\nfrom model import Model\nfrom utils.data_utils import SequenceLabelingDataset, glue_processor, prepare_data\nfrom seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ndef evaluate(model, data_raw, id_to_label,mode='dev'):\n model.eval()\n test_data = SequenceLabelingDataset(data_raw)\n test_dataloader = DataLoader(test_data, batch_size=32, collate_fn=test_data.collate_fn)\n preds = []\n labels = []\n epoch_pbar = tqdm(test_dataloader, desc=\"Evaluation\", disable=False)\n for step, batch in enumerate(test_dataloader):\n batch = [b.to(device) if not isinstance(b, int) else b for b in batch]\n input_ids, segment_ids, input_mask, label_ids = batch\n with torch.no_grad():\n output = model(input_ids, segment_ids, input_mask)\n output = output.argmax(dim=2)\n output = output.tolist()\n label_ids = label_ids.tolist()\n output,label_ids = align_predictions(output,label_ids,id_to_label)\n\n preds = preds + output\n labels = labels + label_ids\n epoch_pbar.update(1)\n epoch_pbar.close()\n\n res = {\n \"accuracy_score\": accuracy_score(labels,preds),\n \"precision\": precision_score(labels,preds),\n \"recall\": recall_score(labels,preds),\n \"f1\": f1_score(labels,preds),\n }\n print('Evaluation on ', mode, ' dataset: ', res)\n return res\n\n\ndef align_predictions(preds,label_ids,id_to_label):\n aligned_labels = []\n aligned_preds = []\n\n for p,l in zip(preds,label_ids):\n p_list = []\n l_list = []\n for p_i,l_i in zip(p,l):\n if l_i != -100:\n p_list.append(id_to_label[p_i])\n l_list.append(id_to_label[l_i])\n aligned_preds.append(p_list)\n aligned_labels.append(l_list)\n\n return aligned_preds,aligned_labels\n\ndef set_seed(seed: int):\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n\ndef main(args):\n # Init\n set_seed(args.seed)\n processor = glue_processor[args.task_name.lower()]\n tokenizer = BertTokenizer(args.vocab_path, do_lower_case=True)\n\n # Data\n dev_examples = processor.get_dev_examples(args.data_dir)\n test_examples = processor.get_test_examples(args.data_dir)\n labels = processor.get_labels(args.data_dir)\n dev_data_raw = prepare_data(dev_examples,args.max_seq_len,tokenizer,labels)\n test_data_raw = prepare_data(test_examples, args.max_seq_len, tokenizer, labels)\n\n # Model\n model_config = BertConfig.from_json_file(args.bert_config_path)\n model_config.dropout = args.dropout\n model_config.num_labels = len(labels)\n model = Model(model_config)\n ckpt = torch.load(args.model_ckpt_path, map_location='cpu')\n model.load_state_dict(ckpt, strict=False)\n model.to(device)\n evaluate(model,dev_data_raw,labels,'dev')\n evaluate(model, test_data_raw, labels,'test')\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--seed\", default=42, type=int)\n\n parser.add_argument(\"--task_name\", default='sf', type=str)\n parser.add_argument(\"--data_dir\", default='data/atis/', type=str)\n parser.add_argument(\"--model_path\", default='assets/', type=str)\n\n parser.add_argument(\"--model_ckpt_path\", default='outputs/model_best.bin', type=str)\n parser.add_argument(\"--max_seq_len\", default=60, type=int)\n parser.add_argument(\"--batch_size\", default=32, type=int)\n parser.add_argument(\"--dropout\", default=0.1, type=float)\n args = parser.parse_args()\n args.vocab_path = os.path.join(args.model_path, 'vocab.txt')\n args.bert_config_path = os.path.join(args.model_path, 'config.json')\n print(args)\n main(args)\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"104372281","text":"\n\nfrom xai.brain.wordbase.nouns._hunchback import _HUNCHBACK\n\n#calss header\nclass _HUNCHBACKS(_HUNCHBACK, ):\n\tdef __init__(self,): \n\t\t_HUNCHBACK.__init__(self)\n\t\tself.name = \"HUNCHBACKS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"hunchback\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_hunchbacks.py","file_name":"_hunchbacks.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"554336138","text":"\nfrom PyQt5 import QtWidgets\nfrom ui.like_form import Ui_like_form\n\n\nclass LikeWidget(QtWidgets.QWidget, Ui_like_form):\n def __init__(self, username, sql_connector, central_widget, parent=None):\n super(LikeWidget, self).__init__(parent)\n self.setupUi(self)\n self.parent = parent\n self.central_widget = central_widget\n self.sql_connector = sql_connector\n self.username = username\n self.setup()\n\n def setup(self):\n self.liked_list.clear()\n self.music_list.clear()\n self.music_combo.clear()\n\n cursor = self.sql_connector.cursor()\n cursor.execute(\"SELECT title, id FROM music\")\n musics = []\n for row in cursor:\n self.music_list.addItem(row[0] + \"-\" + str(row[1]))\n musics.append((row[0], str(row[1])))\n\n cursor = self.sql_connector.cursor()\n cursor.execute(\"SELECT music.title, music.id FROM music, user_music_like WHERE music.id=user_music_like.music_id and user_music_like.username=%s\", (self.username, ))\n liked = set()\n for row in cursor:\n self.liked_list.addItem(row[0] + \"-\" + str(row[1]))\n liked.add((row[0], str(row[1])))\n\n for music in musics:\n if music not in liked:\n self.music_combo.addItem(music[0]+\"-\"+ music[1])\n\n self.like_button.clicked.connect(self.like)\n self.back_button.clicked.connect(self.back)\n\n\n def like(self):\n id = int(self.music_combo.currentText().split('-')[1])\n cursor = self.sql_connector.cursor()\n cursor.execute(\"INSERT INTO user_music_like VALUES(%s, %s)\", (self.username, id))\n self.sql_connector.commit()\n self.setup()\n\n def back(self):\n self.parent.setup()\n self.central_widget.setCurrentWidget(self.parent)\n self.central_widget.removeWidget(self)\n del self\n\n","sub_path":"like.py","file_name":"like.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"238960428","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct 10 19:39:26 2017\r\n\r\n@author: PiotrTutak\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom itertools import zip_longest\r\nfrom operator import itemgetter\r\nimport random\r\nimport sys\r\n\r\n\"\"\"\r\nRóżne funkcje aktywacji używane w testowaniu neuronu: \r\n\"\"\"\r\n\r\ndef ident(x):\r\n return float(x)\r\n\r\ndef rectifier(x):\r\n return max(0,x)\r\n\r\ndef one(x):\r\n return 1.0\r\n\r\ndef zero(x):\r\n return 0.0\r\n\r\nclass Const:\r\n def __call__(self,alfa):\r\n def const(x):\r\n return float(alfa)\r\n const.__name__+='({0:.3f})'.format(alfa)\r\n return const\r\n\r\n\r\ndef hardOne(x):\r\n if x<0:\r\n return 0.0\r\n return 1.0\r\n\r\n\r\ndef hardSign(x):\r\n if x<0:\r\n return -1.0\r\n return 1.0\r\n \r\n\r\ndef squash(x):\r\n if x<-1:\r\n return -1.0\r\n elif x>1:\r\n return 1.0\r\n return x\r\n\r\n\r\nclass Sigm:\r\n def __call__(self,alfa):\r\n def sigm(x):\r\n return 1.0/(1.0+np.exp(-alfa*x))\r\n sigm.__name__+='({0:.3f})'.format(alfa)\r\n return sigm\r\n def derivative(self,alfa):\r\n def sigmDeriv(x):\r\n return alfa*np.exp(-alfa*x)/((1.0+np.exp(-alfa*x))**2)\r\n sigmDeriv.__name__+='({0:.3f})'.format(alfa)\r\n return sigmDeriv\r\n\r\nclass SignSigm:\r\n def __call__(self,alfa):\r\n def signSigm(x):\r\n return (2.0/(1.0+np.exp(-alfa*x)))-1.0\r\n signSigm.__name__+='({0:.3f})'.format(alfa)\r\n return signSigm\r\n def derivative(self,alfa):\r\n def signSigmDeriv(x):\r\n return 2.0*alfa*np.exp(-alfa*x)/((1.0+np.exp(-alfa*x))**2)\r\n signSigmDeriv.__name__+='({0:.3f})'.format(alfa)\r\n return signSigmDeriv\r\n\r\n#funkcja wypisująca zawartosc listy z zadaną precyzją\r\ndef listWithPrec(listA,prec):\r\n ret=\"[\"\r\n formatStr=\"{0: \"+str(int(prec+3))+\".\"+str(int(prec))+\"f}\"\r\n for x in listA:\r\n ret+=formatStr.format(x)\r\n ret+=\",\"\r\n ret=ret[:-1]+']'\r\n return ret\r\n\r\n#funkcje liczace wartosci błędów\r\ndef MSE(results,expected):\r\n sum=0.0\r\n for i in range(len(results)):\r\n sum+=(results[i]-expected[i])**2\r\n return sum/len(results)\r\n\r\ndef MAPE(results,expected):\r\n sum=0.0\r\n for i in range(len(results)):\r\n sum+=abs((expected[i]-results[i])/results[i])\r\n return 100*sum/len(results)\r\n\r\n\r\nclass Neuron:\r\n \"\"\"\r\n Klasa Neuron\r\n \"\"\"\r\n def __init__(self, weights, activFunc, activFuncDeriv, learnRate=0.1, bias=-0.5):\r\n self.__dict__['_weights']=np.array(weights)\r\n self.__dict__['_learnRate']=learnRate\r\n self.__dict__['_activFunc']=activFunc\r\n self.__dict__['_activFuncDeriv']=activFuncDeriv\r\n self.__dict__['_bias']=bias\r\n self.__dict__['_error']=None\r\n self.__dict__['_inputValues']=None\r\n self.__dict__['_val']=None\r\n self.__dict__['_output']=None\r\n def process(self,inputValues):\r\n \"\"\"\r\n Funkcja przetwarzająca dane wejsciowe na dane wyjsciowe\r\n \"\"\"\r\n if len(inputValues)!=len(self._weights):\r\n raise TypeError('Wrong values length')\r\n self.__dict__['_inputValues']=np.array(inputValues)\r\n self.__dict__['_val']=np.dot(self._weights,self._inputValues)+self._bias\r\n self.__dict__['_output']=self._activFunc(self._val)\r\n return self._output\r\n \r\n def propagateError(self,weights,errors):\r\n \"\"\"\r\n Funkcja propagująca błąd i korygująca wagi oraz bias\r\n \"\"\"\r\n weights=np.array(weights)\r\n errors=np.array(errors)\r\n if len(errors)!=len(weights):\r\n raise TypeError('Wrong values length')\r\n self.__dict__['_error']=np.dot(weights,errors)*self._activFuncDeriv(self._val)\r\n if (self._learnRate):\r\n for i in range(len(self._weights)):\r\n self._weights[i]+=self._learnRate*self._error*self._inputValues[i]\r\n self.__dict__['_bias']+=self._learnRate*self._error\r\n return self._error\r\n \"\"\"\r\n Funkcje dostępowe:\r\n \"\"\"\r\n def __setitem__(self,index,value):\r\n if index=='learnRate':\r\n self.__dict__['_learnRate']=value\r\n elif index=='activFunc':\r\n self.__dict__['_activFunc']=value\r\n \r\n def __getitem__(self,index):\r\n if index=='error':\r\n return self._error\r\n elif index=='input':\r\n return self._inputValues\r\n elif index=='value':\r\n return self._val\r\n elif index=='learnRate':\r\n return self._learnRate\r\n return self._weights[index]\r\n \r\n def __getattr__(self,attr):\r\n raise AttributeError('get: No such attribute: %r'%attr)\r\n \r\n def __setattr__(self,attr,value):\r\n raise AttributeError('set: No such attribute: %r'%attr)\r\n \r\n def __iter__(self):\r\n return iter(self._weights)\r\n \r\n def __len__(self):\r\n return len(self._weights)\r\n \r\n def __repr__(self):\r\n w='['+','.join('{:8.5f}'.format(x) for x in self._weights)+']'\r\n return 'Neuron(weights:{0},bias:{1:8.5f},learnRate:{2:.5f},activFunc:{3!s},activFuncDeriv:{4!s})'.format(w,self._bias,self._learnRate,self._activFunc.__name__,self._activFuncDeriv.__name__)\r\n\r\n\r\nclass Layer:\r\n \"\"\"\r\n Klasa wartstwy używana w wielowarstwowej sieci neuronowej.\r\n \"\"\"\r\n def __init__(self,inputNumber,neuronNumber,activFunc,activFuncDeriv,weights=None,learnRate=None,bias=None):\r\n self.__dict__['_inputNumber']=inputNumber\r\n self.__dict__['_neuronNumber']=neuronNumber\r\n self.__dict__['_activFunc']=activFunc\r\n self.__dict__['_activFuncDeriv']=activFuncDeriv\r\n \r\n if weights!=None:\r\n _weights=list(weights)\r\n if inputNumber>len(_weights):\r\n _weights.extend([0.8*np.random.ranf()+0.1*np.random.choice([-1.0,1.0]) for _ in range(inputNumber-len(_weights))])\r\n else:\r\n _weights=None\r\n \r\n \r\n if learnRate!=None:\r\n self.__dict__['_learnRate']=learnRate\r\n else:\r\n self.__dict__['_learnRate']=0.1\r\n \r\n _bias=bias\r\n if _weights:\r\n if _bias!=None:\r\n self.__dict__['_neurons']=[Neuron(_weights[:inputNumber],activFunc,activFuncDeriv,learnRate=self._learnRate,bias=_bias) for _ in range(neuronNumber)]\r\n else:\r\n self.__dict__['_neurons']=[Neuron(_weights[:inputNumber],activFunc,activFuncDeriv,learnRate=self._learnRate,bias=-0.08*np.random.ranf()-0.01) for _ in range(neuronNumber)]\r\n else:\r\n if _bias!=None:\r\n self.__dict__['_neurons']=[Neuron([(0.08*np.random.ranf()+0.01)*np.random.choice([-1.0,1.0]) for _ in range(inputNumber)],activFunc,activFuncDeriv,learnRate=self._learnRate,bias=_bias) for _ in range(neuronNumber)]\r\n else:\r\n self.__dict__['_neurons']=[Neuron([(0.08*np.random.ranf()+0.01)*np.random.choice([-1.0,1.0]) for _ in range(inputNumber)],activFunc,activFuncDeriv,learnRate=self._learnRate,bias=-0.08*np.random.ranf()-0.01) for _ in range(neuronNumber)]\r\n \r\n \"\"\"\r\n Funkcje dostępowe\r\n \"\"\"\r\n def __len__(self):\r\n return len(self._neurons)\r\n def __getitem__(self,index):\r\n if index=='learnRate':\r\n return self._learnRate\r\n return self._neurons[index]\r\n def __iter__(self):\r\n return iter(self._neurons)\r\n def __setitem__(self,index,value):\r\n if index=='learnRate':\r\n self.__dict__['_learnRate']=value\r\n for x in self._neurons:\r\n x['learnRate']=value\r\n elif index=='activFunc':\r\n self.__dict__['_activFunc']=value\r\n for x in self._neurons:\r\n x['activFunc']=value\r\n def __getattr__(self,attr):\r\n raise AttributeError('get: No such attribute: %r'%attr)\r\n \r\n def __setattr__(self,attr,value):\r\n raise AttributeError('set: No such attribute: %r'%attr)\r\n \r\n def __repr__(self):\r\n result='Layer(inputNumber:{0}, neuronNumber:{1}, activFunc:{2!s}, activFuncDeriv:{3!s}, learnRate:{4:.5f})'\\\r\n .format(self._inputNumber,self._neuronNumber,self._activFunc.__name__,self._activFuncDeriv.__name__,self._learnRate)\r\n return result\r\n def __str__(self):\r\n result=repr(self)+'\\n'\r\n for p in self:\r\n result+=' '+str(p)+'\\n'\r\n return result\r\n\r\n\r\n\r\nclass Multilayer:\r\n \"\"\"\r\n Wielowarstwa z możliwoscią zaprogramwania indywidualnie każdej wartstwy\r\n \"\"\"\r\n def __init__(self,layers,activFuncs=None,activFuncDerivs=None,weights=[], learnRates=[], biases=[]):\r\n if isinstance(layers[0],Layer):\r\n self._layers=layers\r\n elif isinstance(layers[0],int):\r\n if not all([activFuncs,activFuncDerivs]):\r\n raise TypeError('Missing activation functions or derivatives')\r\n neuronNumbers=layers\r\n l=zip_longest(neuronNumbers,activFuncs,activFuncDerivs,weights,learnRates,biases,fillvalue=None)\r\n prev=next(l)\r\n layerList=[Layer(1,*prev)]\r\n for x in l:\r\n layerList.append(Layer(prev[0],*x))\r\n prev=x\r\n self._layers=layerList\r\n def process(self,inputValues):\r\n \"\"\"\r\n Funkcja przetwarzająca dane wejsciowe sieci na dane wyjsciowe\r\n \"\"\"\r\n inputValues=list(inputValues)\r\n values=[]\r\n \r\n for p in self._layers[0]:\r\n values.append(p.process(inputValues[:len(p)]))\r\n inputValues=inputValues[len(p):] \r\n \r\n for layer in self._layers[1:]:\r\n inputValues=values\r\n values=[]\r\n for p in layer:\r\n values.append(p.process(inputValues))\r\n return values\r\n \r\n def learn(self,inputValues,expectedValues):\r\n \"\"\"\r\n Funkcja ucząca sieć neuronową po uprzednim nadaniu współczynników uczenia\r\n dla każdej wartwy\r\n \"\"\"\r\n results=self.process(inputValues)\r\n if len(results)!=len(expectedValues):\r\n raise IndexError('wrong number of expected values')\r\n results=iter(results)\r\n lenExpectedValues=len(expectedValues)\r\n expectedValues=iter(expectedValues)\r\n errors=[]\r\n for _ in range(lenExpectedValues):\r\n errors.append([next(expectedValues)-next(results)])\r\n weights=[[1] for _ in range(len(errors))]\r\n for layer in reversed(self._layers):\r\n newErrors=[]\r\n oldWeights=[]\r\n for p in layer:\r\n oldWeights.append(p[:])\r\n newErrors.append(p.propagateError(weights.pop(0),errors.pop(0)))\r\n weights=list(zip(*oldWeights))\r\n errors=[newErrors for x in range(len(weights))]\r\n \"\"\"\r\n Funkcje dostępowe\r\n \"\"\"\r\n def multiLearnRates(self,value):\r\n for l in self._layers:\r\n l['learnRate']*=value\r\n def setLearnRates(self,value):\r\n for l in self._layers:\r\n l['learnRate']=value\r\n def __getitem__(self,index):\r\n return self._layers[index]\r\n def __iter__(self):\r\n return iter(self._layers)\r\n def __repr__(self):\r\n result='Multilayer:\\n'\r\n for layer in self._layers:\r\n result+=' '+repr(layer)\r\n result+='\\n'\r\n return result\r\n def __str__(self):\r\n result='Multilayer:\\n'\r\n for layer in self._layers:\r\n result+=' '+str(layer)\r\n result+='\\n'\r\n return result\r\n \r\n \r\n\r\nif __name__=='__main__':\r\n \"\"\"\r\n Kod programu przeprowadzającego uczenie i testowanie neuronu\r\n Wyjscie jest przekierowywane do pliku results.txt\r\n \"\"\"\r\n# STDOUT=sys.stdout\r\n# f=open('results.txt','w');\r\n# sys.stdout=f\r\n \r\n SigmFactory=SignSigm()\r\n print('Funkcja AND:')\r\n inputData=(\r\n ((0,0),0),\r\n ((0,1),0),\r\n ((1,0),0),\r\n ((1,1),1)\r\n )\r\n for x in inputData:\r\n print(\"data: {0}, expected: {1}\".format(*x))\r\n \r\n listPerc=[]\r\n RES_NUMBER=100\r\n while(len(listPerc) maxLeftRight:\n dataItemsCentre = dataItems[\"centre\"]\n shuffle(dataItemsCentre)\n nDataPoints = (len(dataItems[\"left\"]) + len(dataItems[\"right\"])) // 2\n dataItemsCentreSampled = dataItems[\"centre\"][0:nDataPoints]\n print(\"DEBUG (post balance): len(dataItemsCentreSampled) = \" + str(len(dataItemsCentreSampled)))\n self._trainingDataItems = dataItems[\"left\"] + dataItems[\"right\"] + dataItemsCentreSampled\n else:\n self._trainingDataItems = dataItems[\"left\"] + dataItems[\"right\"] + dataItems[\"centre\"]\n \n def read_training_data(self):\n dataItems =\t{\n \"left\": [],\n \"right\": [],\n \"centre\": []\n }\n for line in self._trainingData:\n imagesPerLine = len(self._cameraAngleCorrectionFactors)\n for i in range(imagesPerLine): \n filename = self.convert_path(\"..\\\\user-data\" + line[i].split('user-data')[-1].strip())\n angle = float(line[3]) + self._cameraAngleCorrectionFactors[i]\n if angle > self._steeringThreshold:\n dataItems[\"right\"].append(DataItem(filename, angle))\n elif angle < -1 * self._steeringThreshold:\n dataItems[\"left\"].append(DataItem(filename, angle))\n else:\n dataItems[\"centre\"].append(DataItem(filename, angle))\n return dataItems\n \n def read_validation_data(self):\n for line in self._validationData:\n imagesPerLine = len(self._cameraAngleCorrectionFactors)\n for i in range(imagesPerLine): \n filename = self.convert_path(\"..\\\\user-data\" + line[i].split('user-data')[-1].strip())\n angle = float(line[3]) + self._cameraAngleCorrectionFactors[i]\n self._validationDataItems.append(DataItem(filename, angle))\n \n def convert_path(self, path):\n if platform.system() == 'Linux':\n return path.replace('\\\\','/')\n else:\n return path\n","sub_path":"project3/src/data_items.py","file_name":"data_items.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"319668066","text":"# -*- coding: utf-8 -*-\n# BioSTEAM: The Biorefinery Simulation and Techno-Economic Analysis Modules\n# Copyright (C) 2020, Yoel Cortes-Pena \n# \n# This module is under the UIUC open-source license. See \n# github.com/BioSTEAMDevelopmentGroup/biosteam/blob/master/LICENSE.txt\n# for license details.\n\"\"\"\n\"\"\"\nfrom .utils import colors\nimport flexsolve as flx\n\n__all__ = ('UndefinedChemical',\n 'UndefinedPhase',\n 'DimensionError',\n 'InfeasibleRegion',\n 'DomainError',\n 'InvalidMethod',\n 'message_with_object_stamp',\n 'try_method_with_object_stamp',\n 'raise_error_with_object_stamp')\n\nclass InfeasibleRegion(RuntimeError):\n \"\"\"Runtime error regarding infeasible processes.\"\"\"\n def __init__(self, region): \n self.region = region\n super().__init__(region + ' is infeasible')\n\nclass UndefinedChemical(AttributeError):\n \"\"\"AttributeError regarding undefined chemicals.\"\"\"\n def __init__(self, ID): super().__init__(repr(ID))\n \nclass UndefinedPhase(AttributeError):\n \"\"\"AttributeError regarding undefined phases.\"\"\"\n def __init__(self, phase): super().__init__(repr(phase))\n\nclass DimensionError(ValueError):\n \"\"\"ValueError regarding wrong dimensions.\"\"\"\n\nclass DomainError(ValueError):\n \"\"\"ValueError regarding an attempt to evaluate a model out of its domain.\"\"\"\n def __init__(self, msg, **data):\n super().__init__(msg)\n self.__dict__.update(data)\n\nclass InvalidMethod(ValueError):\n \"\"\"ValueError regarding an attempt to evaluate an invalid method.\"\"\"\n def __init__(self, method):\n super().__init__(repr(method))\n \ndef message_with_object_stamp(object, msg):\n object_name = str(repr(object))\n if object_name in msg:\n return msg\n else:\n return colors.violet(object_name) + ' ' + msg\n\ndef raise_error_with_object_stamp(object, error):\n try: \n msg, *args = error.args\n error.args = (message_with_object_stamp(object, msg), *args)\n except: pass\n raise error\n\ndef try_method_with_object_stamp(object, method, args=()):\n try:\n return method(*args)\n except KeyError as error:\n raise error\n except Exception as error:\n raise_error_with_object_stamp(object, error)","sub_path":"thermosteam/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"273002709","text":"m,n,k=map(int,input().split())\r\nfor i in range(k):\r\n flag=True\r\n stack=[] #模拟栈\r\n l=list(map(int,input().split()))\r\n index=0 #待查找的下标\r\n start=1 #往栈里添加的元素，从2开始\r\n while index0 and stack[-1]!=l[index]:\r\n start+=1\r\n stack.append(start)\r\n if len(stack)>m: #超过栈的最大容量\r\n flag=False\r\n break\r\n if not flag:\r\n break\r\n else: #未超过最大容量且栈顶等于序列index处值\r\n while len(stack)>0 and stack[-1]==l[index]:\r\n stack.pop()\r\n index+=1\r\n start+=1\r\n if not flag or len(stack)>0:\r\n print(\"NO\")\r\n else:\r\n print(\"YES\")\r\n","sub_path":"1051.py","file_name":"1051.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"526344414","text":"#!/usr/bin/python3\n\nimport sys\nimport random\nimport os\nimport socket\n\n\nuser=os.getlogin()\nsrc_station=socket.gethostname()\nstream_splitter_ip='10.0.10.2'\nstream_splitter_port='4444'\n\n\n\n\n\n####### Parse input arguments into variables ##############\nif __name__ == \"__main__\":\n\timport argparse\n\tparser = argparse.ArgumentParser()\n\t## station input should depreciate as multisend pases through servers\n\t##parser.add_argument('--station', '-s', help=\"Station destination identity\")\n\tparser.add_argument('--profile', '-p', help=\"Override default profile\")\n\tparser.add_argument('--force', '-f', help=\"Force existing docker to close and run again\")\n\tparser.add_argument('--destroy', '-d', help=\"Destroy the direct-send container\")\n\tparser.add_argument('--mode', '-m', help=\"Define mode (udp/rtp)\")\n\tparser.add_argument('--relay1', '-1', help=\"Specify first relay server\")\n\tparser.add_argument('--relay2', '-2', help=\"Specify second relay server\")\n\tparser.add_argument('--relay3', '-3', help=\"Specify third relay server\")\n\n\t## Replicate above line to add more optional input arguments\n\t\n\targs = parser.parse_args()\n\tprint()\n\t## Desitnation station not defined in relay mode\n\t##print(\"Retrieving station details for station Ident: \", args.station)\n\n\n\tprofile = args.profile\n\tmode = args.mode\n\n\t## Destination not needed in relay mode\n\t##if(args.station):\n\t##\tdest_station=\"Destination determined by Relay\"\n\t##else:\n\t##\tdest_station='None'\n\n\n\tif(args.force=='1'):\n\t\tprint(\"*** FORCING ------------------------ ***\")\n\t\tprint(\"*** Destroying container: multi-send ***\")\n\t\tprint(\"*** -------------------------------- ***\")\n\t\tos.system(\"sudo docker rm -f multi-send\")\n\n\t\n\n\tif(args.destroy=='1'):\n\t\tprint(\"*** Destroying container: multi-send ***\")\n\t\tos.system(\"sudo docker rm -f multi-send\")\n\t\tquit()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\tif(args.relay1 is not None):\n\t\tprint('relay 1 manually entered, but not programmed to override defaults yet')\n\tif(args.relay2 is not None):\n\t\tprint('relay 2 manually entered, but not programmed to override defaults yet')\n\tif(args.relay3 is not None):\n\t\tprint('relay 3 manually entered, but not programmed to override defaults yet')\n\t\t\n\t\n\n######## END Processing input args into variables ############\n\n\n\n\n\n################ CLASS DEFINITION FOR channel ##############\nclass channelObj:\n\t####### Source Machine Variables #######\n\t__station_src_ident = None\n\t__station_src_ip = None\n\t__station_src_mode = None\n\t__station_src_profile_pref = None\n\t__station_src_tx_limit_mbps = None\n\t__station_src_rx_limit_mbps = None\n\t__station_src_rx_port = None\n\t__station_src_relay_ident_1 = None\n\t__station_src_relay_ident_2 = None\n\t__station_src_relay_ident_3 = None\n\t__station_src_contact_admin_name = None\n\t__station_src_contact_admin_phone = None\n\t__station_src_contact_admin_email = None\n\t__station_src_contact_admin_company = None\n\t__station_src_contact_admin_notes = None\n\t####### Destination Variables #########\n\t__station_dest_ident = None\n\t__station_dest_ip = None\n\t__station_dest_mode = None\n\t__station_profile_pref = None\n\t__station_dest_tx_limit_mbps = None\n\t__station_dest_rx_limit_mbps = None\n\t__station_dest_rx_port = None\n\t__station_dest_relay_ident_1 = None\n\t__station_dest_relay_ident_2 = None\n\t__station_dest_relay_ident_3 = None\n\t__station_dest_contact_admin_name = None\n\t__station_dest_contact_admin_phone = None\n\t__station_dest_contact_admin_email = None\n\t__station_dest_contact_admin_company = None\n\t__station_dest_contact_admin_notes = None\n\t###### Set standard relay variables\n\t__relay_1_ident = None\n\t__relay_1_ip = None\n\t__relay_1_port_in = None\n\t__relay_1_port_tcp = None\n\t__relay_2_ident = None\n\t__relay_2_ip = None\n\t__relay_2_port_in = None\n\t__relay_2_port_tcp = None\n\t__relay_3_ident = None\n\t__relay_3_ip = None\n\t__relay_3_port_in = None\n\t__relay_3_port_tcp = None\n\t###### Set standard profile override variables\n\t__profile_profile = None\n\t__profile_tx_mode = None\n\t__profile_v_ts_type= None\n\t__profile_v_video_format = None\n\t__profile_v_pid = None\n\t__profile_v_vbv_bitrate = None\n\t__profile_v_vbv_maxrate = None\n\t__profile_v_muxrate = None\n\t__profile_v_vbv_bufsize = None\n\t__profile_v_format = None\n\t__profile_v_aspect_ratio = None\n\t__profile_v_cbr = None\n\t__profile_v_keyint = None\n\t__profile_v_bframes = None\n\t__profile_v_level = None\n\t__profile_v_profile = None\n\t__profile_intra_refresh = None\n\t__profile_v_threads = None\n\t__profile_system_type = None\n\t__profile_a_pid = None\n\t__profile_a_bitrate = None\n\t__profile_a_format = None\n\t__profile_a_profile = None\n\t__profile_a_aac_encap = None\n\t__profile_a_aac_profile = None\n\t__profile_service_name = None\n\t__profile_provider_name = None\n\t__profile_pmt_pid = None\n\t### end variable definitions\n\n\n\t### Method Definitions\n\n\t#### init method\n\tdef __init__(self, station_dest_ident):\n\t\tself.__station_dest_ident = station_dest_ident\n\t\tself.__station_src_ident = src_station\n\n\t#### source methods\n\tdef set_station_src_ip(self, station_src_ip):\n\t\tself.__station_src_ip = station_src_ip\n\n\tdef set_station_src_mode(self, station_src_mode):\n\t\tself.__station_src_mode = station_src_mode\n\n\tdef set_station_src_profile_pref(self, station_src_profile_pref):\n\t\tself.__station_src_profile_pref = station_src_profile_pref\n\n\tdef set_station_src_mode(self, station_src_mode):\n\t\tself.__station_src_mode = station_src_mode\n\n\tdef set_station_src_tx_limit_mbps(self, station_src_tx_limit_mbps):\n\t\tself.__station_src_tx_limit_mbps = station_src_tx_limit_mbps\n\n\tdef set_station_src_rx_limit_mbps(self, station_src_rx_limit_mbps):\n\t\tself.__station_src_rx_limit_mbps = station_src_rx_limit_mbps\n\n\tdef set_station_src_rx_limit_mbps(self, station_src_rx_limit_mbps):\n\t\tself.__station_src_rx_limit_mbps = station_src_rx_limit_mbps\n\n\tdef set_station_src_rx_port(self, station_src_rx_port):\n\t\tself.__station_src_rx_port = station_src_rx_port\n\n\tdef set_station_src_relay_ident_1(self, station_src_relay_ident_1):\n\t\tself.__station_src_relay_ident_1 = station_src_relay_ident_1\n\n\tdef set_station_src_relay_ident_2(self, station_src_relay_ident_2):\n\t\tself.__station_src_relay_ident_2 = station_src_relay_ident_2\n\n\tdef set_station_src_relay_ident_3(self, station_src_relay_ident_3):\n\t\tself.__station_src_relay_ident_3 = station_src_relay_ident_3\n\n\tdef set_station_src_relay_ident_3(self, station_src_relay_ident_3):\n\t\tself.__station_src_relay_ident_3 = station_src_relay_ident_3\n\n\tdef set_station_src_contact_admin_name(self, station_src_contact_admin_name):\n\t\tself.__station_src_contact_admin_name = station_src_contact_admin_name\n\n\tdef set_station_src_contact_admin_phone(self, station_src_contact_admin_phone):\n\t\tself.__station_src_contact_admin_phone = station_src_contact_admin_phone\n\n\tdef set_station_src_contact_admin_email(self, station_src_contact_admin_email):\n\t\tself.__station_src_contact_admin_email = station_src_contact_admin_email\n\n\tdef set_station_src_contact_admin_company(self, station_src_contact_admin_company):\n\t\tself.__station_src_contact_admin_company = station_src_contact_admin_company\n\n\tdef set_station_src_contact_admin_notes(self, station_src_contact_admin_notes):\n\t\tself.__station_src_contact_admin_notes = station_src_contact_admin_notes\n\n\t#### destination methods\n\tdef set_station_dest_ip(self, station_dest_ip):\n\t\tself.__station_dest_ip = station_dest_ip\n\n\tdef set_station_dest_mode(self, station_dest_mode):\n\t\tself.__station_dest_mode = station_dest_mode\n\n\tdef set_station_dest_profile_pref(self, station_dest_profile_pref):\n\t\tself.__station_dest_profile_pref = station_dest_profile_pref\n\n\tdef set_station_dest_tx_limit_mbps(self, station_dest_tx_limit_mbps):\n\t\tself.__station_dest_tx_limit_mbps = station_dest_tx_limit_mbps\n\t\n\tdef set_station_dest_rx_limit_mbps(self, station_dest_rx_limit_mbps):\n\t\tself.__station_dest_rx_limit_mbps = station_dest_rx_limit_mbps\n\n\tdef set_station_dest_rx_port(self, station_dest_rx_port):\n\t\tself.__station_dest_rx_port = station_dest_rx_port\n\n\tdef set_station_dest_relay_ident_1(self, station_dest_relay_ident_1):\n\t\tself.__station_dest_relay_ident_1 = station_dest_relay_ident_1\n\n\tdef set_station_dest_relay_ident_2(self, station_dest_relay_ident_2):\n\t\tself.__station_dest_relay_ident_2 = station_dest_relay_ident_2\n\n\tdef set_station_dest_relay_ident_3(self, station_dest_relay_ident_3):\n\t\tself.__station_dest_relay_ident_3 = station_dest_relay_ident_3\n\n\tdef set_station_dest_contact_admin_name(self, station_dest_contact_admin_name):\n\t\tself.__station_dest_contact_admin_name = station_dest_contact_admin_name\n\n\tdef set_station_dest_contact_admin_phone(self, station_dest_contact_admin_phone):\n\t\tself.__station_dest_contact_admin_phone = station_dest_contact_admin_phone\n\n\tdef set_station_dest_contact_admin_email(self, station_dest_contact_admin_email):\n\t\tself.__station_dest_contact_admin_email = station_dest_contact_admin_email\n\n\tdef set_station_dest_contact_admin_company(self, station_dest_contact_admin_company):\n\t\tself.__station_dest_contact_admin_company = station_dest_contact_admin_company\n\n\tdef set_station_dest_contact_admin_notes(self, station_dest_contact_admin_notes):\n\t\tself.__station_dest_contact_admin_notes = station_dest_contact_admin_notes\n\n\n\n\t### profile methods\n\tdef set_profile_profile(self, profile_profile):\n\t\tself.__profile_profile = profile_profile\n\n\tdef set_profile_tx_mode(self, profile_tx_mode):\n\t\tself.__profile_tx_mode = profile_tx_mode\n\t\t\n\tdef set_profile_v_ts_type(self, profile_v_ts_type):\n\t\tself.__profile_v_ts_type = profile_v_ts_type\n\n\tdef set_profile_v_video_format(self, v_video_format):\n\t\tself.__profile_v_video_format = v_video_format\n\n\tdef set_profile_v_pid(self, profile_v_pid):\n\t\tself.__profile_v_pid = profile_v_pid\n\n\tdef set_profile_v_vbv_bitrate(self, profile_v_vbv_bitrate):\n\t\tself.__profile_v_vbv_bitrate = profile_v_vbv_bitrate\n\n\tdef set_profile_v_vbv_maxrate(self, profile_v_vbv_maxrate):\n\t\tself.__profile_v_vbv_maxrate = profile_v_vbv_maxrate\n\n\tdef set_profile_v_muxrate(self, profile_v_muxrate):\n\t\tself.__profile_v_muxrate = profile_v_muxrate\n\n\tdef set_profile_v_vbv_bufsize(self, profile_v_vbv_bufsize):\n\t\tself.__profile_v_vbv_bufsize = profile_v_vbv_bufsize\n\n\tdef set_profile_v_format(self, profile_v_format):\n\t\tself.__profile_v_format= profile_v_format\n\n\tdef set_profile_v_aspect_ratio(self, profile_v_aspect_ratio):\n\t\tself.__profile_v_aspect_ratio = profile_v_aspect_ratio\n\n\tdef set_profile_v_cbr(self, profile_v_cbr):\n\t\tself.__profile_v_cbr = profile_v_cbr\n\n\tdef set_profile_v_keyint(self, profile_v_keyint):\n\t\tself.__profile_v_keyint = profile_v_keyint\n\n\tdef set_profile_v_bframes(self, profile_v_bframes):\n\t\tself.__profile_v_bframes = profile_v_bframes\n\n\tdef set_profile_v_level(self, profile_v_level):\n\t\tself.__profile_v_level = profile_v_level\n\n\tdef set_profile_v_profile(self, profile_v_profile):\n\t\tself.__profile_v_profile = profile_v_profile\n\n\tdef set_profile_v_intra_refresh(self, profile_v_intra_refresh):\n\t\tself.__profile_v_intra_refresh = profile_v_intra_refresh\n\n\tdef set_profile_v_threads(self, profile_v_threads):\n\t\tself.__profile_v_threads = profile_v_threads\n\n\tdef set_profile_system_type(self, profile_system_type):\n\t\tself.__profile_system_type = profile_system_type\n\n\tdef set_profile_a_pid(self, profile_a_pid):\n\t\tself.__profile_a_pid = profile_a_pid\n\n\tdef set_profile_a_bitrate(self, profile_a_bitrate):\n\t\tself.__profile_a_bitrate = profile_a_bitrate\n\n\tdef set_profile_a_format(self, profile_a_format):\n\t\tself.__profile_a_format = profile_a_format\n\n\tdef set_profile_a_profile(self, profile_a_profile):\n\t\tself.__profile_a_profile = profile_a_profile\n\n\tdef set_profile_a_aac_encap(self, profile_a_aac_encap):\n\t\tself.__profile_a_aac_encap = profile_a_aac_encap\n\n\tdef set_profile_a_aac_profile(self, profile_a_aac_profile):\n\t\tself.__profile_a_aac_profile = profile_a_aac_profile\n\n\tdef set_profile_service_name(self, profile_service_name):\n\t\tself.__profile_service_name = profile_service_name\n\n\tdef set_profile_provider_name(self, profile_provider_name):\n\t\tself.__profile_provider_name = profile_provider_name\n\n\tdef set_profile_pmt_pid(self, profile_pmt_pid):\n\t\tself.__profile_pmt_pid = profile_pmt_pid\n\n\n\t### relay methods\n\n\tdef set_relay_info(self, relayNum, ident, ip, port_in, port_tcp):\n\t\tif relayNum == \"1\":\n\t\t\tprint('setting relay 1 info in channel class')\n\t\t\tself.__relay_1_ident = ident\n\t\t\tself.__relay_1_ip = ip\n\t\t\tself.__relay_1_port_in = port_in\n\t\t\tself.__relay_1_port_tcp = port_tcp\n\t\telif relayNum == \"2\":\n\t\t\tprint('setting relay 2 info in channel class')\n\t\t\tself.__relay_2_ident = ident\n\t\t\tself.__relay_2_ip = ip\n\t\t\tself.__relay_2_port_in = port_in\n\t\t\tself.__relay_2_port_tcp = port_tcp\n\t\telif relayNum == \"3\":\n\t\t\tprint('setting relay 3 info in channel class')\n\t\t\tself.__relay_3_ident = ident\n\t\t\tself.__relay_3_ip = ip\n\t\t\tself.__relay_3_port_in = port_in\n\t\t\tself.__relay_3_port_tcp = port_tcp\n\t\telse:\n\t\t\tprint('can not set relay info in Connection class')\n\n\n\n\t#### ADD MORE SET STATMENTS ABOVE THIS LINE\n\t#### ADD GET STATEMENTS BELOW THIS LINE\n\n\tdef get_station_src_ident(self):\n\t\treturn(self.__station_src_ident)\n\n\tdef get_station_src_ip(self):\n\t\treturn(self.__station_src_ip)\n\n\tdef get_station_src_mode(self):\n\t\treturn(self.__station_src_mode)\n\n\tdef get_station_src_profile_pref(self):\n\t\treturn(self.__station_src_profile_pref)\t\n\n\tdef get_station_src_tx_limit_mbps(self):\n\t\treturn(self.__station_src_tx_limit_mbps)\t\n\n\tdef get_station_src_rx_limit_mbps(self):\n\t\treturn(self.__station_src_rx_limit_mbps)\n\n\tdef get_station_src_rx_port(self):\n\t\treturn(self.__station_src_rx_port)\t\n\n\tdef get_station_src_relay_ident_1(self):\n\t\treturn(self.__station_src_relay_ident_1)\n\n\tdef get_station_src_relay_ident_2(self):\n\t\treturn(self.__station_src_relay_ident_2)\n\n\tdef get_station_src_relay_ident_3(self):\n\t\treturn(self.__station_src_relay_ident_3)\n\n\tdef get_station_src_contact_admin_name(self):\n\t\treturn(self.__station_src_contact_admin_name)\t\n\n\tdef get_station_src_contact_admin_phone(self):\n\t\treturn(self.__station_src_contact_admin_phone)\t\n\n\tdef get_station_src_contact_admin_email(self):\n\t\treturn(self.__station_src_contact_admin_email)\n\n\tdef get_station_src_contact_admin_company(self):\n\t\treturn(self.__station_src_contact_admin_company)\n\n\tdef get_station_src_contact_admin_notes(self):\n\t\treturn(self.__station_src_contact_admin_notes)\n\n\n\n\tdef get_station_dest_ident(self):\n\t\treturn(self.__station_dest_ident)\n\n\tdef get_station_dest_ip(self):\n\t\treturn(self.__station_dest_ip)\n\n\tdef get_station_dest_mode(self):\n\t\treturn(self.__station_dest_mode)\n\n\tdef get_station_dest_profile_pref(self):\n\t\treturn(self.__station_dest_profile_pref)\n\n\tdef get_station_dest_tx_limit_mbps(self):\n\t\treturn(self.__station_dest_tx_limit_mbps)\n\n\tdef get_station_dest_rx_limit_mbps(self):\n\t\treturn(self.__station_dest_rx_limit_mbps)\n\n\tdef get_station_dest_rx_port(self):\n\t\treturn(self.__station_dest_rx_port)\n\n\tdef get_station_dest_relay_ident_1(self):\n\t\treturn(self.__station_dest_relay_ident_1)\n\n\tdef get_station_dest_relay_ident_2(self):\n\t\treturn(self.__station_dest_relay_ident_2)\n\n\tdef get_station_dest_relay_ident_3(self):\n\t\treturn(self.__station_dest_relay_ident_3)\n\n\tdef get_station_active_state(self):\n\t\treturn(self.__station_active_state)\n\n\tdef get_station_dest_contact_admin_name(self):\n\t\treturn(self.__station_dest_contact_admin_name)\n\n\tdef get_station_dest_contact_admin_phone(self):\n\t\treturn(self.__station_dest_contact_admin_phone)\n\n\tdef get_station_dest_contact_admin_email(self):\n\t\treturn(self.__station_dest_contact_admin_email)\n\n\tdef get_station_dest_contact_admin_company(self):\n\t\treturn(self.__station_dest_contact_admin_company)\n\n\tdef get_station_dest_contact_admin_notes(self):\n\t\treturn(self.__station_dest_contact_admin_notes)\n\n\tdef get_profile_profile(self):\n\t\treturn(self.__profile_profile)\n\n\tdef get_profile_tx_mode(self):\n\t\treturn(self.__profile_tx_mode)\n\n\tdef get_profile_v_ts_type(self):\n\t\treturn(self.__profile_v_ts_type)\n\n\tdef get_profile_v_video_format(self):\n\t\treturn(self.__profile_v_video_format)\n\n\tdef get_profile_v_pid(self):\n\t\treturn(self.__profile_v_pid)\n\n\tdef get_profile_v_vbv_bitrate(self):\n\t\treturn(self.__profile_v_vbv_bitrate)\n\n\tdef get_profile_v_vbv_maxrate(self):\n\t\treturn(self.__profile_v_vbv_maxrate)\n\n\tdef get_profile_v_muxrate(self):\n\t\treturn(self.__profile_v_muxrate)\n\n\tdef get_profile_v_vbv_bufsize(self):\n\t\treturn(self.__profile_v_vbv_bufsize)\n\n\tdef get_profile_v_format(self):\n\t\treturn(self.__profile_v_format)\n\n\tdef get_profile_v_aspect_ratio(self):\n\t\treturn(self.__profile_v_aspect_ratio)\n\n\tdef get_profile_v_cbr(self):\n\t\treturn(self.__profile_v_cbr)\n\n\tdef get_profile_v_keyint(self):\n\t\treturn(self.__profile_v_keyint)\n\n\tdef get_profile_v_bframes(self):\n\t\treturn(self.__profile_v_bframes)\n\n\tdef get_profile_v_level(self):\n\t\treturn(self.__profile_v_level)\n\n\tdef get_profile_v_profile(self):\n\t\treturn(self.__profile_v_profile)\n\n\tdef get_profile_v_intra_refresh(self):\n\t\treturn(self.__profile_v_intra_refresh)\n\n\tdef get_profile_v_threads(self):\n\t\treturn(self.__profile_v_threads)\n\n\tdef get_profile_system_type(self):\n\t\treturn(self.__profile_system_type)\n\n\tdef get_profile_a_pid(self):\n\t\treturn(self.__profile_a_pid)\n\n\tdef get_profile_a_bitrate(self):\n\t\treturn(self.__profile_a_bitrate)\n\n\tdef get_profile_a_format(self):\n\t\treturn(self.__profile_a_format)\n\n\tdef get_profile_a_profile(self):\n\t\treturn(self.__profile_a_profile)\n\n\tdef get_profile_a_aac_encap(self):\n\t\treturn(self.__profile_a_aac_encap)\n\n\tdef get_profile_a_aac_profile(self):\n\t\treturn(self.__profile_a_aac_profile)\n\n\tdef get_profile_service_name(self):\n\t\treturn(self.__profile_service_name)\n\n\tdef get_profile_provider_name(self):\n\t\treturn(self.__profile_provider_name)\n\n\tdef get_profile_pmt_pid(self):\n\t\treturn(self.__profile_pmt_pid)\n\n\n\tdef get_relay_1(self, attrib):\n\t\tif attrib is 'ident':\n\t\t\treturn self.__relay_1_ident\n\t\tif attrib is 'ip':\n\t\t\treturn self.__relay_1_ip \n\t\tif attrib is'port_in':\n\t\t\treturn self.__relay_1_port_in\n\t\tif attrib is 'port_tcp':\n\t\t\treturn self.__relay_1_port_tcp\n\n\tdef get_relay_2(self, attrib):\n\t\tif attrib is 'ident':\n\t\t\treturn self.__relay_2_ident\n\t\tif attrib is 'ip':\n\t\t\treturn self.__relay_2_ip \n\t\tif attrib is 'port_in':\n\t\t\treturn self.__relay_2_port_in\n\t\tif attrib is'port_tcp':\n\t\t\treturn self.__relay_2_port_tcp\n\n\tdef get_relay_3(self, attrib):\n\t\tif attrib is 'ident':\n\t\t\treturn self.__relay_3_ident\n\t\tif attrib is 'ip':\n\t\t\treturn self.__relay_3_ip \n\t\tif attrib is 'port_in':\n\t\t\treturn self.__relay_3_port_in\n\t\tif attrib is'port_tcp':\n\t\t\treturn self.__relay_3_port_tcp\n\n\t#### OTHER OBJECT FUNCTIONS\n\n\n################# END OF CLASS DEFINITION #########################################\n####################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n########################### FUNC GET DEST STATION INFO # ############################\n##### This funtion connects to the database and loads destination station profile information\n#####\n#####\n\ndef updateDestStation (station):\n\tfrom configparser import ConfigParser\n\t### Pull db host/user/pass from config.ini\n\tparser = ConfigParser()\n\tparser.read('config.ini')\n\n\t#convert list to dictionary\n\tconfig=dict(parser.items('stationDatabase'))\n\n\t#set usable variables \t\n\tdbhost=(config.get(\"dbhost\"))\n\tdbuser=(config.get(\"dbuser\"))\n\tdbpass=(config.get(\"dbpass\"))\n\tdbname=(config.get(\"dbname\"))\n\tdbtable=(config.get(\"dbtable\"))\n\n\t#print confirmation of variables loaded\n\t##print('dbhost: ', dbhost)\n\t##print('dbuser: ', dbuser)\n\t##print('dbpass: ', dbpass)\n\t##print('dbname: ', dbname)\n\t##print('dbtable:', dbtable)\n\n\timport pymysql\n\timport pymysql.cursors\n\t## Connect to db\n\tconn=pymysql.connect(host=dbhost, user=dbuser, passwd=dbpass, db=dbname, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith conn.cursor() as cursor:\n\t\t\t## Read the record\n\t\t\tsql = \"SELECT * FROM `{}` WHERE ident=\\\"{}\\\"\".format(dbtable, station)\n\t\t\t##print(\"PySQL: \", sql)\n\t\t\tcursor.execute(sql)\n\t\t\tresult = cursor.fetchone()\n\t\t\t##print(result)\n\tfinally:\n\t\tconn.close\n\n\t## update channelObj (object with all destination station properties)\n\n\t\n\t##channel.set_station_dest_ip(result.get('ip'))\n\tchannel.set_station_dest_ip(result.get('ip'))\n\tchannel.set_station_dest_mode(result.get('mode'))\n\tchannel.set_station_dest_profile_pref(result.get('profile_pref'))\n\tchannel.set_station_dest_tx_limit_mbps(result.get('tx_limit_mbps'))\n\tchannel.set_station_dest_rx_limit_mbps(result.get('rx_limit_mbps'))\n\tchannel.set_station_dest_rx_port(result.get('rx_port'))\n\tchannel.set_station_dest_relay_ident_1(result.get('relay_ident_1'))\n\tchannel.set_station_dest_relay_ident_2(result.get('relay_ident_2'))\n\tchannel.set_station_dest_relay_ident_3(result.get('relay_ident_3'))\n\tchannel.set_station_dest_contact_admin_name(result.get('contact_admin_name'))\n\tchannel.set_station_dest_contact_admin_phone(result.get('contact_admin_phone'))\n\tchannel.set_station_dest_contact_admin_email(result.get('contact_admin_email'))\n\tchannel.set_station_dest_contact_admin_company(result.get('contact_admin_company'))\n\tchannel.set_station_dest_contact_admin_notes(result.get('contact_admin_notes'))\n####################################################################################\n####################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n########################### FUNC GET SOURCE STATION INFO ############################\n##### This funtion connects to the database and loads source station profile information\n#####\n#####\n\n\ndef updateSrcStation (station):\n\tfrom configparser import ConfigParser\n\t### Pull db host/user/pass from config.ini\n\tparser = ConfigParser()\n\tparser.read('config.ini')\n\n\t#convert list to dictionary\n\tconfig=dict(parser.items('stationDatabase'))\n\n\t#set usable variables \t\n\tdbhost=(config.get(\"dbhost\"))\n\tdbuser=(config.get(\"dbuser\"))\n\tdbpass=(config.get(\"dbpass\"))\n\tdbname=(config.get(\"dbname\"))\n\tdbtable=(config.get(\"dbtable\"))\n\n\t#print confirmation of variables loaded\n\t##print('dbhost: ', dbhost)\n\t##print('dbuser: ', dbuser)\n\t##print('dbpass: ', dbpass)\n\t##print('dbname: ', dbname)\n\t##print('dbtable:', dbtable)\n\n\timport pymysql\n\timport pymysql.cursors\n\t## Connect to db\n\n\tconn=pymysql.connect(host=dbhost, user=dbuser, passwd=dbpass, db=dbname, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith conn.cursor() as cursor:\n\t\t\t## Read the record\n\t\t\tsql = \"SELECT * FROM `{}` WHERE ident=\\\"{}\\\"\".format(dbtable, src_station)\n\t\t\t##print(\"PySQL: \", sql)\n\t\t\tcursor.execute(sql)\n\t\t\tresult = cursor.fetchone()\n\t\t\t##print(result)\n\tfinally:\n\t\tconn.close\n\n\t## update channelObj (object with all destination station properties)\n\n\t\n\t##channel.set_station_dest_ip(result.get('ip'))\n\tchannel.set_station_src_ip(result.get('ip'))\n\tchannel.set_station_src_mode(result.get('mode'))\n\tchannel.set_station_src_profile_pref(result.get('profile_pref'))\n\tchannel.set_station_src_tx_limit_mbps(result.get('tx_limit_mbps'))\n\tchannel.set_station_src_rx_limit_mbps(result.get('rx_limit_mbps'))\n\tchannel.set_station_src_rx_port(result.get('rx_port'))\n\tchannel.set_station_src_relay_ident_1(result.get('relay_ident_1'))\n\tchannel.set_station_src_relay_ident_2(result.get('relay_ident_2'))\n\tchannel.set_station_src_relay_ident_3(result.get('relay_ident_3'))\n\tchannel.set_station_src_contact_admin_name(result.get('contact_admin_name'))\n\tchannel.set_station_src_contact_admin_phone(result.get('contact_admin_phone'))\n\tchannel.set_station_src_contact_admin_email(result.get('contact_admin_email'))\n\tchannel.set_station_src_contact_admin_company(result.get('contact_admin_company'))\n\tchannel.set_station_src_contact_admin_notes(result.get('contact_admin_notes'))\n####################################################################################\n####################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n########################## FUNC GET PROFILE OVERRIDE INFO ############################\n##### This funtion connects to the database and loads profile information\n#####\n#####\n\ndef updateSendProfile(profile):\n\tfrom configparser import ConfigParser\n\t### Pull db host/user/pass from config.ini\n\tparser = ConfigParser()\n\tparser.read('config.ini')\n\n\t#convert list to dictionary\n\tconfig=dict(parser.items('profileDatabase'))\n\n\t#set usable variables \t\n\tdbhost=(config.get(\"dbhost\"))\n\tdbuser=(config.get(\"dbuser\"))\n\tdbpass=(config.get(\"dbpass\"))\n\tdbname=(config.get(\"dbname\"))\n\tdbtable=(config.get(\"dbtable\"))\n\n\timport pymysql\n\timport pymysql.cursors\n\t## Connect to db\n\tconn=pymysql.connect(host=dbhost, user=dbuser, passwd=dbpass, db=dbname, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith conn.cursor() as cursor:\n\t\t\t## Read the record\n\t\t\tsql = \"SELECT * FROM `{}` WHERE profile=\\\"{}\\\"\".format(dbtable, profile)\n\t\t\t##print(\"PySQL: \", sql)\n\t\t\tcursor.execute(sql)\n\t\t\tresult = cursor.fetchone()\n\t\t\t##print(result)\n\tfinally:\n\t\tconn.close\n\n\n\tchannel.set_profile_tx_mode(result.get('tx_mode'))\n\tchannel.set_profile_v_ts_type(result.get('v_ts_type'))\n\tchannel.set_profile_v_video_format(result.get('v_video_format'))\n\tchannel.set_profile_v_pid(result.get('v_pid'))\n\tchannel.set_profile_v_vbv_bitrate(result.get('v_vbv_bitrate'))\n\tchannel.set_profile_v_vbv_maxrate(result.get('v_vbv_maxrate'))\n\tchannel.set_profile_v_muxrate(result.get('v_muxrate'))\n\tchannel.set_profile_v_vbv_bufsize(result.get('v_vbv_bufsize'))\n\tchannel.set_profile_v_format(result.get('v_format'))\n\tchannel.set_profile_v_aspect_ratio(result.get('v_aspect_ratio'))\n\tchannel.set_profile_v_cbr(result.get('v_cbr'))\n\tchannel.set_profile_v_keyint(result.get('v_keyint'))\n\tchannel.set_profile_v_bframes(result.get('v_bframes'))\n\tchannel.set_profile_v_level(result.get('v_level'))\n\tchannel.set_profile_v_profile(result.get('v_profile'))\n\tchannel.set_profile_v_intra_refresh(result.get('v_intra_refresh'))\n\tchannel.set_profile_v_threads(result.get('v_threads'))\n\tchannel.set_profile_system_type(result.get('system_type'))\n\tchannel.set_profile_a_pid(result.get('a_pid'))\n\tchannel.set_profile_a_bitrate(result.get('a_bitrate'))\n\tchannel.set_profile_a_format(result.get('a_format'))\n\tchannel.set_profile_a_profile(result.get('a_profile'))\n\tchannel.set_profile_a_aac_encap(result.get('a_aac_encap'))\n\tchannel.set_profile_a_aac_profile(result.get('a_aac_profile'))\n\tchannel.set_profile_service_name(result.get('service_name'))\n\tchannel.set_profile_provider_name(result.get('provider_name'))\n############## END FUNC GET PROFILE OVERRIDE INFO #############################\n###############################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n############## LOOKUP RELAY INFORMATION FOR STREAM SPLITTER ##################\n####### This funtion:\n####### connects to the centralDB and looks up relay information (IP, PORTS, etc)\n\ndef updateRelays():\n\tfrom configparser import ConfigParser\n\t### Pull db host/user/pass from config.ini\n\tparser = ConfigParser()\n\tparser.read('config.ini')\n\n\t#convert list to dictionary\n\tconfig=dict(parser.items('relaysDatabase'))\n\n\t#set usable variables \t\n\tdbhost=(config.get(\"dbhost\"))\n\tdbuser=(config.get(\"dbuser\"))\n\tdbpass=(config.get(\"dbpass\"))\n\tdbname=(config.get(\"dbname\"))\n\tdbtable=(config.get(\"dbtable\"))\n\n\timport pymysql\n\timport pymysql.cursors\n\t## Connect to db\n\tconn=pymysql.connect(host=dbhost, user=dbuser, passwd=dbpass, db=dbname, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith conn.cursor() as cursor:\n\t\t\t## Read the record\n\t\t\tsql = \"SELECT * FROM `{}` WHERE ident=\\\"{}\\\"\".format(dbtable, channel.get_station_src_relay_ident_1())\n\t\t\t##print(\"PySQL: \", sql)\n\t\t\tcursor.execute(sql)\n\t\t\trelay1 = cursor.fetchone()\n\n\t\t\tsql = \"SELECT * FROM `{}` WHERE ident=\\\"{}\\\"\".format(dbtable, channel.get_station_src_relay_ident_2())\n\t\t\tcursor.execute(sql)\n\t\t\trelay2 = cursor.fetchone()\n\n\t\t\tsql = \"SELECT * FROM `{}` WHERE ident=\\\"{}\\\"\".format(dbtable, channel.get_station_src_relay_ident_3())\n\t\t\tcursor.execute(sql)\n\t\t\trelay3 = cursor.fetchone()\n\n\t\t\t##print(result)\n\t\t\tprint('Relay1: ',relay1)\n\t\t\tprint('Relay2: ',relay2)\n\t\t\tprint('Relay3: ',relay3)\n\n\t\t\tif relay1 is not None:\n\t\t\t\tprint ('setting relay 1 object info')\n\t\t\t\tchannel.set_relay_info('1', relay1.get('ident'), relay1.get('ip'), relay1.get('port_in'), relay1.get('port_tcp'))\n\n\t\t\tif relay2 is not None:\n\t\t\t\tchannel.set_relay_info('2', relay2.get('ident'), relay2.get('ip'), relay2.get('port_in'), relay2.get('port_tcp'))\n\t\t\t\tprint ('setting relay 2 object info')\n\n\t\t\tif relay3 is not None:\n\t\t\t\tchannel.set_relay_info('3', relay3.get('ident'), relay3.get('ip'), relay3.get('port_in'), relay3.get('port_tcp'))\n\t\t\t\tprint ('setting relay 3 object info')\n\n\n\n\tfinally:\n\t\tconn.close\n\n\n#channel.set_profile_tx_mode(result.get('tx_mode'))\n\n\n############### END FUNC LOOKUK RELAY INFORMATION #############################\n###############################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n############## FUNC DISPLAY CURRENT OBJECT DATA ###############################\n####### This funtion:\n####### displace the channelObj Object data for the instance\n####### that is passed into it\n\ndef displaychannelObj(instance):\n\tprint('###################################################################')\n\tprint('-------------------------------------------------------------------')\n\tprint('multi-send will always deliver to the stream-splitter (10.0.10.2)')\n\tprint('-------------------------------------------------------------------')\n\tprint('###################################################################')\n\tprint()\n\tprint()\n\tprint('############## SOURCE STATION INFORMATION ##################')\n\tprint('IDENT: ', instance.get_station_src_ident())\n\tprint('IP: ', instance.get_station_src_ip())\n\tprint('MODE: ', instance.get_station_src_mode())\n\tprint('SENDprof:', instance.get_station_src_profile_pref())\n\tprint('TX lim: ', instance.get_station_src_tx_limit_mbps())\n\tprint('RX lim: ', instance.get_station_src_rx_limit_mbps())\n\tprint('RX port: ', instance.get_station_src_rx_port())\n\tprint('Relay 1: ', instance.get_station_src_relay_ident_1())\n\tprint('Relay 2: ', instance.get_station_src_relay_ident_2())\n\tprint('Relay 3: ', instance.get_station_src_relay_ident_3())\n\tprint('########### Displaying Source Contact Info ###########')\n\tprint('NAME: ', instance.get_station_src_contact_admin_name())\n\tprint('PHONE: ', instance.get_station_src_contact_admin_phone())\n\tprint('EMAIL: ', instance.get_station_src_contact_admin_email())\n\tprint('COMPANY ', instance.get_station_src_contact_admin_company())\n\tprint('NOTES: ', instance.get_station_src_contact_admin_notes())\n\tprint()\n\tprint()\n\tprint('############ DESTINATION STATION INFORMATION ###############')\n\tprint('IDENT: ', instance.get_station_dest_ident())\n\tprint('IP: ', instance.get_station_dest_ip())\n\tprint('MODE: ', instance.get_station_dest_mode())\n\tprint('SENDprof:', instance.get_station_dest_profile_pref())\n\tprint('TX lim: ', instance.get_station_dest_tx_limit_mbps())\n\tprint('RX lim ', instance.get_station_dest_rx_limit_mbps())\n\tprint('RX port: ', instance.get_station_dest_rx_port())\n\tprint('Relay 1: ', instance.get_station_dest_relay_ident_1())\n\tprint('Relay 2: ', instance.get_station_dest_relay_ident_2())\n\tprint('Relay 3: ', instance.get_station_dest_relay_ident_3())\n\tprint('########### Displaying Desination Contact Info ###########')\n\tprint('NAME: ', instance.get_station_dest_contact_admin_name())\n\tprint('PHONE: ', instance.get_station_dest_contact_admin_phone())\n\tprint('EMAIL: ', instance.get_station_dest_contact_admin_email())\n\tprint('COMPANY: ', instance.get_station_dest_contact_admin_company())\n\tprint('NOTES: ', instance.get_station_dest_contact_admin_notes())\n\tprint()\n\tprint()\n\tprint('########### Displaying Profile Override data ###########')\n\tprint('TX MODE: ', instance.get_profile_tx_mode())\n\tprint('TX TYPE: ', instance.get_profile_v_ts_type())\n\tprint('VIDFORMAT', instance.get_profile_v_video_format())\n\tprint('VPID: ', instance.get_profile_v_pid())\n\tprint('VBR: ', instance.get_profile_v_vbv_bitrate())\n\tprint('VMAX BIT ', instance.get_profile_v_vbv_maxrate())\n\tprint('MUXRATE: ', instance.get_profile_v_muxrate())\n\tprint('VBUF: ', instance.get_profile_v_vbv_bufsize())\n\tprint('VFORMAT: ', instance.get_profile_v_format())\n\tprint('VASPECT: ', instance.get_profile_v_aspect_ratio())\n\tprint('CBRMODE: ', instance.get_profile_v_cbr())\n\tprint('KEYINT: ', instance.get_profile_v_keyint())\n\tprint('VBFRAME: ', instance.get_profile_v_bframes())\n\tprint('VLEVEL: ', instance.get_profile_v_level())\n\tprint('VPROFILE:', instance.get_profile_v_profile())\n\tprint('INTRAREF:', instance.get_profile_v_intra_refresh())\n\tprint('VTHREADS:', instance.get_profile_v_threads())\n\tprint('SYSTYPE: ', instance.get_profile_system_type())\n\tprint('APID ', instance.get_profile_a_pid())\n\tprint('ABIT: ', instance.get_profile_a_bitrate())\n\tprint('AFORMAT: ', instance.get_profile_a_format())\n\tprint('APROFILE:', instance.get_profile_a_profile())\n\tprint('AACPROF: ', instance.get_profile_a_aac_profile())\n\tprint('AENCAP: ', instance.get_profile_a_aac_encap())\n\tprint('SERVICE: ', instance.get_profile_service_name())\n\tprint('PROVIDER:', instance.get_profile_provider_name())\n\tprint()\n\tprint()\n\tprint('################## USING RELAYS ######################')\n\tprint()\n\tprint('- RELAY 1 -----------------------------------')\n\tprint('Ident: ', channel.get_relay_1('ident'))\n\tprint('IP: ', channel.get_relay_1('ip'))\n\tprint('Port in: ', channel.get_relay_1('port_in'))\n\tprint('TCP port:', channel.get_relay_1('port_tcp'))\n\tprint()\n\n\tprint()\n\tprint('- RELAY 2 -----------------------------------')\n\tprint('Ident: ', channel.get_relay_2('ident'))\n\tprint('IP: ', channel.get_relay_2('ip'))\n\tprint('Port in: ', channel.get_relay_2('port_in'))\n\tprint('TCP port:', channel.get_relay_2('port_tcp'))\n\tprint()\n\n\tprint()\n\tprint('- RELAY 3 -----------------------------------')\n\tprint('IP: ', channel.get_relay_3('ip'))\n\tprint('Port in: ', channel.get_relay_3('port_in'))\n\tprint('TCP port:', channel.get_relay_3('port_tcp'))\n\tprint()\n\n\n\tprint('##########################################################')\n\n############ END FUNC DISPLAY CURRENT OBJECT DATA #############\n###############################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n########################## AGGREGARTP ENTRYPOINT CONSTRUCTOR #######################\n##### construct the shell script that will be transferred to host files to begin the \n##### aggregartp stream splitter \n#####\n\ndef buildAggregartpEntrypoint():\n\tprint ('** ADVISORY ** stream splitter entrypoint constructor assumes incoming port from OBE stream is \\'4444\\'')\n\n\tfrom subprocess import call\n\timport stat\n\n\tsplitter_start_docker = open(\"../stream-split/hostfiles/start-aggregartp.sh\", \"wb\")\n\n\tsplitter_start_docker.write(bytes(\"#!/bin/bash\\n\", 'UTF-8'))\n\tsplitter_start_docker.write(bytes('aggregartp @:4444 ', 'UTF-8'))\n\tif channel.get_relay_1('ip') is not None:\n\t\tsplitter_start_docker.write(bytes('{0}:{1} '.format(channel.get_relay_1(\"ip\"), channel.get_relay_1(\"port_in\")), 'UTF-8'))\n\tif channel.get_relay_2('ip') is not None:\n\t\tsplitter_start_docker.write(bytes('{0}:{1} '.format(channel.get_relay_2(\"ip\"), channel.get_relay_2(\"port_in\")), 'UTF-8'))\n\tif channel.get_relay_3('ip') is not None:\n\t\tsplitter_start_docker.write(bytes('{0}:{1}\\n '.format(channel.get_relay_3(\"ip\"), channel.get_relay_3(\"port_in\")), 'UTF-8'))\n\t\t\n\n\tsplitter_start_docker.close()\n\n############################ END BUILD OBE RUNNER ############################\n#################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n########################## FUNC BUILD OBE RUNNER SCRIPT ############################\n##### construct the shell script that will be transferred to host files to run OBE\n##### from within the docker environment.\n#####\ndef buildObeRunner():\n\timport stat\n\t## open file for writing\n\tobe_send_file = open(\"hostfiles/start-obe.sh\", \"wb\")\n\n\tobe_send_file.write(bytes(\"#!/bin/bash\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"NAME=obe\\n\", 'UTF-8'))\n\t######## '-d' here \n\tobe_send_file.write(bytes(\"screen -d -m -S $NAME obecli\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"sleep 2\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set input decklink\" + r\"\\012\" +\"\\\"\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set input opts card-idx=0\" + r\"\\012\" +\"\\\"\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set input opts video-format=%s\" % channel.get_profile_v_video_format() + r'\\012' +'\\\"\\n' , 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set input opts video-channel=sdi\" + r\"\\012\" +\"\\\"\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set input opts audio-channel=embedded\" + r\"\\012\" +\"\\\"\\n\" ,'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"set obe opts system-type=lowestlatency\" + r\"\\012\" +\"\\\"\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"probe input\" + r\"\\012\" +\"\\\"\\n\", 'UTF-8'))\n\tobe_send_file.write(bytes(\"sleep 1\\n\", 'UTF-8'))\n\n\t#### Video \n\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\tobe_send_file.write(bytes('$\\\"set stream opts 0:pid=%s' % channel.get_profile_v_pid() + ',' ,'UTF-8'))\n\t\n\tif(channel.get_profile_v_vbv_maxrate() is not None):\n\t\tobe_send_file.write(bytes('vbv-maxrate=%s' % channel.get_profile_v_vbv_maxrate() + ',' ,'UTF-8'))\n\n\tif(channel.get_profile_v_vbv_bitrate() is not None):\n\t\tobe_send_file.write(bytes('bitrate=%s' % channel.get_profile_v_vbv_bitrate() + ',' ,'UTF-8'))\n\t\n\tif(channel.get_profile_v_keyint() is not None):\n\t\tobe_send_file.write(bytes('keyint=%s' % channel.get_profile_v_keyint() + ',' ,'UTF-8'))\n\t\n\tif(channel.get_profile_v_bframes() is not None):\n\t\tobe_send_file.write(bytes('bframes=%s' % channel.get_profile_v_bframes() + ',' ,'UTF-8'))\n\t\n\tif(channel.get_profile_v_threads() is not None):\n\t\tobe_send_file.write(bytes('threads=%s' % channel.get_profile_v_threads() + ',' ,'UTF-8'))\n\n\tif(channel.get_profile_system_type() is not None):\n\t\tobe_send_file.write(bytes('$\\\"set obe opts %s' % channel.get_profile_system_type() + ',' ,'UTF-8'))\n\n\tif(channel.get_profile_v_format() is not None):\n\t\tobe_send_file.write(bytes('format=%s' % channel.get_profile_v_format() + ',' , 'UTF-8'))\n\t\n\tif(channel.get_profile_v_profile() is not None):\n\t\tobe_send_file.write(bytes('profile=%s' % channel.get_profile_v_profile() + ',' , 'UTF-8'))\n\t\n\tif(channel.get_profile_v_level() is not None):\n\t\tobe_send_file.write(bytes('level=%s' % channel.get_profile_v_level() + ',' , 'UTF-8'))\n\t\n\tif(channel.get_profile_v_aspect_ratio() is not None):\n\t\tobe_send_file.write(bytes('aspect-ratio=%s' % channel.get_profile_v_aspect_ratio() + ',' , 'UTF-8'))\n\t\n\tif(channel.get_profile_v_intra_refresh() is not None):\n\t\tobe_send_file.write(bytes('intra-refresh=%s' % channel.get_profile_v_intra_refresh() , 'UTF-8'))\n\n\tobe_send_file.write(bytes( r'\\012' + '\\\"\\n' , 'UTF-8'))\t\n\n\t\n\n\t#### Audio\n\n\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\tobe_send_file.write(bytes('$\\\"set stream opts 1:pid=%s' % channel.get_profile_a_pid() + ',' ,'UTF-8'))\n\t\n\tif(channel.get_profile_a_bitrate() is not None):\n\t\tobe_send_file.write(bytes('bitrate=%s' % channel.get_profile_a_bitrate(),'UTF-8'))\n\t\tif(channel.get_profile_a_format() is not None):\n\t\t\tobe_send_file.write(bytes(',','UTF-8'))\n\t\telse:\n\t\t\tobe_send_file.write(bytes( r'\\012' +'\\\"\\n' ,'UTF-8'))\n\n\tif(channel.get_profile_a_format() is not None):\n\t\tobe_send_file.write(bytes('format=%s' % channel.get_profile_a_format(),'UTF-8'))\n\t\tif(channel.get_profile_a_profile() is not None):\n\t\t\tobe_send_file.write(bytes(',','UTF-8'))\n\t\telse:\n\t\t\tobe_send_file.write(bytes( r'\\012' + 'boom\\\"\\n' ,'UTF-8'))\n\n\tif(channel.get_profile_a_profile() is not None):\n\t\tobe_send_file.write(bytes('aac-profile=%s' % channel.get_profile_a_profile(),'UTF-8'))\n\t\tif(channel.get_profile_a_aac_encap() is not None):\n\t\t\tobe_send_file.write(bytes(',','UTF-8'))\n\t\telse:\n\t\t\tobe_send_file.write(bytes( r'\\012' + '\\\"\\n' ,'UTF-8'))\n\n\tif(channel.get_profile_a_aac_encap() is not None):\n\t\tobe_send_file.write(bytes('aac-encap=%s' % channel.get_profile_a_aac_encap() + r'\\012' +'\\\"\\n','UTF-8'))\t\n\n\t#####\n\t\n\tif(channel.get_profile_pmt_pid() is not None):\n\t\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\t\tobe_send_file.write(bytes('$\\\"set stream opts 0:pid=%s' % channel.get_profile_pmt_pid() + r'\\012' +'\\\"\\n','UTF-8'))\n\n\tif(channel.get_profile_v_ts_type() is not None):\n\t\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\t\tobe_send_file.write(bytes('$\\\"set muxer opts ts-type=%s' % channel.get_profile_v_ts_type() + \",\" ,'UTF-8'))\n\n\tif(channel.get_profile_v_muxrate() is not None):\n\t\tobe_send_file.write(bytes('ts-muxrate=%s' % (channel.get_profile_v_muxrate() * 1000) + r'\\012' +'\\\"\\n' ,'UTF-8'))\n\n\n\tif(channel.get_profile_tx_mode() is not None):\n\t\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\t\tobe_send_file.write(bytes('$\\\"set output %s' % channel.get_profile_tx_mode() + r'\\012' +'\\\"\\n','UTF-8'))\n\n\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\tobe_send_file.write(bytes('$\\\"set outputs 1' + r'\\012' + '\\\"\\n','UTF-8'))\n\t\n\tif(channel.get_profile_tx_mode() is not None):\n\t\tobe_send_file.write(bytes('screen -p 0 -S $NAME -X stuff ', 'UTF-8'))\n\t\tobe_send_file.write(bytes('$\\\"set output opts 0:target={0}://{1}:{2}'.format(channel.get_profile_tx_mode(), stream_splitter_ip, stream_splitter_port) + r'\\012' +'\\\"\\n', 'UTF-8'))\n\n\t\tobe_send_file.write(bytes(\"screen -p 0 -S $NAME -X stuff $\\\"start\" + r'\\012' + \"\\\"\\n\", 'UTF-8'))\n\n\tobe_send_file.write(bytes(\"screen -r\\n\", 'UTF-8'))\n\n\t\t\t### lowlat setting could be auto, but better to be in db and pull into script\n\n\tobe_send_file.close()\n\n\n\t##os.chmod('hostfiles/start-obe.sh', stat.S_IXOTH)\n\n################### END BUILD OBE RUNNER ###################\n###############################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n################################ INITIATE OBE DOCKER #################################\n##### Luach Obe Docker and pass all variables into docker\n##### (THIS IS NOT CURRENTLY WORKING - calling initiateObeDockerTemp until this can be \n##### sorted out.\n\ndef initiateObeDocker():\n\tprint()\n\tprint('################################## Launching OBE DOCKER ##########################################')\n\tfrom configparser import ConfigParser\n\t### Pull dcocker image name from config.ini\n\tparser2 = ConfigParser()\n\tparser2.read('config.ini')\n\t#convert list to dictionary\n\tconfig=dict(parser2.items('dockerImages'))\n\t#set usable variables \t\n\tobeDockerName=(config.get(\"obesenddocker\"))\n\tprint ('using docker: ' + obeDockerName)\n\n\n\timport docker\n\tdocker = docker.from_env() \n\n\tcontainer = docker.create_container(\n\t\timage = obeDockerName,\n\t\tstdin_open=True,\n\t\ttty=True,\n\t\tcommand='/bin/bash/',\n\t\tvolumes=['/home/default/hostfiles', '/home/default/recorded-video'],\n\n\t\thost_config=docker.create_host_config(binds={\n\t\t\t'/home/kevin/docker/obe-rt-send/hostfiles': {\n\t\t\t\t'bind': '/home/default/hostfiles',\n\t\t\t\t'mode': 'rw',\n\t\t\t},\n\t\t\t'/home/kevin/recorded-video': {\n\t\t\t\t'bind': '/home/default/recorded-video',\n\t\t\t\t'mode': 'rw'\n\n\t\t\t}\n\n\t\t\t})\n\n\t\t)\n\n\tdocker.start(containter)\n\n############################### END INITIATE OBE DOCKER ##############################\n#######################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n################################ INITIATE OBE DOCKER (TEMP) #################################\n##### initiate docker by forming a bash script and executing it.\n##### (Eventually, we should use the docker api for python to run docker from within the py)\n##### \n\ndef initiateObeDockerTemp():\n\n\tprint('Initiating Obe Docker (using temporary function)')\n\n\timport subprocess\n\timport stat\n\n\n\tobe_start_docker = open(\"hostfiles/start-docker.sh\", \"wb\")\n\n\tobe_start_docker.write(bytes(\"#!/bin/bash\\n\", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"docker run \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"--network=\\\"split\\\" \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"--ip=\\\"10.0.10.3\\\" \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"--name=\\\"multi-send\\\" \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"-v /home/\" + user + \"/apps/multi-send/hostfiles:/home/default/hostfiles \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"-v /home/\" + user + \"/recorded-video:/home/default/recorded-video \", 'UTF-8'))\n\t##obe_start_docker.write(bytes(\"--entrypoint=\\\"/bin/bash\\\" \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"-itd \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"--device /dev/blackmagic/io0 \", 'UTF-8'))\n\tobe_start_docker.write(bytes(\"pmw1/direct-send \", 'UTF-8'))\n\n\tobe_start_docker.close()\n\n\tos.chmod('hostfiles/start-docker.sh', stat.S_IXOTH)\n\tproc = subprocess.Popen('sudo hostfiles/start-docker.sh', shell=True)\n\n\t\n\n\t##call(['bash', 'hostfiles/start-docker.sh'])\n\t##os.system(\"start hostfiles/start-docker.sh\")\n\n############################### END INITIATE OBE DOCKER ##############################\n######################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n################################ INITIATE AGGRERTP ##################################\n##### \n##### \n##### \n\ndef initiateAggregartp():\n\tprint('Initiating Aggregartp Docker')\n\n\timport subprocess\n\timport stat\n\n\tstream_splitter_start_docker = open(\"../stream-split/start-stream-split.sh\", \"wb\")\n\tstream_splitter_start_docker.write(bytes(\"#!/bin/bash\\n\", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"sudo docker kill stream-split\\n\", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"sudo docker rm -f stream-split\\n\", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"echo Inter-Docker user: $USER\\n\", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"sudo docker run \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"-v $HOME/apps/stream-split/hostfiles/:/hostfiles \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"-p 4444:4444/udp \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"-p 3005:3005/tcp \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"--name=\\\"stream-split\\\" \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"--network=\\\"split\\\" \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"--ip=\\\"10.0.10.2\\\" \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"--privileged -i -t -d \", 'UTF-8'))\n\tstream_splitter_start_docker.write(bytes(\"pmw1/split-rtp\\n\", 'UTF-8'))\n\n\n\tstream_splitter_start_docker.close()\n\n\tos.chmod('../stream-split/start-stream-split.sh', stat.S_IXOTH)\n\tproc = subprocess.Popen('sudo ../stream-split/start-stream-split.sh', shell=True)\n\n\t##call(['bash', '../stream-split/start-stream-split.sh'])\n\n############################### END INITIATE AGGREGARTP ############################\n######################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n################################ SET ACTIVE STATUS #################################\n##### connect to database and change change status to active\n##### \n\ndef setActive():\n\tfrom configparser import ConfigParser\n\t### Pull db host/user/pass from config.ini\n\tparser = ConfigParser()\n\tparser.read('config.ini')\n\n\t#convert list to dictionary\n\tconfig=dict(parser.items('stationDatabase'))\n\n\t#set usable variables \t\n\tdbhost=(config.get(\"dbhost\"))\n\tdbuser=(config.get(\"dbuser\"))\n\tdbpass=(config.get(\"dbpass\"))\n\tdbname=(config.get(\"dbname\"))\n\tdbtable=(config.get(\"dbtable\"))\n\n\timport pymysql\n\timport pymysql.cursors\n\t## Connect to db\n\tconn=pymysql.connect(host=dbhost, user=dbuser, passwd=dbpass, db=dbname, charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor)\n\ttry:\n\t\twith conn.cursor() as cursor:\n\t\t\t## Read the record\n\t\t\tsql = \"UPDATE {} SET active_state=\\'1\\' WHERE ident=\\'{}\\'\".format(dbtable, station)\n\t\t\tprint(sql)\n\t\t\t##print(\"PySQL: \", sql)\n\t\t\tcursor.execute(sql)\n\t\t\tresult = cursor.fetchone()\n\t\t\t##print(result)\n\tfinally:\n\t\tconn.close\n\n############################### END SET ACTIVE STATE ##############################\n######################################################################################\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n######################################################################################\n############################ START EXECUTING THINGS #################################\n\nchannel = \"multisend\"\n\n## if channel instance exists, print ident and update target station\n## by calling the updateDestStation function\nif (channel):\n\n\t## update source station info in channel (channelOBJ) class\n\t## updateSrcStation(src_station)\n\n\t## update target station info in channel (channelObj) class\t\n\t## updateDestStation(dest_station) not neccessary in relay mode\n\t#updateDestStation(dest_station)\n\n\t## lookup relay information and set channel object class with relay data\n\t## updateRelays()\n\n\n\t\n\t## update profile information if override is specified\n\tif(profile is not None):\n\t\tprint(\"Profile override (based on start flag): \", profile)\n\t\tupdateSendProfile(profile)\n\telif(channel.get_station_src_profile_pref()):\n\t\tprint(\"Profile override (based on DB source station definition)\", channel.get_station_src_profile_pref())\n\t\tupdateSendProfile(channel.get_station_src_profile_pref())\n\telse:\n\t\tprint(\"No Profile overrides detected\")\n\n\n\n\tdisplaychannelObj(channel)\n\n\t## bufld the entrypoint shell script for the stream-splitter (aggreartp)\n\tbuildAggregartpEntrypoint()\n\n\t## build shell file to execute obe\n\tbuildObeRunner()\n\n\t## build aggregartp launch shell script (entrypoint script for docker pmw1/split-rtp)\n\tinitiateAggregartp()\n\n\t#initiateObeDocker()\n\tinitiateObeDockerTemp()\n\n\t##set active status\n\t##setActive()\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"splitsend.py","file_name":"splitsend.py","file_ext":"py","file_size_in_byte":50277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"68113773","text":"import pandas as pd\nimport numpy as np\nimport pandas_datareader.data as pdb\nfrom fbprophet import Prophet\nimport datetime\nfrom flask import Flask, render_template, url_for , make_response\nfrom flask import request, redirect\nfrom flask_bootstrap import Bootstrap\nimport csv\nfrom itertools import zip_longest\nimport pygal\nfrom sklearn.svm import SVR\nfrom sklearn import preprocessing, svm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nimport sys\n\n\napp = Flask(__name__)\nBootstrap(app)\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n\n\n\ndef fetch_data(companyname, start, end):\n return pdb.DataReader(companyname, 'yahoo', start, end)\n\n\ndef get_stock_data(companyname):\n\n print(\"Fetching stock prices to train model \", companyname)\n\n #Starting date provided. We are taking 1 year data as. of now\n start = datetime.datetime(2018, 1, 1)\n end = datetime.datetime(2018, 11, 1)\n\n try:\n data = fetch_data(companyname, start, end)\n except Exception as e:\n print(\"You have entered either invalid company stock tinker symbol or invalid no.of days\"+ str(e))\n sys.exit()\n return data\n\n\ndef prophet_model(stockhistory,num_days):\n stock_data = stockhistory.filter(['Close'])\n\n # Prophet would need a feature column ds. Hence creating a new column with name ds which is the Date feature.\n stock_data['ds'] = stock_data.index\n\n # log transform the ‘Close’ variable to convert non-stationary data to stationary.\n stock_data['y'] = np.log(stock_data['Close'])\n\n # Using the Prophet model for analysis\n clf = Prophet()\n clf.fit(stock_data)\n\n ending_stock_price = stock_data['Close'][-1]\n\n # num_days = 10\n future = clf.make_future_dataframe(periods=num_days)\n forecast = clf.predict(future)\n\n #print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())\n\n # Prophet plots the observed values of our time series (the black dots), the forecasted values (blue line) and\n # the uncertainty intervalsof our forecasts (the blue shaded regions).\n\n #forecast_plot = clf.plot(forecast)\n #forecast_plot.show()\n\n # make the vizualization a little better to understand\n stock_data.set_index('ds', inplace=True)\n forecast.set_index('ds', inplace=True)\n # date = df['ds'].tail(plot_num)\n\n stock_visual = stock_data.join(forecast[['yhat', 'yhat_lower', 'yhat_upper']], how='outer')\n # Visualize the original values .. non logarithmic.\n stock_visual['yhat_scaled'] = np.exp(stock_visual['yhat'])\n\n actual_data = stock_visual.Close.apply(lambda x: round(x, 2))\n\n forecasted_data = stock_visual.yhat_scaled.apply(lambda x: round(x, 2))\n\n date = future['ds']\n\n d = [date, actual_data, forecasted_data]\n\n # predictions = pd.DataFrame(np.column_stack([date, actual_data, forecasted_data]), columns=['Date', 'Actual_Price','Predicted_Price'])\n\n readcsvdata = zip_longest(*d, fillvalue='')\n with open('predictions.csv', 'w', encoding=\"ISO-8859-1\", newline='') as myfile:\n wr = csv.writer(myfile)\n wr.writerow((\"Date\", \"Actual_price\", \"Forecasted_Price\"))\n wr.writerows(readcsvdata)\n myfile.close()\n\n graph = pygal.Line()\n graph.title = '%Prophet Model%'\n graph.x_labels = date\n graph.add('Actual data', actual_data)\n graph.add('Forecasted data', forecasted_data)\n graph_data = graph.render_data_uri()\n\n df = pd.read_csv('Predictions.csv')\n df = df[['Date', 'Forecasted_Price']]\n df = df[-num_days:]\n df['Date_new'] = pd.to_datetime(df['Date'])\n df['Future_date'] = df['Date_new'].dt.strftime('%Y-%m-%d')\n df = df[['Future_date', 'Forecasted_Price']]\n df.set_index('Future_date', inplace=True)\n suggestion = \"Buy\" if df['Forecasted_Price'][0] > ending_stock_price else \"Sell\"\n\n return graph_data, df, suggestion, d\n\ndef feature(df,date_column):\n df[date_column] = df[date_column].dt.strftime('%Y-%m-%d')\n df['day'] = df[date_column].apply(lambda x: x.split('-')[2]).astype(int)\n df['month'] = df[date_column].apply(lambda x: x.split('-')[1]).astype(int)\n df['year'] = df[date_column].apply(lambda x: x.split('-')[0]).astype(int)\n df = df[['day', 'month', 'year']]\n X= np.array(df)\n dates = np.reshape(X, (len(X), 3))\n return dates\n\ndef get_features(df,date_column,ylabel):\n\n df[date_column] = df[date_column].dt.strftime('%Y-%m-%d')\n df['day'] = df[date_column].apply(lambda x: x.split('-')[2]).astype(int)\n df['month'] = df[date_column].apply(lambda x: x.split('-')[1]).astype(int)\n df['year'] = df[date_column].apply(lambda x: x.split('-')[0]).astype(int)\n df = df[['day', 'month', 'year', ylabel]]\n\n ## Preparing Feature matrix\n X = np.array(df.drop([ylabel], 1))\n ## Preparing label\n prices = []\n for row in df[ylabel]:\n prices.append(float(row))\n dates = np.reshape(X, (len(X), 3)) # converting to matrix of n X 1\n #print(dates)\n prices = np.reshape(prices, (len(prices), 1))\n #print(prices)\n\n\n return dates, prices\n\ndef svm_model(stockhistory,num_days):\n df = stockhistory\n df['ds'] = df.index\n last_date = df.index[-1]\n\n\n ### Make future dataframe\n df1 = pd.DataFrame(index=range(num_days), columns=['Date', 'Close', 'predictions'])\n for i in range(num_days):\n df1['Date'][i] = last_date + datetime.timedelta(days=i + 1)\n\n # print(df1)\n\n # Get predictions on original dataset\n def create_dataset(df, prediction):\n se = pd.Series(prediction)\n df['predictions'] = se.values\n\n return df\n\n\n dates, prices = get_features(df, 'ds', 'Close')\n # print(dates)\n svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)\n svr_rbf.fit(dates, prices.ravel())\n prediction = svr_rbf.predict(dates)\n svm_original = create_dataset(df, prediction)\n svm_original = svm_original[['Close', 'predictions']]\n # print(svm_original)\n\n future_dates = feature(df1, 'Date')\n prediction_new = svr_rbf.predict(future_dates)\n # print(prediction_new)\n svm_future = create_dataset(df1, prediction_new)\n svm_future.set_index('Date', inplace=True)\n svm_future = svm_future[['Close', 'predictions']]\n # print(svm_future)\n\n svm = svm_original.append(svm_future)\n #print(svm)\n\n graph = pygal.Line()\n graph.title = '%SVM Model%'\n graph.x_labels = svm.index\n graph.add('Actual data', svm['Close'])\n graph.add('Forecasted data', svm['predictions'])\n graph_data = graph.render_data_uri()\n suggestion = \"Buy\" if svm_future['predictions'][0] > svm_original['Close'][len(svm_original) - 1] else \"Sell\"\n\n #Refactoring done for UI\n svm_future.index.names = ['Future_date']\n svm_future = svm_future.rename(columns={'Close': 'Actual_Price', 'predictions': 'Forecasted_Price'})\n del svm_future['Actual_Price']\n\n return graph_data, svm_future, suggestion, svm\n\n\ndef prepare_data(df,forecast_col,forecast_out,test_size):\n\n label = df[forecast_col].shift(-forecast_out);#creating new column called label with the last 5 rows are nan\n X = np.array(df[[forecast_col]]); #creating the feature array\n\n X = preprocessing.scale(X) #processing the feature array\n X_lately = X[-forecast_out:] #creating the column i want to use later in the predicting method\n X = X[:-forecast_out] # X that will contain the training and testing\n\n\n label.dropna(inplace=True); #dropping na values\n y = np.array(label) # assigning Y\n X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=test_size) #cross validation\n\n response = [X_train,X_test , Y_train, Y_test , X_lately, X]\n return response\n\ndef linear_regression(stockhistory, num_days):\n df = stockhistory\n forecast_col = 'Close'\n forecast_out = num_days\n test_size = 0.2\n\n X_train, X_test, Y_train, Y_test, X_lately, X = prepare_data(df, forecast_col, forecast_out, test_size)\n learner = LinearRegression()\n\n learner.fit(X_train, Y_train)\n score = learner.score(X_test, Y_test)\n forecast = learner.predict(X)\n forecast_future = learner.predict(X_lately)\n ##### df - for original values prediction\n df['Forecast'] = 0.0\n for i in range(len(forecast)):\n df['Forecast'][i] = forecast[i]\n\n df['Act_forecast'] = df['Forecast'].shift(forecast_out)\n df['Date'] = df.index\n #### df2 - for future value prediction\n df2 = df[-forecast_out:]\n #print(df2)\n last_date = df2.index[-1]\n df2 = df[-forecast_out:]\n last_date = df2.index[-1]\n df2['future_date'] = last_date\n\n df2['future_preds'] = 0.0\n df2['Actual'] = np.nan\n preds = []\n for i in range(len(df2)):\n df2['future_date'][i] = last_date + datetime.timedelta(days=i + 1)\n for i in range(len(forecast_future)):\n df2['future_preds'][i] = forecast_future[i]\n\n # print(forecast_future)\n linear = df2[['future_date', 'Actual', 'future_preds']].reset_index(drop=True)\n linear = linear.rename(columns={'future_date': 'Date', 'Actual': 'Close', 'future_preds': 'Act_forecast'})\n #print(linear)\n original = df[['Date', 'Close', 'Act_forecast']].reset_index(drop=True)\n # print(original)\n\n final_df = original.append(linear, sort=True)\n\n # Suggest whether to buy stock or sell. if predicted value is greater for tomorrow then buy\n suggestion = \"Buy\" if linear['Act_forecast'][0] > stockhistory['Close'][len(stockhistory)-1] else \"Sell\"\n\n # calculate mean square error\n error_df = final_df.dropna()\n\n graph = pygal.Line()\n graph.title = '%Linear Model%'\n graph.x_labels = final_df['Date']\n graph.add('Actual data', final_df['Close'])\n graph.add('Forecasted data', final_df['Act_forecast'])\n graph_data = graph.render_data_uri()\n\n #Refactoring for UI - part of integration\n linear = linear.rename(columns={'Date': 'Future_date', 'Act_forecast': 'Forecasted_Price'})\n del linear['Close']\n linear.set_index('Future_date', inplace=True)\n\n return graph_data,linear , suggestion, error_df\n\n\n@app.route('/predict',methods=['POST','GET'])\ndef predict():\n\n if request.method == 'POST':\n\n companyname = request.form['companyname']\n num_days = int(request.form['num_days'])\n\n print(\"Getting data for\" + companyname)\n #Get stock history from yahoo page\n stockhistory = get_stock_data(companyname)\n\n graph_data, df, suggestion_pro, d = prophet_model(stockhistory, num_days)\n graph_data_svm, df2 , suggestion_svm, svm = svm_model(stockhistory, num_days)\n graph_data_linear , linear , suggestion_linear, error_df = linear_regression(stockhistory, num_days)\n\n return render_template(\"graphing.html\", graph_data=graph_data, graph_data_svm = graph_data_svm, graph_data_linear = graph_data_linear,\\\n tables=[df.to_html()], svm_table=[df2.to_html()], linear_table= [linear.to_html()],\\\n suggestion_pro =suggestion_pro, suggestion_svm = suggestion_svm, suggestion_linear= suggestion_linear)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True, threaded=True)","sub_path":"Flaskblog/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":11008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"621953226","text":"import chisel\n\n# my_action3 should not load from here - will error if it does...\nfrom .sub.subsub.submodule import my_action3 # pylint: disable=unused-import\n\n\n# Action callback used by test_app.py\n@chisel.action\ndef my_action(ctx, dummy_req):\n\n # Log info and a warning\n ctx.log.debug('Some info')\n ctx.log.warning('A warning...')\n\n return {}\n\n\n@chisel.action\ndef my_action2(ctx, req):\n ctx.log.info('In my_action2')\n\n if 'MYENVIRON' in ctx.environ:\n multiplier = int(ctx.environ['MYENVIRON'])\n else:\n multiplier = 2\n\n return {'result': req['value'] * multiplier}\n\n\n@chisel.action\ndef my_action4(ctx, dummy_req):\n ctx.log.info('In my_action4')\n\n return {'reconstructedURL': ctx.reconstructed_url or 'ERROR'}\n","sub_path":"chisel/tests/test_app_files/module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"405040290","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2020/4/3 13:51\n# @File : jieba_test.py\n# @IDE : PyCharm\n\n\nfrom functools import wraps\nimport time\nimport jieba\n\n# s = '可使用 jieba.cut 和 jieba.cut_for_search 方法进行分词，两者所返回的结构都是一个可迭代的 generator，可使用 for 循环来获得分词后得到的每一个词语（unicode），或者直接使用 jieba.lcut 以及 jieba.lcut_for_search 直接返回 list'\n# lst = jieba.lcut(s, cut_all=False)\n# print(lst)\n# # print(' '.join(lst))\n#\n#\n# seg_list = jieba.cut_for_search(\"他毕业于上海交通大学机电系，后来在一机部上海电器科学研究所工作\")\n# print(\"【搜索引擎模式】：\" + \"/ \".join(seg_list))\n\n\ndef time_this(func):\n \"\"\"\n Decorator defines a method's boundaries and execution speed.\n :param func: inner func\n :return: the result of inner func\n \"\"\"\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n start = time.time()\n result = func(*args, **kwargs)\n end = time.time()\n print('The execution time of', func.__name__, 'is',\n (end - start) * 1000, 'milliseconds')\n\n return result\n\n return wrapper\n\n\n@time_this\ndef test(n):\n c = 0\n while c < n:\n tt = 'testHello,Java'\n b = tt.upper().encode()\n c += 1\n\n@time_this\ndef test1(n):\n c = 0\n while c < n:\n tt = 'testHello,Java'\n b = tt.encode().upper()\n c += 1\n\n\n\n\n# 1855 1835\n# 1851 1835\n\nif __name__ == '__main__':\n # test(10000000)\n test1(10000000)","sub_path":"test/jieba_test.py","file_name":"jieba_test.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"195338328","text":"#!/usr/bin/python\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as ppl\r\nfrom scipy.io import netcdf\r\nfrom scipy.interpolate import griddata\r\nfrom datetime import datetime\r\nfrom matplotlib.dates import date2num, num2date\r\nfrom os.path import join\r\nimport vtktools\r\n\r\nclass GPSConverter(object):\r\n '''\r\n GPS Converter class which is able to perform convertions between the \r\n CH1903 and WGS84 system.\r\n '''\r\n # Convert CH y/x/h to WGS height\r\n def CHtoWGSheight(self, y, x, h):\r\n # Axiliary values (% Bern)\r\n y_aux = (y - 600000) / 1000000\r\n x_aux = (x - 200000) / 1000000\r\n h = (h + 49.55) - (12.60 * y_aux) - (22.64 * x_aux)\r\n return h\r\n\r\n # Convert CH y/x to WGS lat\r\n def CHtoWGSlat(self, y, x):\r\n # Axiliary values (% Bern)\r\n y_aux = (y - 600000) / 1000000\r\n x_aux = (x - 200000) / 1000000\r\n lat = (16.9023892 + (3.238272 * x_aux)) + \\\r\n - (0.270978 * pow(y_aux, 2)) + \\\r\n - (0.002528 * pow(x_aux, 2)) + \\\r\n - (0.0447 * pow(y_aux, 2) * x_aux) + \\\r\n - (0.0140 * pow(x_aux, 3))\r\n # Unit 10000\" to 1\" and convert seconds to degrees (dec)\r\n lat = (lat * 100) / 36\r\n return lat\r\n\r\n # Convert CH y/x to WGS long\r\n def CHtoWGSlng(self, y, x):\r\n # Axiliary values (% Bern)\r\n y_aux = (y - 600000) / 1000000\r\n x_aux = (x - 200000) / 1000000\r\n lng = (2.6779094 + (4.728982 * y_aux) + \\\r\n + (0.791484 * y_aux * x_aux) + \\\r\n + (0.1306 * y_aux * pow(x_aux, 2))) + \\\r\n - (0.0436 * pow(y_aux, 3))\r\n # Unit 10000\" to 1\" and convert seconds to degrees (dec)\r\n lng = (lng * 100) / 36\r\n return lng\r\n\r\n # Convert decimal angle (deg dec) to sexagesimal angle (dd.mmss,ss)\r\n def DecToSexAngle(self, dec):\r\n degree = int(np.floor(dec))\r\n minute = int(np.floor((dec - degree) * 60))\r\n second = (((dec - degree) * 60) - minute) * 60\r\n return degree + (float(minute) / 100) + (second / 10000)\r\n\t\t\r\n # Convert sexagesimal angle (dd.mmss,ss) to seconds\r\n def SexAngleToSeconds(self, dms):\r\n degree = 0 \r\n minute = 0 \r\n second = 0\r\n degree = np.floor(dms)\r\n minute = np.floor((dms - degree) * 100)\r\n second = (((dms - degree) * 100) - minute) * 100\r\n return second + (minute * 60) + (degree * 3600)\r\n\r\n # Convert sexagesimal angle (dd.mmss) to decimal angle (degrees)\r\n def SexToDecAngle(self, dms):\r\n degree = 0\r\n minute = 0\r\n second = 0\r\n degree = np.floor(dms)\r\n minute = np.floor((dms - degree) * 100)\r\n second = (((dms - degree) * 100) - minute) * 100\r\n return degree + (minute / 60) + (second / 3600)\r\n \r\n # Convert WGS lat/long (deg dec) and height to CH h\r\n def WGStoCHh(self, lat, lng, h):\r\n lat = self.DecToSexAngle(lat)\r\n lng = self.DecToSexAngle(lng)\r\n lat = self.SexAngleToSeconds(lat)\r\n lng = self.SexAngleToSeconds(lng)\r\n # Axiliary values (% Bern)\r\n lat_aux = (lat - 169028.66) / 10000\r\n lng_aux = (lng - 26782.5) / 10000\r\n h = (h - 49.55) + (2.73 * lng_aux) + (6.94 * lat_aux)\r\n return h\r\n\r\n # Convert WGS lat/long (deg dec) to CH x\r\n def WGStoCHx(self, lat, lng):\r\n lat = self.DecToSexAngle(lat)\r\n lng = self.DecToSexAngle(lng)\r\n lat = self.SexAngleToSeconds(lat)\r\n lng = self.SexAngleToSeconds(lng)\r\n # Axiliary values (% Bern)\r\n lat_aux = (lat - 169028.66) / 10000\r\n lng_aux = (lng - 26782.5) / 10000\r\n x = ((200147.07 + (308807.95 * lat_aux) + \\\r\n + (3745.25 * pow(lng_aux, 2)) + \\\r\n + (76.63 * pow(lat_aux,2))) + \\\r\n - (194.56 * pow(lng_aux, 2) * lat_aux)) + \\\r\n + (119.79 * pow(lat_aux, 3))\r\n return x\r\n\r\n\t# Convert WGS lat/long (deg dec) to CH y\r\n def WGStoCHy(self, lat, lng):\r\n lat = self.DecToSexAngle(lat)\r\n lng = self.DecToSexAngle(lng)\r\n lat = self.SexAngleToSeconds(lat)\r\n lng = self.SexAngleToSeconds(lng)\r\n # Axiliary values (% Bern)\r\n lat_aux = (lat - 169028.66) / 10000\r\n lng_aux = (lng - 26782.5) / 10000\r\n y = (600072.37 + (211455.93 * lng_aux)) + \\\r\n - (10938.51 * lng_aux * lat_aux) + \\\r\n - (0.36 * lng_aux * pow(lat_aux, 2)) + \\\r\n - (44.54 * pow(lng_aux, 3))\r\n return y\r\n\r\n def LV03toWGS84(self, east, north, height):\r\n '''\r\n Convert LV03 to WGS84 Return a array of double that contain lat, long,\r\n and height\r\n '''\r\n d = []\r\n d.append(self.CHtoWGSlat(east, north))\r\n d.append(self.CHtoWGSlng(east, north))\r\n d.append(self.CHtoWGSheight(east, north, height))\r\n return d\r\n \r\n def WGS84toLV03(self, latitude, longitude, ellHeight):\r\n '''\r\n Convert WGS84 to LV03 Return an array of double that contaign east,\r\n north, and height\r\n '''\r\n d = []\r\n d.append(self.WGStoCHy(latitude, longitude))\r\n d.append(self.WGStoCHx(latitude, longitude))\r\n d.append(self.WGStoCHh(latitude, longitude, ellHeight))\r\n return d\r\n\r\n\r\n# extract COSMO grid info\r\ndef get_COSMO_grid(cosmo_file):\r\n #%Extract geographical data from the first COSMO-2 file\r\n #date = datevec(dateini); %Date vector\r\n #FileName = [datapath sprintf('%i',date(1)) '\\cosmo2_epfl_lakes_' sprintf('%i%02i%02i',date(1:3)) '.nc'];\r\n f = netcdf.netcdf_file(cosmo_file, 'r')\r\n lon = f.variables[\"lon_1\"][:]\r\n lat = f.variables[\"lat_1\"][:]\r\n f.close()\r\n converter = GPSConverter()\r\n x = np.zeros_like(lon)\r\n y = np.zeros_like(lat)\r\n for jj in xrange(x.shape[1]):\r\n for ii in xrange(x.shape[0]):\r\n x[ii,jj], y[ii,jj], _ = converter.WGS84toLV03(\r\n lat[ii, jj], lon[ii,jj], 0.0)\r\n return x, y\r\n\r\n\r\ndef plot_cosmo_scalar(input, data_path, data_root, date, cosmovar, era40var):\r\n\r\n\r\n # Time range to be extracted\r\n date = date2num(datetime.strptime(date, '%d-%m-%Y %H:%M'))\r\n\r\n in_f = netcdf.netcdf_file(input, 'r')\r\n ref_date = in_f.variables[\"time\"].units\r\n ref_date = ref_date.lstrip(\"seconds since \")\r\n ref_date = date2num(datetime.strptime(ref_date, '%d-%m-%Y %H:%M:%S'))\r\n\r\n xi = in_f.variables[\"longitude\"][:].copy()\r\n yi = in_f.variables[\"latitude\"][:].copy()\r\n\r\n time = int( 24 * (date - ref_date))\r\n\r\n var_f = in_f.variables[era40var][time, ...]\r\n\r\n in_f.close()\r\n\r\n # Get grid info\r\n fname = data_root + num2date(int(date)).strftime(\"%Y%m%d\") + \".nc\"\r\n fname = join(data_path,fname)\r\n xc, yc = get_COSMO_grid(fname)\r\n\r\n # define the limits of the plot domain\r\n minx = xi.min() - 1e4\r\n maxx = xi.max() + 1e4\r\n miny = yi.min() - 1e4\r\n maxy = yi.max() + 1e4\r\n\r\n ind = np.where((xc>=minx) & (xc<=maxx) & (yc>=miny) & (yc<=maxy))\r\n\r\n xc = xc[ind[0], ind[1]]\r\n yc = yc[ind[0], ind[1]]\r\n\r\n fname = data_root + num2date(int(date)).strftime(\"%Y%m%d\") + \".nc\"\r\n in_f = netcdf.netcdf_file(join(data_path,fname), 'r')\r\n\r\n hour = int((date % 1) * 24)\r\n\r\n var_c = in_f.variables[cosmovar][hour, ind[0], ind[1]]\r\n\r\n in_f.close()\r\n\r\n xp = np.linspace(xi.min(), xi.max(), 100)\r\n yp = np.linspace(yi.min(), yi.max(), 100)\r\n xplot, yplot = np.meshgrid(xp, yp)\r\n out_var = griddata((xc,yc), var_c.ravel(), (xplot, yplot))\r\n\r\n # Convert to km\r\n xi *= 1e-3\r\n yi *= 1e-3\r\n xp *= 1e-3\r\n yp *= 1e-3\r\n\r\n # Load shoreline\r\n xs, ys = np.genfromtxt(\"/media/space/Data/Bathy_Lac_Leman/shoreline.ldb\",\r\n unpack=True)\r\n xs *= 1.e-3\r\n ys *= 1.e-3\r\n\r\n f = ppl.figure()\r\n ax = f.add_subplot(111)\r\n \r\n minval = np.percentile(out_var, 2)\r\n maxval = np.percentile(out_var, 98)\r\n\r\n if minval == maxval:\r\n if np.all(out_var==0.) & np.all(var_f==0.):\r\n print(\"Both fields are zero.\")\r\n return\r\n\r\n C = ax.contourf(xp, yp, out_var, 40, vmin=minval, vmax=maxval, alpha=0.7)\r\n ppl.colorbar(C)\r\n\r\n ax.contour(xi, yi, var_f, 40, vmin=minval, vmax=maxval, lw=2)\r\n\r\n # shore\r\n ax.plot(xs, ys, 'k-', lw=0.8)\r\n\r\n ax.set_xlabel(\"Lat (km CH1903)\")\r\n ax.set_ylabel(\"Lon (km CH1903)\")\r\n\r\n f.suptitle(\"Cosmo (fill): {}, fluidity (contours): {}\".format(cosmovar, era40var))\r\n\r\n ax.grid(\"off\")\r\n\r\n ppl.show()\r\n\r\n__doc__ = \\\r\n\"\"\"plot_cosmo_scalar.py\r\n\r\nPlot COSMO scalar input and interpolated version for fluidity (usually,\r\nERA40-style).\r\n\r\nUsage:\r\n plot_cosmo_scalar.py -c -e [-h]\r\n\r\nArguments:\r\n NetCDF file for fluidity BC computed by cosmo2fluidity.py\r\n Directory where the COSMO2 files are located.\r\n Root of the file names of COSMO2 output.\r\n Date to plot, in the format \"01-12-2015 23:00\".\r\n\r\nOptions:\r\n -c Variable name in the COSMO2 file.\r\n -e (Corresponding) variable name in ERA40 file.\r\n\r\n\"\"\"\r\n\r\n\r\nif __name__ == '__main__':\r\n from docopt import docopt\r\n args = docopt(__doc__) # applause\r\n\r\n plot_cosmo_scalar(args[\"\"],\r\n args[\"\"],\r\n args[\"\"],\r\n args[\"\"],\r\n args[\"-c\"],\r\n args[\"-e\"])\r\n","sub_path":"plot_cosmo_scalar.py","file_name":"plot_cosmo_scalar.py","file_ext":"py","file_size_in_byte":9455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"324260321","text":"import tensorflow as tf\nimport numpy as np\nimport core as cr\nimport env.env_config as env_set\nimport random\nfrom collections import deque\nfrom tensorboardX import SummaryWriter\n\nenv_set = env_set.reacher_set\n\nclass PPO:\n def __init__(self):\n self.sess = tf.Session()\n self.state_size = env_set['state']\n self.output_size = env_set['action']\n self.worker_size = env_set['worker']\n self.gamma = env_set['gamma']\n self.lamda = 0.97\n self.hidden = env_set['hidden']\n self.pi_lr = 0.00025\n self.v_lr = 0.00025\n self.ppo_eps = 0.2\n self.epoch = 10\n\n self.x_ph, self.a_ph, self.adv_ph, self.target_ph, self.logp_old_ph, self.old_value = \\\n cr.placeholders(self.state_size, self.output_size, None, None, None, None)\n\n self.pi, self.logp, self.logp_pi, self.v = cr.ppo_mlp_actor_critic(\n x=self.x_ph,\n a=self.a_ph,\n hidden=self.hidden,\n activation=tf.nn.relu,\n output_activation=None,\n output_size=self.output_size\n )\n\n self.all_phs = [self.x_ph, self.a_ph, self.adv_ph, self.target_ph, self.logp_old_ph, self.old_value]\n self.get_action_ops = [self.pi, self.v, self.logp_pi]\n\n self.ratio = tf.exp(self.logp - self.logp_old_ph)\n\n self.min_adv = tf.where(self.adv_ph > 0, (1.0 + self.ppo_eps)*self.adv_ph, (1.0 - self.ppo_eps)*self.adv_ph)\n self.pi_loss = -tf.reduce_mean(tf.minimum(self.ratio * self.adv_ph, self.min_adv))\n\n self.clipped_value_loss = self.old_value + tf.clip_by_value(self.v - self.old_value, -self.ppo_eps, self.ppo_eps)\n self.v_loss1 = (self.target_ph - self.clipped_value_loss) ** 2\n self.v_loss2 = (self.target_ph - self.v) ** 2\n self.v_loss = 0.5 * tf.reduce_mean(tf.maximum(self.v_loss1, self.v_loss2))\n\n self.train_pi = tf.train.AdamOptimizer(self.pi_lr).minimize(self.pi_loss)\n self.train_v = tf.train.AdamOptimizer(self.v_lr).minimize(self.v_loss)\n\n self.approx_kl = tf.reduce_mean(self.logp_old_ph - self.logp)\n self.approx_ent = tf.reduce_mean(-self.logp)\n\n self.sess.run(tf.global_variables_initializer())\n\n def update(self, state, action, target, adv, logp_old, value):\n zip_ph = [state, action, adv, target, logp_old, value]\n inputs = {k:v for k,v in zip(self.all_phs, zip_ph)}\n value_loss, kl, ent = 0, 0, 0\n for i in range(self.epoch):\n _, _, v_loss, approxkl, approxent = self.sess.run([self.train_pi, self.train_v, self.v_loss, self.approx_kl, self.approx_ent], feed_dict=inputs)\n value_loss += v_loss\n kl += approxkl\n ent += approxent\n return value_loss, kl, ent\n\n def get_action(self, state):\n a, v, logp_t = self.sess.run(self.get_action_ops, feed_dict={self.x_ph: state})\n return a, v, logp_t\n\n \n def run(self):\n from mlagents.envs import UnityEnvironment\n\n writer = SummaryWriter('runs/ppo2_' + env_set['env_name'])\n n_step = 64\n step = 0\n end_step = 1000 * env_set['ep_len']\n\n scores = np.zeros([self.worker_size])\n score = deque(maxlen=10)\n\n env = UnityEnvironment(file_name='env/' + env_set['env_name'], worker_id=0)\n default_brain = env.brain_names[0]\n\n env_info = env.reset(train_mode=True)[default_brain]\n states = env_info.vector_observations\n\n while True:\n values_list, states_list, actions_list, dones_list, logp_ts_list, rewards_list = \\\n [], [], [], [], [], []\n for _ in range(n_step):\n step += 1\n actions, values, logp_ts = self.get_action(states)\n env_info = env.step(actions)[default_brain]\n\n next_states = env_info.vector_observations\n rewards = env_info.rewards\n dones = env_info.local_done\n\n scores += rewards\n\n states_list.append(states)\n values_list.append(values)\n actions_list.append(actions)\n dones_list.append(dones)\n logp_ts_list.append(logp_ts)\n rewards_list.append(rewards)\n\n states = next_states\n for idx, d in enumerate(dones):\n if d:\n score.append(scores[idx])\n scores[idx] = 0\n\n if step % 1000 == 0:\n print('episode :', int(step / 1000), '| score : ',\n \"{0:.2f}\".format(np.mean(score)))\n #if step < end_step:\n writer.add_scalar('data/reward', np.mean(score), int(step / 1000))\n\n actions, values, logp_ts = self.get_action(states)\n values_list.append(values)\n values_list = np.stack(values_list).transpose([1, 0])\n\n current_value_list = values_list[:, :-1]\n next_value_list = values_list[:, 1:]\n\n states_list = np.stack(states_list).transpose([1, 0, 2]).reshape([-1, self.state_size])\n actions_list = np.stack(actions_list).transpose([1, 0, 2]).reshape([-1, self.output_size])\n dones_list = np.stack(dones_list).transpose([1, 0]).reshape([-1])\n logp_ts_list = np.stack(logp_ts_list).transpose([1, 0]).reshape([-1])\n rewards_list = np.stack(rewards_list).transpose([1, 0]).reshape([-1])\n current_value_list = np.stack(current_value_list).reshape([-1])\n next_value_list = np.stack(next_value_list).reshape([-1])\n\n adv_list, target_list = [], []\n for idx in range(self.worker_size):\n start_idx = idx * n_step\n end_idx = (idx + 1) * n_step\n adv, target = cr.get_gaes(\n rewards_list[start_idx : end_idx],\n dones_list[start_idx : end_idx],\n current_value_list[start_idx : end_idx],\n next_value_list[start_idx : end_idx],\n self.gamma,\n self.lamda,\n True\n )\n adv_list.append(adv)\n target_list.append(target)\n\n adv_list = np.stack(adv_list).reshape([-1])\n target_list = np.stack(target_list).reshape([-1])\n \n value_loss, kl, ent = self.update(states_list, actions_list, target_list,\n adv_list, logp_ts_list, current_value_list)\n\nif __name__ == '__main__':\n agent = PPO()\n agent.run()","sub_path":"ppo2.py","file_name":"ppo2.py","file_ext":"py","file_size_in_byte":6589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"204743561","text":"#!/usr/bin/env python3\na = abs\nz = [list(map(int, input().split())) for _ in range(int(input()))]\nz.sort(key = lambda x: x[2])\nx, y, h = z[-1]\nr = range(101)\nfor c in r:\n for d in r:\n H = h + a(c-x) + a(d-y)\n if all(u == max(H - a(s-c) - a(t-d), 0) for s, t, u in z):\n print(c, d, H)\n exit()","sub_path":"Contest/ABC112/c/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388439210","text":"#Definition for singly-linked list.\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n def show(self):\n while(self):\n print(self.val,end='->')\n self=self.next\n print('')\nclass Solution:\n def mergeTwoLists(self, l1: 'ListNode', l2: 'ListNode') -> 'ListNode':\n p=l1\n q=l2\n head=ListNode(0)\n pre=head\n while True:\n if p and q:\n if p.val<=q.val:\n pre.next=ListNode(p.val)\n p=p.next\n pre=pre.next\n else:\n pre.next=ListNode(q.val)\n q=q.next\n pre=pre.next\n else:\n break\n if q:\n pre.next=q\n if p:\n pre.next=p\n return head.next\nA=ListNode(1)\nB=ListNode(1)\n\nSolution().mergeTwoLists(A,B).show()","sub_path":"201902/21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"505553994","text":"# Derry Lammerding\n# CTEC 121 / Winter 2019\n# Module 4 / Problem Set 5\n# Problem 1 (25 points)\n\n\"\"\"\nUsing the graphics library, draw a picture of the side view of a car. \nMake sure to include two tires, an area for windows and the roof. \nInclude a front and rear bumper as well. \nUse all of the following types of objects and functions listed below:\n\n- Line\n- Circle\n- Rectangle\n- Polygon\n- setOutline\n- setFill\n\"\"\"\nimport graphics\n\n\ndef main():\n win = graphics.GraphWin(\"Polygonal Car\", 1000, 700)\n\n body = graphics.Rectangle(graphics.Point(\n 100, 400), graphics.Point(900, 510))\n body.draw(win)\n body.setFill('dark red')\n body.setOutline('red')\n\n tire1 = graphics.Circle(graphics.Point(250, 500), 80)\n tire1.draw(win)\n tire1.setFill('black')\n tire2 = graphics.Circle(graphics.Point(750, 500), 80)\n tire2.draw(win)\n tire2.setFill('black')\n hubCap1 = graphics.Circle(graphics.Point(250, 500), 50)\n hubCap1.draw(win)\n hubCap1.setFill('grey')\n hubCap2 = graphics.Circle(graphics.Point(750, 500), 50)\n hubCap2.draw(win)\n hubCap2.setFill('grey')\n centerCap1 = graphics.Circle(graphics.Point(250, 500), 40)\n centerCap1.draw(win)\n centerCap2 = graphics.Circle(graphics.Point(750, 500), 40)\n centerCap2.draw(win)\n\n frontBumper = graphics.Rectangle(graphics.Point(\n 850, 475), graphics.Point(920, 525))\n frontBumper.draw(win)\n frontBumper.setFill('grey')\n frontBumper.setOutline('grey')\n rearBumper = graphics.Rectangle(graphics.Point(\n 80, 475), graphics.Point(150, 525))\n rearBumper.draw(win)\n rearBumper.setFill('grey')\n rearBumper.setOutline('grey')\n\n cabin = graphics.Polygon(graphics.Point(200, 400), graphics.Point(\n 250, 100), graphics.Point(600, 100), graphics.Point(700, 400))\n cabin.draw(win)\n cabin.setFill('dark red')\n cabin.setOutline('red')\n\n rearWindow = graphics.Polygon(graphics.Point(215, 396), graphics.Point(\n 260, 115), graphics.Point(390, 115), graphics.Point(390, 396))\n rearWindow.draw(win)\n rearWindow.setFill('light blue')\n rearWindow.setOutline('blue')\n rearWindow.setWidth(4)\n\n frontWindow = graphics.Polygon(graphics.Point(420, 396), graphics.Point(\n 420, 115), graphics.Point(590, 115), graphics.Point(685, 396))\n frontWindow.draw(win)\n frontWindow.setFill('light blue')\n frontWindow.setOutline('blue')\n frontWindow.setWidth(4)\n\n doorHandle = graphics.Rectangle(graphics.Point(\n 420, 415), graphics.Point(450, 425))\n doorHandle.draw(win)\n doorHandle.setFill('grey')\n doorHandle.setOutline('grey')\n\n doorLine = graphics.Line(graphics.Point(\n 405, 100), graphics.Point(405, 510))\n doorLine.draw(win)\n doorLine.setOutline('red')\n\n win.getMouse()\n\n\nmain()\n","sub_path":"problem-set-5-problem-1.py","file_name":"problem-set-5-problem-1.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"513297115","text":"'''\nImplement function ToLowerCase() that has a string parameter str, and returns the same string in lowercase.\nExample 1:\nInput: \"Hello\"\nOutput: \"hello\"\nExample 2:\nInput: \"here\"\nOutput: \"here\"\nExample 3:\nInput: \"LOVELY\"\nOutput: \"lovely\"\n'''\n###########################################################\nclass Solution:\n def toLowerCase(self, str):\n \"\"\"\n :type str: str\n :rtype: str\n \"\"\"\n dict_str={'A':'a','B':'b','C':'c','D':'d','E':'e','F':'f','G':'g',\n 'H':'h','I':'i','J':'j','K':'k','L':'l','M':'m','N':'n',\n 'O':'o','P':'p','Q':'q','R':'r','S':'s','T':'t',\n 'U':'u','V':'v','W':'w','X':'x','Y':'y','Z':'z'}\n for i in range(len(str)):\n if str[i] in dict_str:\n str=str.replace(str[i],dict_str[str[i]])\n return str\n","sub_path":"String/00709. To Lower Case.py","file_name":"00709. To Lower Case.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"162954527","text":"from gda.device.scannable import ScannableMotionBase\nfrom gdascripts.utils import caget\nfrom gdascripts.pd.time_pds import tictoc\nfrom gda.epics import CAClient \n\nclass SetPvAndWaitForCallback(ScannableMotionBase):\n\n\t\"\"\" This is the constructor for the class. \"\"\"\n\tdef __init__(self, name, pvstring, timeout):\n\t\tself.name = name\n\t\tself.cli = CAClient(pvstring)\n\t\tself.timeout = timeout\n\t\tself.setInputNames([name])\n\t\tself.setOutputFormat(['%.9f'])\n\t\t\n\t# Configure the CA client's channel at the start of a scan\n\tdef atScanStart(self):\n\t\tif not(self.cli.isConfigured()):\n\t\t\tself.cli.configure()\n\n\tdef isBusy(self):\n\t\t\treturn 0\n\n\tdef getPosition(self):\t\n\t\tif self.cli.isConfigured():\n\t\t\treturn float(self.cli.caget())\n\t\telse:\n\t\t\tself.cli.configure()\n\t\t\treturn float(self.cli.caget())\n\t\t\tself.cli.clearup()\n\n\tdef asynchronousMoveTo(self, value):\n\t\tif self.cli.isConfigured():\t\t\n\t\t\tself.cli.caput(self.timeout, value)\n\t\telse:\n\t\t\tself.cli.configure()\n\t\t\tself.cli.caput(self.timeout, value)\n\t\t\tself.cli.clearup()\n\n\t#Close the CA EPICS channel at the end of a scan:\n\tdef atScanEnd(self):\n\t\tif self.cli.isConfigured():\n\t\t\tself.cli.clearup()\n\n","sub_path":"configurations/b16-config/scripts/pd_SetPvAndWaitForCallback.py","file_name":"pd_SetPvAndWaitForCallback.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"563291323","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"garbage code to make garbage text\"\"\"\n\nimport random\nimport re\nimport string\nimport upsidedown\nfrom functools import wraps\nfrom math import ceil\nfrom typing import List, Optional\nfrom logging import getLogger\nfrom timeit import default_timer as timer\n\nimport homoglyphs as hg\nimport inflect\nimport nltk\nimport numpy as np\nimport pronouncing\nfrom better_profanity import profanity\nfrom nltk.tokenize.treebank import TreebankWordDetokenizer\nfrom textblob import TextBlob, Word, Sentence\nfrom word2number import w2n\n\n# TODO: issues with pyenchant\n# import splitter\nfrom recumpiler.cheap_emoji_alias import get_cheap_emoji_alias\n\n# These are imported like this as the dependencies are complicated to install\n# and require large files. These are annoying to test in a CI for now.\n# TODO: avoid this work around in future make better CI solution\ntry:\n from recumpiler.emojijnet import get_gloveword_emoji\nexcept:\n get_gloveword_emoji = lambda word: None\ntry:\n from recumpiler.mutators_deepmoji import get_sentiment_emoji\nexcept:\n get_sentiment_emoji = lambda sentence: None\n\n\nfrom recumpiler.mutators_emoji_data import get_emoji_from_data\nfrom recumpiler.mutators_emotlib import get_emoticon\nfrom recumpiler.utils import (\n load_simple_text_emojis,\n load_action_verbs,\n load_rp_pronouns,\n init_emoji_database,\n get_emoji_database,\n load_text_face_emoji,\n load_garbage_tokens,\n decision,\n TweetWordTokenizer,\n)\n\ninflect_engine = inflect.engine()\n\n__log__ = getLogger(__name__)\n\n\ndef logged_mutator(f):\n @wraps(f)\n def wrapper(*args, **kwds):\n start = timer()\n output = f(*args, **kwds)\n end = timer()\n # TODO: issue hitting recursion limit\n # __log__.info(\n # {\n # \"message\": \"called mutator\",\n # \"mutator\": f.__name__,\n # \"args\": args,\n # \"kwargs\": kwds,\n # \"output\": output,\n # \"exc_time\": \"{0:.15f}\".format(end - start),\n # }\n # )\n return output\n\n return wrapper\n\n\n# TODO: refactor this global garbage\n\n\nnum_to_word_probability = 0.3\nword_to_num_probability = 0.3\n\ncommon_misspellings_probability = 0.2\nhard_owo_replace_probability = 0.2\n\nbold_text_probability = 0.04\n\nREEE_probability = 0.06\nREEE_allcaps_probability = 0.3\n\nadd_random_rp_action = True\nadd_random_rp_mid_sentence_action_probability = 0.005\nadd_random_rp_end_sentence_action_probability = 0.02\nmore_verbs_probability_decay = 0.4\n\nadd_random_garbage = True\nadd_random_garbage_probability = 0.01\n\nadd_random_plurals = True\nadd_random_plurals_probability = 0.1\n\nrandomly_lemmatize = True\nrandomly_lemmatize_probability = 0.1\n\nrandomly_overemphasis_punctuation = True\nrandomly_overemphasis_punctuation_probability = 0.5\nrandomly_overemphasis_punctuation_max_fuck = 4\n\nrandomly_capitalize_word = True\nrandomly_capitalize_word_probability = 0.1\n\nrandomly_spongebob_word = True\nrandomly_spongebob_word_probability = 0.1\n\nadd_randomly_text_face_emoji = True\nadd_randomly_text_face_emoji_probability = 0.05\n\nadd_random_simple_text_emoji = True\nadd_random_simple_text_emoji_probability = 0.07\n\nrandomly_swap_char = True\nrandomly_swap_char_probability = 0.04\nrandomly_swap_char_swap_percent = 0.2\n\nrandomly_insert_char = True\nrandomly_insert_char_probability = 0.04\nrandomly_insert_char_insert_percent = 0.1\n\nrandom_leet_speak = True\nrandom_leet_speak_probability = 0.1\n\nutf_8_char_swaps_probability = 0.1\n\nrandom_censor_probability = 0.01\nrandom_censor_percent = 0.25\n\ncensor_profanity_probability = 0.7\ncensor_profanity_percent = 0.25\n\nrandom_synonym_probability = 0.5\n\nrandom_ending_y_probability = 0.05\nleet_speak_min_token_length = 5\n\nadding_ending_ksksk_andioop_probability = 0.8\nadding_ending_ksksk_save_the_turtles_probability = 0.3\n\nksksk_enlargement_probability = 0.7\n\nowo_vs_ouo_bias = 0.5\n\nadd_extra_ed_probability = 0.05\nsplit_compound_word_probability = 0.03\n\nlazy_char_subbing_probability = 0.6\nuck_to_ucc_swap_probability = 0.4\n\njuwuice_swap_probability = 0.5\n\nadd_x3_if_token_has_rawr_probability = 0.2\nme_2_meh_swap_probability = 0.5\nme_2_meow_swap_probability = 0.5\n\nhard_uwu_replace_probability = 0.3\nsub_to_subby_swap_probability = 0.3\n\nfucking_normies_addition = 0.3\n\nget_rhymes_probability = 0.01\nmax_runon_rhymes = 3\n\nhomofiy_probability = 0.3\nhomofiy_percentage = 0.3\n\nback_tick_text_probability = 0.05\n\nspace_gap_text_probability = 0.02\nspace_gap_text_min_gap_size = 1\nspace_gap_text_max_gap_size = 4\n\nadd_text_relevant_emoji_probability = 0.1\nwrap_text_relevant_emoji_probability = 0.02\n\nlr_to_w_swap_probability = 0.4\n\ninvert_word_probability = 0.04\n\nupside_down_word_probability = 0.05\n\n\n@logged_mutator\ndef num_to_word(token: str) -> str:\n try:\n return str(w2n.word_to_num(token))\n except ValueError:\n return token\n\n\n@logged_mutator\ndef word_to_num(token: str) -> str:\n try:\n return inflect_engine.number_to_words(int(token))\n except ValueError:\n return token\n\n\n@logged_mutator\ndef knotter(token: str) -> str:\n token = re.sub(\n r\"(([^kK]|^)no+t)\",\n lambda match: f\"kn{'o' * random.choice(range(1, 3))}t\",\n token,\n flags=re.IGNORECASE,\n )\n return token\n\n\n@logged_mutator\ndef homoify(token: str, homo_percent: float = 0.3):\n if len(token) <= 3: # dont homoglyph censor stuff this small\n return token\n swaps = int(ceil(len(token) * homo_percent))\n indexes = random.choices(range(1, len(token)), k=swaps)\n for i in indexes:\n token = \"\".join(\n [\n token[w]\n if w != i\n else random.choice(hg.Homoglyphs().get_combinations(token[w]))\n for w in range(len(token))\n ]\n )\n return token\n\n\n@logged_mutator\ndef owoer(token: str) -> str:\n # TODO: owo usually goes to owoo should suppress.\n # TODO: does this still happen?\n\n token = re.sub(\n r\"(ou)([^o]|$)\",\n lambda match: f\"ouo{match.group(2) or ''}\",\n token,\n flags=re.IGNORECASE,\n )\n token = re.sub(\n r\"(ow)([^o]|$)\",\n lambda match: f\"owo{match.group(2) or ''}\",\n token,\n flags=re.IGNORECASE,\n )\n token = re.sub(\n r\"(ov)([^o]|$)\",\n lambda match: f\"ovo{match.group(2) or ''}\",\n token,\n flags=re.IGNORECASE,\n )\n\n token = re.sub(r\"(cor)\", lambda match: f\"cowor\", token)\n\n if (\n \"owo\" not in token.lower()\n and \"ouo\" not in token.lower()\n and decision(hard_owo_replace_probability)\n ):\n owo_str = \"owo\" if decision(owo_vs_ouo_bias) else \"ouo\"\n token = re.sub(\n r\"(o+)\",\n lambda match: (owo_str * len(match.group(1))).replace(\"oo\", \"o\"),\n token,\n flags=re.IGNORECASE,\n count=random.choice(range(0, 2)),\n )\n\n # juice -> juwuice\n if decision(juwuice_swap_probability):\n token = re.sub(\n r\"u+(i?ce)\",\n lambda match: f\"uwu{match.group(1)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n if \"uwu\" not in token.lower() and decision(hard_uwu_replace_probability):\n uwu_str = \"uwu\"\n token = re.sub(\n r\"u+\", uwu_str, token, flags=re.IGNORECASE, count=random.choice(range(0, 2))\n )\n\n return token\n\n\n@logged_mutator\ndef fuckyer(token: str) -> str:\n extra_fun = \"\"\n y_choice_1 = (\"y\" if decision(0.5) else \"i\") * random.choice(range(1, 5))\n y_choice_2 = (\"y\" if decision(0.5) else \"i\") * random.choice(range(1, 5))\n if decision(0.5):\n extra_fun = f\"w{'u' * random.choice(range(1, 5))}k{y_choice_2}\"\n token = re.sub(\n r\"([Ff])?uck(er|ing)?\",\n lambda match: f\"{match.group(1) or ''}{'u' * random.choice(range(1,5))}k{y_choice_1}{match.group(2) or ''}\"\n + \" \"\n + extra_fun,\n token,\n )\n return token\n\n\n@logged_mutator\ndef garbage(token: str) -> str:\n # inserting gay\n token = re.sub(r\"([a-fh-zA-FH-Z])a+y+\", lambda match: f\"{match.group(1)}gay\", token)\n\n # hello -> hewwo\n token = re.sub(r\"([Hh])e+ll+o+?\", lambda match: f\"{match.group(1)}ewwo\", token)\n\n # er -> ur\n if decision(0.4):\n token = re.sub(\n r\"e+r+\",\n lambda match: f\"u{'r' * ceil(np.random.rayleigh(1.2))}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # ello - >ewwo\n if decision(0.4):\n token = re.sub(\n r\"e+ll+o+?\",\n lambda match: f\"ew{'w' * ceil(np.random.rayleigh(1.2))}o\",\n token,\n flags=re.IGNORECASE,\n ) # 2-6ish\n\n # cute -> koot\n token = re.sub(\n r\"([Cc])u+te\",\n lambda match: f\"{match.group(1)}oo{'o' * random.randint(0,5)}t\",\n token,\n )\n\n # ove -> wuv\n if decision(0.7):\n token = re.sub(r\"(o+)ve\", lambda match: f\"w{'u' * len(match.group(1))}v\", token)\n\n # one -> wun\n if decision(0.7):\n token = re.sub(r\"one\", \"wun\", token, flags=re.IGNORECASE)\n\n # as -> ass asss\n if decision(0.5):\n token = re.sub(\n r\"([aA])([sS])($|[^s])\",\n lambda match: f\"{match.group(1)}{match.group(2) * random.randint(2,3)}t\",\n token,\n )\n\n # TODO: refactor (me -> meh|me -> meow) together?\n # me -> meow\n if decision(me_2_meow_swap_probability):\n token = re.sub(\n r\"^me+$\",\n lambda match: f\"m{'e' * random.randint(1,3)}{'o' * random.randint(1,3)}w\",\n token,\n flags=re.IGNORECASE,\n )\n\n # me -> meh\n if decision(me_2_meh_swap_probability):\n token = re.sub(\n r\"^me+$\",\n lambda match: f\"m{'e' * random.randint(1, 3)}h\",\n token,\n flags=re.IGNORECASE,\n )\n\n # my -> mah, myah\n if decision(0.5):\n token = re.sub(\n r\"^my+$\",\n lambda match: f\"m{'y' if decision(0.3) else ''}{'a' * random.randint(2, 3)}{'h' if decision(0.5) else ''}\",\n token,\n )\n\n # ion -> shun\n if decision(0.5):\n token = re.sub(r\"ion$\", \"shun\", token)\n\n # .ome -> .um\n if decision(0.5):\n token = re.sub(r\"([a-zA-Z])ome\", lambda match: f\"{match.group(1)}um\", token)\n\n # teh or da\n if decision(0.5):\n token = re.sub(r\"^([Tt])he$\", lambda match: f\"{match.group(1)}eh\", token)\n else:\n token = re.sub(\n r\"^([Tt])he$\",\n lambda match: f\"{'D' if match.group(1) == 'T' else 'd'}a\",\n token,\n )\n\n # ing -> inn\n if decision(0.5):\n token = re.sub(\n r\"ing$\",\n f\"in{'n' * random.randint(0,4) if decision(0.5) else 'in' * random.randint(0, 4)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # ks -> ksksksk\n if decision(ksksk_enlargement_probability):\n token = re.sub(\n r\"[kK][sS]|[sS][kK]\",\n lambda match: f\"{match.group(0) * random.randint(2,6)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # uck -> ucc, uccci\n if decision(uck_to_ucc_swap_probability):\n token = re.sub(\n r\"u+c+k+\",\n lambda match: f\"u{'c' * random.randint(2,6)}{'i' * random.randint(0,3)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n if decision(sub_to_subby_swap_probability):\n token = re.sub(\n r\"s(u+)b\",\n lambda match: f\"s{match.group(1)}bb{('y' if decision(0.5) else 'i') * random.randint(1, 2)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # no -> nu+ nyu+\n if decision(0.5):\n token = re.sub(\n \"([nN])(o+)\",\n lambda match: f\"{match.group(1)}{'y' if decision(0.5) else ''}{'u' * (len(match.group(2)) * random.randint(1, 6))}\",\n token,\n flags=re.IGNORECASE,\n )\n return token\n\n\n@logged_mutator\ndef reeeer(token: str) -> str:\n if decision(REEE_probability):\n token = re.sub(\n r\"([Rr])e*\",\n lambda match: f\"{match.group(1)}e\" + \"e\" * random.choice(range(1, 15)),\n token,\n )\n if decision(REEE_allcaps_probability):\n token = token.upper()\n return token\n\n\ndef rawrer(token: str) -> str:\n token = re.sub(r\"ra([a-zA-Z])?\", lambda match: f\"rawr{match.group(1) or ''}\", token)\n token = re.sub(\n r\"ar([a-zA-Z])?\", lambda match: f\"arawr{match.group(1) or ''}\", token\n )\n token = re.sub(r\"([Rr])oar\", lambda match: f\"{match.group(1)}awr\", token)\n\n return token\n\n\n@logged_mutator\ndef lr_to_w_swap(token: str) -> str:\n token = re.sub(\n r\"([lL])\",\n lambda match: f\"{('w' if decision(0.7) else 'wl') if match.group(1).islower() else ('W' if decision(0.7) else 'WL')}\",\n token,\n )\n token = re.sub(\n r\"([rR])\",\n lambda match: f\"{('w' if decision(0.7) else 'wr') if match.group(1).islower() else ('W' if decision(0.7) else 'WR')}\",\n token,\n )\n return token\n\n\n@logged_mutator\ndef jizzer(token: str) -> str:\n token = re.sub(r\"(.iz+)\", \"jizz\", token)\n return token\n\n\n@logged_mutator\ndef cummer(token: str) -> str:\n token = re.sub(r\"(.ome|co+m|co+n{1,3})\", \"cum\", token)\n token = re.sub(r\"(c.{0,2}u+m)\", \"cum\", token)\n token = re.sub(r\"(cau|cou)\", \"cum\", token)\n token = re.sub(r\"(cow)\", \"cum\", token)\n token = re.sub(r\"(son|sun$)\", \"cum\", token)\n\n token = re.sub(r\"([a-bd-zA-BD-Z])um\", lambda match: f\"{match.group(1)}cum\", token)\n\n token = re.sub(\n r\"([a-bd-zA-BD-Z])u(nn|mm)([yi])\",\n lambda match: f\"{match.group(1)}cumm{match.group(3)}\",\n token,\n )\n token = re.sub(r\"(cally)\", \"cummy\", token)\n\n return token\n\n\ngarbage_tokens = load_garbage_tokens()\n\n\n@logged_mutator\ndef add_random_garbage_token():\n return random.choice(garbage_tokens)\n\n\ntext_face_emojis = load_text_face_emoji()\n\n\n@logged_mutator\ndef find_text_relevant_emoji(token: str) -> Optional[str]:\n if (\n len(token) < 4\n ): # TODO: find better logic to avoid getting garbage or complete unrelated emojis\n return\n results = (\n get_emoji_database()\n .execute(\n \"\"\"select Emoji from Emoji_Sentiment_Data where \"Unicode name\" LIKE ?\"\"\",\n (\"%\" + token.upper() + \"%\",),\n )\n .fetchall()\n )\n if results:\n return random.choice(results)[0]\n\n\nemoji_database = init_emoji_database()\n\nsimple_text_emojis = load_simple_text_emojis()\n\naction_verbs = load_action_verbs()\n\nrp_pronouns = load_rp_pronouns()\n\n\n@logged_mutator\ndef get_random_text_face_emojis():\n return random.choice(text_face_emojis)\n\n\n@logged_mutator\ndef get_random_simple_text_emojis():\n return random.choice(simple_text_emojis)\n\n\n@logged_mutator\ndef generate_spongebob_text(token: str) -> str:\n \"\"\"gEnErAtEs sPoNgEbOb mEmE TeXt\"\"\"\n spongebob_text = \"\"\n for i, char in enumerate(token):\n if i % 2 == 0:\n spongebob_text += char.lower()\n else:\n spongebob_text += char.upper()\n return spongebob_text\n\n\n@logged_mutator\ndef shuffle_str(token: str) -> str:\n token_str_list = list(token)\n random.shuffle(token_str_list)\n return \"\".join(token_str_list)\n\n\n@logged_mutator\ndef get_runon_of_rhymes(\n token: str,\n max_runon: int = 3,\n allow_token_dupe: bool = False,\n allow_rhyme_dupes: bool = False,\n) -> List[str]:\n # TODO: this is a complicated mess\n selected_rhymes = []\n\n rhymes = get_pronouncing_rhyme(token)\n if not allow_token_dupe:\n try:\n rhymes.remove(token)\n except ValueError:\n pass\n\n level = 4\n while True:\n rhymes += get_nltk_rymes(token, level)\n if not allow_token_dupe:\n try:\n rhymes.remove(token)\n except ValueError:\n pass\n if rhymes:\n break\n if level == 0 or len(rhymes) > max_runon:\n break\n level -= 1\n\n if not allow_token_dupe:\n try:\n rhymes.remove(token)\n except ValueError:\n pass\n if not allow_rhyme_dupes:\n rhymes = list(sorted(list(set(rhymes))))\n if rhymes:\n selected_rhymes += random.choices(rhymes, k=min(len(rhymes), max_runon))\n return selected_rhymes\n\n\n@logged_mutator\ndef get_pronouncing_rhyme(token: str) -> List[str]:\n return pronouncing.rhymes(token)\n\n\n@logged_mutator\ndef get_nltk_rymes(token: str, level: int) -> List[str]:\n # TODO: stub\n def rhyme(inp, level: int):\n \"\"\"\n 1 bad rhymes\n 2\n 4 good rhymes\n \"\"\"\n entries = nltk.corpus.cmudict.entries()\n syllables = [(word, syl) for word, syl in entries if word == inp]\n rhymes = []\n for (word, syllable) in syllables:\n rhymes += [\n word for word, pron in entries if pron[-level:] == syllable[-level:]\n ]\n return set(rhymes)\n\n return list(rhyme(token, level))\n\n\n@logged_mutator\ndef over_emphasise_punctuation(token: str, max_fuck: int = 4) -> str:\n if token == \"?\":\n token += \"\".join(\n random.choices(\n [\n \"1\",\n # \"i\",\n \"!\",\n \"?\",\n # \"I\",\n # \"/\",\n # \".\",\n # \"\\\\\"\n ],\n k=random.choice(range(0, max_fuck)),\n )\n )\n token = shuffle_str(token)\n if token == \"!\":\n token += \"\".join(\n random.choices(\n [\n \"1\",\n # \"i\",\n \"!\",\n \"?\",\n # \"I\",\n # \"/\",\n \"|\",\n ],\n k=random.choice(range(0, max_fuck)),\n )\n )\n token = shuffle_str(token)\n if token == \".\":\n token += \"\".join(\n random.choices([\",\", \".\"], k=random.choice(range(0, max_fuck)))\n )\n token = shuffle_str(token)\n\n return token\n\n\n@logged_mutator\ndef to_rp_text(token: str) -> str:\n return f\"*{token}*\"\n\n\n@logged_mutator\ndef get_random_action_verb():\n return random.choice(action_verbs)\n\n\n@logged_mutator\ndef get_random_rp_pronoun():\n return random.choice(rp_pronouns)\n\n\n@logged_mutator\ndef random_swap_char(token: str, swaps_percent: float = 0.2) -> str:\n if len(token) < 3: # dont do this for small tokens as they become un decipherable\n return token\n swaps = int(ceil(len(token) * swaps_percent))\n indexes = random.choices(range(len(token)), k=swaps)\n for i in indexes:\n token = \"\".join(\n [\n token[w] if w != i else random.choice(string.ascii_letters)\n for w in range(len(token))\n ]\n )\n return token\n\n\n@logged_mutator\ndef random_insert_char(token: str, insert_percent: float = 0.1) -> str:\n swaps = int(ceil(len(token) * insert_percent))\n indexes = random.choices(range(len(token)), k=swaps)\n token_str_list = list(token)\n for i in indexes:\n token_str_list.insert(i, random.choice(string.ascii_letters))\n token = \"\".join(token_str_list)\n return token\n\n\n@logged_mutator\ndef token_to_leet(token: str) -> str:\n if len(token) < 5: # leet speaking small text has hard to read results\n return token\n leet_char_mapping = {\n # \"a\": \"4\",\n \"a\": \"@\",\n \"e\": \"3\",\n \"8\": \"&\",\n \"l\": \"1\",\n \"o\": \"0\",\n \"s\": \"5\",\n \"i\": \"1\",\n }\n getchar = (\n lambda c: leet_char_mapping[c.lower()] if c.lower() in leet_char_mapping else c\n )\n return \"\".join(getchar(c) for c in token)\n\n\n# TODO: lots of options maybe something learned?\n@logged_mutator\ndef utf_8_char_swaps(token: str) -> str:\n if decision(0.5):\n token = re.sub(r\"ae\", \"æ\", token)\n token = re.sub(r\"AE\", \"Æ\", token)\n if decision(0.3):\n token = re.sub(r\"ea\", \"æ\", token)\n token = re.sub(r\"EA\", \"Æ\", token)\n return token\n\n\n# TODO: this is only for discord so we don't break tokenization\n\n\n@logged_mutator\ndef recumpile_sentence(sentence: Sentence) -> List[str]:\n new_tokens = []\n # TODO: determine mood classifier for sentence and add respective emoji\n sentiment_emoji = None\n if decision(0.89):\n sentiment_emoji = get_sentiment_emoji(sentence)\n\n for token in sentence.tokenize(TweetWordTokenizer()):\n # TODO: this is only for discord so we dont break tokenization\n if re.match(\n r\"@everyone|@here|<:[^:\\s]+:[0-9]+>||<(?:@!?\\d+|:[A-Za-z0-9]+:)\\w+>\",\n token,\n ):\n new_tokens.append(token)\n continue\n\n emoji = None\n alias_emoji = get_cheap_emoji_alias(token)\n\n # TODO: refactor into its own mutator\n if decision(0.9) and (\n re.match(\"among\", token, flags=re.IGNORECASE)\n or re.match(\"amogus\", token, flags=re.IGNORECASE)\n or re.match(r\"su+s\", token, flags=re.IGNORECASE)\n ):\n emoji = \"ඞ\"\n\n emoticon = get_emoticon(token)\n\n if alias_emoji:\n if decision(0.1) or (len(str(token)) == 1 and decision(0.9)):\n new_tokens.append(alias_emoji)\n continue\n else:\n if decision(0.5):\n new_tokens.append(alias_emoji)\n\n if decision(0.5):\n emoji = get_emoji_from_data(token)\n if decision(0.3):\n emoji = get_gloveword_emoji(token)\n if emoji:\n if decision(0.5):\n new_tokens.append(emoji)\n\n if decision(random_synonym_probability):\n token = replace_with_random_synonym(token)\n if decision(0.5) and profanity.contains_profanity(token):\n token = token.upper()\n if decision(censor_profanity_probability) and profanity.contains_profanity(\n token\n ):\n if decision(0.1):\n token = custom_censoring(token, 1)\n else:\n token = custom_censoring(token, censor_profanity_percent)\n elif decision(random_censor_probability):\n token = custom_censoring(token, random_censor_percent)\n\n if re.match(\"musk\", token, flags=re.IGNORECASE):\n add_husky = True\n else:\n add_husky = False\n\n # processing\n recumpiled_token = recumpile_token(token)\n\n # post processing\n new_tokens.append(recumpiled_token)\n\n if emoji:\n if decision(0.8):\n new_tokens.append(emoji)\n if alias_emoji:\n if decision(0.8):\n new_tokens.append(alias_emoji)\n if emoticon:\n if decision(0.8):\n new_tokens.append(emoticon)\n\n if add_husky:\n new_tokens.append(recumpile_token(\"husky\"))\n\n if add_random_garbage and decision(add_random_garbage_probability):\n new_tokens.append(recumpile_token(add_random_garbage_token()))\n if add_randomly_text_face_emoji and decision(\n add_randomly_text_face_emoji_probability\n ):\n new_tokens.append(get_random_text_face_emojis())\n if add_random_simple_text_emoji and decision(\n # TODO: use textblob to determine mood of text and insert faces\n # accordingly likely need to do this after reconstruction of the\n # text blob and go through this sentence by sentence rather than\n # word by word.\n add_random_simple_text_emoji_probability\n ):\n new_tokens.append(get_random_simple_text_emojis())\n if add_random_rp_action and decision(\n add_random_rp_mid_sentence_action_probability\n ):\n new_tokens.append(get_random_rp_action_sentence())\n if add_random_rp_action and decision(add_random_rp_end_sentence_action_probability):\n new_tokens.append(get_random_rp_action_sentence())\n\n if sentiment_emoji:\n new_tokens.append(sentiment_emoji)\n if decision(0.4):\n for i in range(5):\n if decision(0.3):\n new_tokens.append(sentiment_emoji)\n else:\n break\n\n return new_tokens\n\n\n@logged_mutator\ndef add_ending_y(token: str) -> str:\n return re.sub(r\"([a-zA-Z]{4,}[^sy])\", lambda match: f\"{match.group(1)}y\", token)\n\n\ndef remove_dupe_chars(text: str) -> str:\n \"\"\"accept -> acept\"\"\"\n text = re.sub(r\"([a-zA-Z])\\1+\", r\"\\1\", text)\n return text\n\n\n@logged_mutator\ndef recumpile_token(token: str) -> str:\n # TODO: determine mood classifier for token and add respective emoji\n if decision(split_compound_word_probability):\n tokens = split_compound_word(token)\n else:\n tokens = [token]\n\n # TODO: migrate fuck_token to maybe a generator?\n fucked_tokens = []\n for token in tokens:\n relevant_emoji = None\n if decision(add_text_relevant_emoji_probability):\n relevant_emoji = find_text_relevant_emoji(\n token\n ) # TODO: add ability to get multiple?\n if relevant_emoji and decision(wrap_text_relevant_emoji_probability):\n fucked_tokens.append(relevant_emoji)\n\n if decision(0.1):\n token = remove_dupe_chars(token)\n\n if decision(lazy_char_subbing_probability):\n token = lazy_char_subbing(token)\n\n # TODO: this is a potential for unexpected behavior\n if decision(word_to_num_probability):\n token = word_to_num(token)\n if decision(num_to_word_probability):\n token = num_to_word(token)\n\n if decision(lr_to_w_swap_probability):\n token = lr_to_w_swap(token)\n\n # TODO: this might be too much idk\n if decision(invert_word_probability):\n token = word_inverter(token)\n\n if decision(upside_down_word_probability):\n token = word_upside_downer(token)\n elif decision(upside_down_word_probability):\n token = word_upside_downer_preserve_char_order(token)\n\n fucked_token = knotter(fuckyer(reeeer(rawrer(garbage(owoer(cummer(token)))))))\n\n if decision(add_extra_ed_probability):\n fucked_token = add_extra_ed(fucked_token)\n\n if decision(random_ending_y_probability):\n fucked_token = add_ending_y(fucked_token)\n\n # TODO: likely making fu@k into k\n # TODO: NOTE: indeed it is doing this fu@k\n # >>>list(TextBlob(\"fu@k\").words)\n # ['fu', 'k']\n if add_random_plurals and decision(add_random_plurals_probability):\n fucked_token = Word(fucked_token).pluralize()\n\n if randomly_lemmatize and decision(randomly_lemmatize_probability):\n fucked_token = Word(fucked_token).lemmatize()\n\n if randomly_capitalize_word and decision(randomly_capitalize_word_probability):\n fucked_token = fucked_token.upper()\n\n if randomly_spongebob_word and decision(randomly_spongebob_word_probability):\n fucked_token = generate_spongebob_text(fucked_token)\n\n if randomly_overemphasis_punctuation and decision(\n randomly_overemphasis_punctuation_probability\n ):\n fucked_token = over_emphasise_punctuation(\n fucked_token, randomly_overemphasis_punctuation_max_fuck\n )\n\n if decision(common_misspellings_probability):\n fucked_token = common_mispellings(fucked_token)\n\n if randomly_swap_char and decision(randomly_swap_char_probability):\n fucked_token = random_swap_char(\n fucked_token, randomly_swap_char_swap_percent\n )\n\n if randomly_insert_char and decision(randomly_insert_char_probability):\n fucked_token = random_insert_char(\n fucked_token, randomly_insert_char_insert_percent\n )\n if decision(utf_8_char_swaps_probability):\n fucked_token = utf_8_char_swaps(fucked_token)\n\n if random_leet_speak and decision(random_leet_speak_probability):\n fucked_token = token_to_leet(fucked_token)\n\n if decision(common_misspellings_probability):\n fucked_token = common_mispellings(fucked_token)\n\n # TODO: likely also breaking the spacing between punctuation kittly 1!\n # TODO: `fucked` went to `DS` investigate\n # TODO: likely this is at fault\n if decision(homofiy_probability):\n fucked_token = homoify(fucked_token, homofiy_probability)\n\n fucked_tokens.append(fucked_token)\n\n if decision(add_x3_if_token_has_rawr_probability) and (\n \"rawr\" in fucked_token.lower()\n ):\n fucked_tokens.append(\"X3\" if decision(0.5) else \"x3\")\n\n if decision(adding_ending_ksksk_andioop_probability) and (\n fucked_token.lower().endswith(\"ksk\")\n or fucked_token.lower().endswith(\"sks\")\n or \"ksksk\" in fucked_token.lower()\n or \"sksks\" in fucked_token.lower()\n ):\n for i in range(random.randint(1, 2)):\n fucked_tokens.append(recumpile_token(\"andioop\"))\n if decision(adding_ending_ksksk_save_the_turtles_probability) and (\n fucked_token.lower().endswith(\"ksk\")\n or fucked_token.lower().endswith(\"sks\")\n or \"ksksk\" in fucked_token.lower()\n or \"sksks\" in fucked_token.lower()\n ):\n fucked_tokens.append(recumpile_text(\"save the turtles!\"))\n\n if decision(fucking_normies_addition) and \"reee\" in fucked_token.lower():\n fucked_tokens.append(recumpile_text(\"fucking normies!\"))\n\n if decision(get_rhymes_probability):\n for rhyme in get_runon_of_rhymes(token, max_runon=max_runon_rhymes):\n fucked_rhyme = recumpile_token(rhyme)\n fucked_tokens.append(fucked_rhyme)\n\n if relevant_emoji:\n fucked_tokens.append(relevant_emoji)\n\n for i, fucked_token in enumerate(fucked_tokens):\n if decision(space_gap_text_probability):\n # TODO: this modification may be better placed elsewhere\n fucked_token = space_gap_text(\n fucked_token,\n min_gap_size=space_gap_text_min_gap_size,\n max_gap_size=space_gap_text_max_gap_size,\n )\n # TODO: discord format options\n if decision(bold_text_probability):\n fucked_token = bold_text(fucked_token)\n elif decision(back_tick_text_probability):\n fucked_token = back_tick_text(fucked_token)\n fucked_tokens[i] = fucked_token\n\n return \" \".join(fucked_tokens)\n\n\n@logged_mutator\ndef bold_text(token: str) -> str:\n if not token.strip(\n string.punctuation\n ): # don't bold tokens of all punctuation as it bugs up rejoining punctuation later *todo: maybe alternate fix?\n return token\n return f\"**{token.strip('*')}**\"\n\n\n@logged_mutator\ndef get_random_rp_action_sentence() -> str:\n more_verbs = []\n more_verbs_probability = 1\n while True:\n if decision(more_verbs_probability):\n additional_verb = get_random_action_verb()\n if decision(0.5): # TODO: config\n additional_verb = Word(additional_verb).lemmatize()\n additional_verb = recumpile_token(additional_verb)\n additional_verb = Word(additional_verb).pluralize()\n more_verbs.append(additional_verb)\n else:\n break\n more_verbs_probability -= more_verbs_probability_decay\n\n noun = get_random_rp_pronoun()\n if decision(0.5): # TODO: config\n noun = Word(noun).lemmatize()\n\n # TODO: add boolean for enable\n noun = recumpile_token(noun)\n noun = Word(noun).pluralize()\n return to_rp_text(f\"{' and '.join(more_verbs)}{' ' if more_verbs else ''}{noun}\")\n\n\n@logged_mutator\ndef lazy_char_subbing(token: str) -> str:\n \"\"\"e.g.you -> u are -> r\"\"\"\n # TODO: better capital replacement\n\n # you -> u, yuu\n token = re.sub(\n \"^y+(o+)?u+$\",\n lambda match: f\"u\" if decision(0.5) else f\"y{'u' * random.randint(1, 4)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # are -> r, arrr\n token = re.sub(\n \"^a+(r+)?e+$\",\n lambda match: f\"r\" if decision(0.5) else f\"a{'r' * random.randint(1, 4)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # with -> wif\n token = re.sub(\n \"^wi+th+$\",\n lambda match: f\"w{'i' * random.randint(1, 4)}{'f' * random.randint(1, 4)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # what -> wat OR wut\n if decision(0.5):\n if decision(0.5):\n token = re.sub(\n \"^wha+t$\",\n lambda match: f\"w{random.choice(['a', 'u']) * random.randint(1, 4)}t\",\n token,\n flags=re.IGNORECASE,\n )\n else:\n token = re.sub(\n \"^wha+t$\",\n lambda match: f\"wh{'u' * random.randint(1, 4)}t\",\n token,\n flags=re.IGNORECASE,\n )\n\n # er -> ur\n token = re.sub(\n \"(e+)r\",\n lambda match: f\"{'u' * (len(match.group(1)) + random.randint(0, 3))}r\",\n token,\n flags=re.IGNORECASE,\n count=random.randint(0, 2),\n )\n\n # easy -> ez\n token = re.sub(\n \"^ea+s+y+$\",\n lambda match: f\"e{'z' * random.randint(1, 3)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n # to,too, -> 2\n token = re.sub(\"to+$\", lambda match: f\"2\", token, flags=re.IGNORECASE)\n return token\n\n\n# TODO: funny -> funni spells -> spellz\n@logged_mutator\ndef common_mispellings(token: str) -> str:\n # TODO: cleanup\n token = re.sub(\n r\"([^\\s])y$\", lambda match: f\"{match.group(1)}{'i'*random.randint(1,1)}\", token\n )\n token = re.sub(\n r\"([^\\s])Y$\", lambda match: f\"{match.group(1)}{'Y'*random.randint(1,2)}\", token\n )\n token = re.sub(\n r\"([^\\s])s$\", lambda match: f\"{match.group(1)}{'z'*random.randint(1,2)}\", token\n )\n token = re.sub(\n r\"([^\\s])S$\", lambda match: f\"{match.group(1)}{'Z'*random.randint(1,2)}\", token\n )\n token = re.sub(\n r\"([^\\s])z$\", lambda match: f\"{match.group(1)}{'s'*random.randint(1,2)}\", token\n )\n token = re.sub(\n r\"([^\\s])Z$\", lambda match: f\"{match.group(1)}{'S'*random.randint(1,2)}\", token\n )\n token = re.sub(\n r\"([eE])([iI])\", lambda match: f\"{match.group(2)}{match.group(1)}\", token\n )\n return token\n\n\n@logged_mutator\ndef fix_punctuation_spacing(text: str) -> str:\n # TODO: this is a meh way to solve punct being incorrectly joined should investigate\n return re.sub(\n r\"([^\\s]) ([!?.,]+)\", lambda match: f\"{match.group(1)}{match.group(2)}\", text\n )\n\n\n@logged_mutator\ndef back_tick_text(token: str) -> str:\n if not token.strip(\n string.punctuation\n ): # don't back_tick tokens of all punctuation as it bugs up rejoining punctuation later *todo: maybe alternate fix?\n return token\n return f\"`{token.strip('`')}`\"\n\n\n# TODO: issues with pyenchant quick\n# patch to make this function do nothing for now\n@logged_mutator\ndef split_compound_word(token: str) -> List[str]:\n # tokens = splitter.split(str(token))\n # if isinstance(tokens, list):\n # return tokens\n # return [tokens]\n return [token]\n\n\n@logged_mutator\ndef add_extra_ed(token: str) -> str:\n return re.sub(\n \"([a-zA-Z]{2,})(d|ed)$\",\n lambda match: f\"{match.group(1)}{'ed' * random.randint(1, 2)}\",\n token,\n flags=re.IGNORECASE,\n )\n\n\n# TODO: grabagey code duplication\n@logged_mutator\ndef custom_censoring(swear_word: str, censor_percent: float = 0.25) -> str:\n if len(swear_word) <= 3: # dont censor stuff this small\n return swear_word\n censor_word_list = list(\"@#$%*\")\n swaps = int(ceil(len(swear_word) * censor_percent))\n indexes = list(range(0, len(swear_word)))\n random.shuffle(indexes)\n indexes = indexes[:swaps]\n\n # avoid censoring the start or end of a string if we are not completely censoring the string\n if not (len(indexes) == len(swear_word)):\n try:\n indexes.remove(0)\n except ValueError:\n pass\n try:\n indexes.remove(len(swear_word) - 1)\n except ValueError:\n pass\n\n for i in indexes:\n swear_word = \"\".join(\n [\n swear_word[w] if w != i else random.choice(censor_word_list)\n for w in range(len(swear_word))\n ]\n )\n return swear_word\n\n\n@logged_mutator\ndef space_gap_text(token: str, min_gap_size: int = 1, max_gap_size: int = 4) -> str:\n gap_size = random.randint(min_gap_size, max_gap_size)\n token_ends = \" \" * (gap_size + 1)\n token = token_ends + (\" \" * gap_size).join(token) + token_ends\n return token\n\n\n@logged_mutator\ndef replace_with_random_synonym(token: str) -> str:\n # TODO: fill in with all synonyms for lulz?\n # TODO: download manual dictionary\n return token\n\n\n@logged_mutator\ndef word_inverter(token: str) -> str:\n # Quases shitty word inverter attempt\n\n word = list(token)\n word.reverse()\n reversed_word = \"\"\n\n for i in word:\n reversed_word += i\n token = reversed_word\n\n return token\n\n\n@logged_mutator\ndef word_upside_downer(token: str) -> str:\n # Quases upside down word transformer\n\n token = upsidedown.transform(token)\n\n return token\n\n\n@logged_mutator\ndef word_upside_downer_preserve_char_order(token: str) -> str:\n new_token = []\n for char_ in token:\n new_token.append(upsidedown.transform(char_))\n return \"\".join(new_token)\n\n\n@logged_mutator\ndef recumpile_line(text: str) -> str:\n new_tokens = []\n for sentence in TextBlob(text).sentences:\n new_tokens += recumpile_sentence(sentence)\n out_str = TreebankWordDetokenizer().detokenize(new_tokens)\n out_str = fix_punctuation_spacing(out_str)\n\n return out_str\n\n\n@logged_mutator\ndef recumpile_text(text: str) -> str:\n # TODO: go sentence by sentence token by token all for sentiment analysis\n lines = []\n\n for line in text.split(\"\\n\"):\n lines.append(recumpile_line(line))\n return \"\\n\".join(lines)\n","sub_path":"recumpiler/mutators.py","file_name":"mutators.py","file_ext":"py","file_size_in_byte":38167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"618223764","text":"# -*- coding: utf-8 -*-\n\n\nimport itertools\n\nfrom django.db import models\n\nfrom common.models import BaseModel\n\n\nclass Card(BaseModel):\n name = models.CharField('名称', max_length=255, default='')\n img = models.CharField('图片', max_length=255, default='NotExist.png')\n\n class Meta:\n abstract = True\n\n\nclass AbilityCard(Card):\n category = models.CharField('分类', max_length=128, default='')\n level = models.PositiveSmallIntegerField('等级', default=0)\n\n\nclass EquipmentCard(Card):\n HEAD = 1\n WEAPON = 2\n BODY = 3\n OFFHAND = 4\n PositionChoices = [\n (HEAD, 'Head'),\n (WEAPON, 'Weapon'),\n (BODY, 'Body'),\n (OFFHAND, 'Offhand'),\n ]\n\n COMMON = 1\n UNCOMMON = 2\n RARE = 3\n EPIC = 4\n RarityChoices = [\n (COMMON, 'C'),\n (UNCOMMON, 'U'),\n (RARE, 'R'),\n (EPIC, 'E'),\n ]\n\n position = models.PositiveSmallIntegerField('位置', choices=PositionChoices, blank=True, null=True)\n rarity = models.PositiveSmallIntegerField('稀有度', choices=RarityChoices, blank=True, null=True)\n level = models.PositiveSmallIntegerField('等级', default=0)\n ability = models.ManyToManyField('Ability', verbose_name='属性', related_name='equipments')\n dlc = models.CharField('DLC', max_length=255, default='')\n shop = models.ForeignKey('Shop', verbose_name='所属商店', related_name='equipments', null=True, blank=True)\n\n def to_dict(self):\n return {\n 'name': self.name,\n 'position': self.get_position_display(),\n 'rarity': self.get_rarity_display(),\n 'level': self.level,\n 'ability': [p.__str__() for p in self.ability.all()]\n }\n\n @classmethod\n def get_all_combinations(cls, equipments):\n head_equips = list(equipments.filter(position=cls.HEAD)) or [None]\n offhand_equips = list(equipments.filter(position=cls.OFFHAND)) or [None]\n weapon_equips = list(equipments.filter(position=cls.WEAPON)) or [None]\n body_equips = list(equipments.filter(position=cls.BODY)) or [None]\n\n all_combinations = itertools.product(head_equips, offhand_equips, weapon_equips, body_equips)\n\n result = []\n for combination in all_combinations:\n ability_dict = {}\n for equipment in combination:\n if equipment:\n abilities = equipment.ability.all()\n for ability in abilities:\n if ability.name not in ability_dict:\n ability_dict[ability.name] = ability.level\n else:\n ability_dict[ability.name] += ability.level\n result.append({\n 'abilities': ability_dict,\n 'equipments': combination,\n })\n\n\n\n\nclass Ability(BaseModel):\n name = models.CharField('名称', max_length=255, default='')\n level = models.PositiveSmallIntegerField('等级', null=True, blank=True)\n\n def __str__(self):\n level_dict = {\n 1: 'I',\n 2: 'II',\n 3: 'III',\n 4: 'IV',\n }\n if self.level:\n if self.name == 'HP':\n return '+{} {}'.format(self.level, self.name)\n else:\n return '{} {}'.format(self.name, level_dict[self.level])\n else:\n return self.name\n\n\nclass Shop(BaseModel):\n name = models.CharField('名称', max_length=128, default='')\n tier = models.PositiveSmallIntegerField('等级', default=0)\n","sub_path":"mysite/GoD/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"416714159","text":"'''\nGiven the root of a binary tree, then value v and depth d, you need to add a row of nodes with value v at the given depth d. The root node is at depth 1.\n\nThe adding rule is: given a positive integer depth d, for each NOT null tree nodes N in depth d-1, create two tree nodes with value v as N's left subtree root and right subtree root. And N's original left subtree should be the left subtree of the new left subtree root, its original right subtree should be the right subtree of the new right subtree root. If depth d is 1 that means there is no depth d-1 at all, then create a tree node with value v as the new root of the whole original tree, and the original tree is the new root's left subtree.\n\nExample 1:\nInput:\nA binary tree as following:\n 4\n / \\\n 2 6\n / \\ /\n 3 1 5\n\nv = 1\n\nd = 2\n\nOutput:\n 4\n / \\\n 1 1\n / \\\n 2 6\n / \\ /\n 3 1 5\n\nExample 2:\nInput:\nA binary tree as following:\n 4\n /\n 2\n / \\\n 3 1\n\nv = 1\n\nd = 3\n\nOutput:\n 4\n /\n 2\n / \\\n 1 1\n / \\\n3 1\nNote:\nThe given d is in range [1, maximum depth of the given tree + 1].\nThe given binary tree has at least one tree node.\n'''\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution(object):\n def addOneRow(self, root, v, d):\n \"\"\"\n :type root: TreeNode\n :type v: int\n :type d: int\n :rtype: TreeNode\n \"\"\"\n if d == 1:\n newroot = TreeNode(v)\n newroot.left = root\n return newroot\n\n self.insrow(root, v, 1, d);\n return root\n\n def insrow(self, root, val, depth, n):\n if not root:\n return\n if depth == n - 1:\n nodeL = root.left\n root.left = TreeNode(val)\n root.left.left = nodeL\n nodeR = root.right\n root.right = TreeNode(val)\n root.right.right = nodeR\n else:\n self.insrow(root.left, val, depth + 1, n)\n self.insrow(root.right, val, depth + 1, n)","sub_path":"Trees/addRowToTree.py","file_name":"addRowToTree.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359068374","text":"import os\nimport lasagne\nfrom lasagne.layers import InputLayer, DenseLayer, batch_norm, ConcatLayer, DropoutLayer, BatchNormLayer, Conv2DLayer, DimshuffleLayer, MaxPool2DLayer, ReshapeLayer, NonlinearityLayer,GRULayer\nimport time\nimport theano\nimport theano.tensor as T\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cPickle\nimport sys\nimport subprocess\nimport scipy.io\nfrom collections import OrderedDict\nimport h5py\nfrom fuel.datasets import H5PYDataset\nfrom lasagne.regularization import regularize_layer_params, l1\nfrom lasagne.init import GlorotUniform,HeNormal\nfrom lasagne.nonlinearities import TemperatureSoftmax\nimport gym\nimport random\n\nclass AttentionLayer(lasagne.layers.Layer):\n def __init__(self, incoming,num_units,demo_input=None,W=lasagne.init.Normal(0.01),\n b=lasagne.init.Constant(0.),**kwargs):\n super(AttentionLayer, self).__init__(incoming, **kwargs)\n\n num_inputs = self.input_shape[1]\n self.num_units=num_units\n self.W = self.add_param(W, (num_inputs, num_units), name='W_attention')\n self.b = self.add_param(b, (num_units,), name='b_attention')\n \n self.demo_input = demo_input\n\n def get_output_for(self, input, **kwargs):\n\n def apply_attention(a_emb,a_dense):\n attn_norm = T.nnet.softmax(a_dense)\n utt_emb = T.dot(attn_norm,a_emb)\n return utt_emb\n \n #compute unnormalized attention scores\n attn_unorm = lasagne.nonlinearities.tanh(T.dot(input,self.W)+self.b)\n attn_unorm = attn_unorm.reshape((-1,500))\n #return attn_unorm\n embs, _ = theano.scan(fn=apply_attention,outputs_info=None,sequences=[self.demo_input,attn_unorm])\n return embs\n\n def get_output_shape_for(self, input_shape):\n return (input_shape[0], self.num_units)\n\n\nclass RepeatLayer(lasagne.layers.Layer):\n \n def __init__(self, incoming, n, **kwargs):\n super(RepeatLayer, self).__init__(incoming, **kwargs)\n self.n = n\n\n def get_output_shape_for(self, input_shape):\n return tuple([input_shape[0], self.n] + list(input_shape[1:]))\n\n def get_output_for(self, input, **kwargs):\n #repeat the input n times\n tensors = [input]*self.n\n stacked = theano.tensor.stack(*tensors)\n dim = [1, 0] + range(2, input.ndim + 1)\n return stacked.dimshuffle(dim)\n\nweights='/misc/scratch03/reco/bhattaga/data/i-vectors/dnn_projects/RL/cloning-priority-hopper-500-na3/epoch_weights/uttCNN-weights-epoch-11.npz'\n\ndef model(demonstration,state):\n \n network={}\n print(\"Building network ...\")\n #read the demonstration\n l_in= lasagne.layers.InputLayer((None,500,3), input_var=demonstration)\n n_batch,maxlen,_ = l_in.input_var.shape\n\n l_encf = lasagne.layers.GRULayer(l_in, num_units=200, name='GRUEncoder', \n mask_input=None, gradient_steps=50,grad_clipping=100,only_return_final=False)\n l_encb = lasagne.layers.GRULayer(l_in, num_units=200, name='GRUEncoderB', \n mask_input=None, gradient_steps=50,grad_clipping=100,backwards=True, only_return_final=False)\n\n l_concat = lasagne.layers.ConcatLayer([l_encf,l_encb],axis=2)\n\n#collect the demonstration embeddings\n demo_emb = lasagne.layers.get_output(l_concat)\n\n#forward prop a minibatch of states\n network['input'] = InputLayer(shape=(None,11), input_var = state)\n batchs,_ = network['input'].input_var.shape\n\n network['ff1'] = DenseLayer(network['input'],100,nonlinearity=lasagne.nonlinearities.tanh,W=lasagne.init.HeNormal())\n\n network['ff2'] = DenseLayer(network['ff1'],100,nonlinearity=lasagne.nonlinearities.tanh,W=lasagne.init.HeNormal())\n\n#need to repeat each of the embedded states seql times\n network['repeat'] = RepeatLayer(network['ff2'],500)\n#repd = lasagne.layers.get_output(network['repeat']).eval({X:x_test})\n\n#Attention processing\n#easiest thing to do is concatenate the state vector with the\n#demonstration along the feature axis and then produce an\n#unnormalized score\n network['pairs'] = ConcatLayer([l_concat,network['repeat']],axis=2)\n#pout = lasagne.layers.get_output(network['pairs']).eval({X:x_test,demonstration:demo})\n\n network['reshape-1'] = lasagne.layers.ReshapeLayer(network['pairs'],(-1,500))\n\n network['attention'] = ReshapeLayer(AttentionLayer(network['reshape-1'],num_units=1,demo_input=demo_emb),(batchs,400))\n#pout = lasagne.layers.get_output(network['attention']).eval({X:x_test,demonstration:demo})\n\n#concatenate the attention embedding with the state embedding\n network['combine'] = ConcatLayer([network['ff2'],network['attention']])\n\n network['ff3'] = DenseLayer(network['combine'],200,nonlinearity=lasagne.nonlinearities.tanh,W=lasagne.init.HeNormal())\n\n network['output'] = DenseLayer(network['ff3'],3,nonlinearity=lasagne.nonlinearities.linear)\n\n return network\n\ndef main():\n\n env = gym.make('Hopper-v1')\n max_steps = 500\n #load a single expert demonstration\n data = np.load('/misc/data15/reco/bhattgau/Rnn/code/Code_/RL/homework/hw1/latest-data/expert-actions-hopper-500.npy')\n data = np.asarray(data,dtype='float32')\n data = np.reshape(data,(data.shape[0],data.shape[1],3)) \n \n sdata = np.load('/misc/data15/reco/bhattgau/Rnn/code/Code_/RL/homework/hw1/latest-data/expert-states-hopper-500.npy')\n sdata = np.asarray(sdata,dtype='float32')\n\n State = T.matrix(name='state',dtype='float32')\n Demo = T.tensor3(name='demo',dtype='float32')\n \n \n network = model(Demo,State)\n \n print(\"Loading trained network\")\n with np.load(weights) as f:\n param_values=[f['arr_%d' % i] for i in range(len(f.files))]\n lasagne.layers.set_all_param_values(network['output'],param_values)\n \n action = lasagne.layers.get_output(network['output'], deterministic=True)\n get_act = theano.function([Demo,State],action)\n\n returns = []\n for i in range(20):\n print('iter', i)\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n obs = np.asarray(obs,dtype='float32')\n obs = obs[None,:]\n obs = np.asarray(obs,dtype='float32')\n \n #sdemo = np.asarray(random.sample(sdata,1),dtype='float32')\n #demo = np.concatenate([sdemo,ademo],axis=2)\n\n demo = np.asarray(random.sample(data,1),dtype='float32')\n noise1 = 0.001*np.random.standard_normal(size=(1,500,3))\n demo = demo+noise1\n demo = np.asarray(demo,dtype='float32')\n\n action = get_act(demo,obs)\n noise = 0.001*np.random.standard_normal(size=(3,))\n action = action + noise\n #action = np.asarray(action,dtype='float64')\n obs, r, done, _ = env.step(action)\n totalr += r\n steps += 1\n #if args.render:\n #env.render()\n if steps % 50 == 0: print(\"%i/%i\"%(steps, max_steps))\n if steps >= max_steps:\n break\n returns.append(totalr)\n\n print('returns', returns)\n print('mean return', np.mean(returns))\n print('std of return', np.std(returns))\n\nif __name__==\"__main__\":\n main()\n\n","sub_path":"hopper_experimens/test-hopper500.py","file_name":"test-hopper500.py","file_ext":"py","file_size_in_byte":6988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"550713707","text":"import matplotlib.pyplot as plt\nimport pandas as pd\ndf_collect = pd.read_excel('2560.xlsx', sheet_name = 'ค่าเฉลี่ยราคาน้ำมันแต่ละเดือน')\nList_x = df_collect['month'].tolist()\nList_y1 = df_collect['Gasohol 91'].tolist()\nList_y2 = df_collect['Gasohol 95'].tolist()\nList_y3 = df_collect['Gasohol E20'].tolist()\nList_y4 = df_collect['Gasohol E85'].tolist()\nList_y5 = df_collect['Ultra Force Diesel'].tolist()\nplt.plot(List_x, List_y1, label='Gasohol 91')\nplt.plot(List_x, List_y2, label='Gasohol 95')\nplt.plot(List_x, List_y3, label='Gasohol E20')\nplt.plot(List_x, List_y4, label='Gasohol E85')\nplt.plot(List_x, List_y5, label='Ultra Force Diesel')\nplt.title('Oil Service Rate Price in 2560')\nplt.xlabel('month')\nplt.ylabel('price')\nplt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\nplt.show()\n","sub_path":"Oil price information/.ipynb_checkpoints/python oil 2560-checkpoint.py","file_name":"python oil 2560-checkpoint.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"511646707","text":"from __future__ import division\nimport tensorflow as tf\n# from rerank_fusion import RerankModel, Reader\nfrom rerank_fusion_v1 import RerankModel, Reader\n# from rerank_fusion_v2 import RerankModel, Reader\nimport os\nimport numpy as np\nimport json\nimport pdb\nfrom util import update_progress\nfrom inference_utils.question_generator_util import SentenceGenerator\n\ntf.flags.DEFINE_string(\"checkpoint_dir\", \"/import/vision-ephemeral/fl302/data/model/%s_kpvaq1_%s\",\n \"Directory for saving and loading model checkpoints.\")\ntf.flags.DEFINE_integer(\"number_of_steps\", 10000000, \"Number of training steps.\")\ntf.flags.DEFINE_integer(\"log_every_n_steps\", 1,\n \"Frequency at which loss and global step are logged.\")\ntf.flags.DEFINE_string(\"result_format\", \"result/%s_vqa_OpenEnded_mscoco_%s2015_baseline_results.json\",\n \"File pattern or comma-separated list of file patterns \"\n \"of image files.\")\nFLAGS = tf.flags.FLAGS\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\nTEST_SET = 'dev'\n# TEST_SET = 'test-dev'\n\n\ndef test(checkpoint_path=None):\n batch_size = 128\n\n # build data reader\n reader = Reader(batch_size=batch_size, subset=TEST_SET, phase='test', version='v1')\n\n if checkpoint_path is None:\n ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir % ('v1',\n 'Fusion'))\n checkpoint_path = ckpt.model_checkpoint_path\n print(checkpoint_path)\n\n # build and restore model\n model = RerankModel(phase='test', version='v1', num_cands=5)\n model.build()\n\n sess = tf.Session(graph=tf.get_default_graph())\n tf.logging.info('Restore from model %s' % os.path.basename(checkpoint_path))\n\n saver = tf.train.Saver()\n saver.restore(sess, checkpoint_path)\n\n # Create the vocabulary.\n to_sentence = SentenceGenerator(trainset='trainval',\n top_ans_file='../iccv_vaq/data/vqa_trainval_top2000_answers.txt')\n\n ans_ids = []\n quest_ids = []\n\n print('Running inference on split %s...' % TEST_SET)\n for i in range(reader.num_batches):\n if i % 10 == 0:\n update_progress(i / float(reader.num_batches))\n outputs = reader.pop_batch()\n model_preds = sess.run(model.preds, feed_dict=model.fill_feed_dict(outputs))\n local_index = model_preds.argmax(axis=1)\n # local_index = outputs[-3].argmax(axis=1) # ivqa\n # local_index = outputs[-4].argmax(axis=1) # vqa\n top_ans = np.array([cand[idx] for idx, cand in zip(local_index, outputs[3])])\n\n ans_ids.append(top_ans)\n quest_id = outputs[-1]\n quest_ids.append(quest_id)\n\n ans_ids = np.concatenate(ans_ids)\n quest_ids = np.concatenate(quest_ids)\n result = [{u'answer': to_sentence.index_to_top_answer(aid),\n u'question_id': qid} for aid, qid in zip(ans_ids, quest_ids)]\n\n # save results\n tf.logging.info('Saving results')\n res_file = FLAGS.result_format % ('v1', TEST_SET)\n json.dump(result, open(res_file, 'w'))\n tf.logging.info('Done!')\n tf.logging.info('#Num eval samples %d' % len(result))\n # ana_ctx.close()\n return res_file, quest_ids\n\n\ndef main():\n from vqa_eval import evaluate_model, write_result_log\n from watch_model import ModelWatcher\n\n def test_model(model_path):\n with tf.Graph().as_default():\n res_file, quest_ids = test(model_path)\n print(res_file)\n acc, details = evaluate_model(res_file, quest_ids,\n version='v1')\n write_result_log(model_path, 'Fusion', acc,\n details)\n return acc\n\n ckpt_dir = FLAGS.checkpoint_dir % ('v1',\n 'Fusion')\n # print(ckpt_dir)\n # test_model(ckpt_dir)\n watcher = ModelWatcher(ckpt_dir, test_model)\n watcher.run()\n\n\nif __name__ == '__main__':\n # test()\n main()\n","sub_path":"test_rerank_fusion_v1.py","file_name":"test_rerank_fusion_v1.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"147915607","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('events', '0012_remove_event_speaker_tba'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='event',\n name='audience',\n field=models.TextField(default=b'oxonly', verbose_name=b'Who can attend'),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='event',\n name='status',\n field=models.TextField(default=b'published', verbose_name=b'Status', choices=[(b'preparation', b'In preparation'), (b'published', b'Published'), (b'cancelled', b'Cancelled')]),\n preserve_default=True,\n ),\n ]\n","sub_path":"talks/events/migrations/0013_auto_20160506_1550.py","file_name":"0013_auto_20160506_1550.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"333952052","text":"import re\n\nn, m = map(int, input().split(\" \"))\n\n# 체스판 입력\nlines = []\nfor _ in range(n):\n line = input()\n line = re.sub(\"W\", \"0\", line)\n line = re.sub(\"B\", \"1\", line)\n lines.append(line)\n\n# 가능한 체스판 2종류\nchess = [[\"01010101\", \"10101010\"] * 4, [\"10101010\", \"01010101\"] * 4]\n# 최저값 체크용\nlow = 64\n\n# 각 체스판 별로\nfor k in range(2):\n # i, j에서 시작하는 체스판으로 체크\n for i in range(n - 8 + 1):\n for j in range(m - 8 + 1):\n # 비트 마스킹을 통해 차이나는 갯수 확인\n count = 0\n for l in range(8):\n count += sum(list(map(int, str(bin(int(chess[k][l], 2) ^ int(lines[i + l][j:j + 8], 2)))[2:])))\n # 최저값 저장\n low = min(low, count)\n\n# 결과값 출력\nprint(low)\n","sub_path":"1018_체스판 다시 칠하기.py","file_name":"1018_체스판 다시 칠하기.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"200083145","text":"#-*- coding:utf-8 -*-\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport os\nimport codecs\nimport csv\nimport datetime\n\n# Yahoo Finance\ndef YahooScrape(code):\n flag = \"1\"\n try:\n url = \"https://stocks.finance.yahoo.co.jp/stocks/detail/?code=\"+str(code)\n html = urlopen(url)\n bsObj = BeautifulSoup(html,\"html.parser\")\n \n name = bsObj.find(\"th\",{\"class\":\"symbol\"}).get_text().replace(\"　\",\" \")\n market = bsObj.find(\"span\",{\"class\":\"stockMainTabName\"}).get_text()\n category = bsObj.find(\"dd\",{\"class\":\"category yjSb\"},{\"a\",\"href\"}).get_text()\n jika = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[3].find(\"strong\").get_text().replace(\",\",\"\").replace(u\"-\",\"\")\n dividend = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[4].find(\"strong\").get_text().replace(\",\",\"\").replace(u\"-\",\"\")\n per = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[5].find(\"strong\").get_text().replace(u\"(連)\",\"\").replace(u\"(単)\",\"\").replace(\" \",\"\").replace(u\"-\",\"\")\n pbr = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[6].find(\"strong\").get_text().replace(u\"(連)\",\"\").replace(u\"(単)\",\"\").replace(\" \",\"\").replace(u\"-\",\"\")\n \n \n owarine = bsObj.findAll(\"td\",{\"class\":\"stoksPrice\"})[1].get_text().replace(\",\",\"\")\n zen_owarine = bsObj.find(\"dd\",{\"class\":\"ymuiEditLink mar0\"},\"strong\").get_text().split(\"（\")[0].replace(\",\",\"\")\n hajimene = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[0].find(\"strong\").get_text().replace(\",\",\"\")\n high = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[1].find(\"strong\").get_text().replace(\",\",\"\")\n low = bsObj.findAll(\"dl\",{\"class\":\"tseDtl\"},\"dd\")[2].find(\"strong\").get_text().replace(\",\",\"\")\n deki = bsObj.findAll(\"dl\",{\"class\":\"tseDtlDelay\"},\"dd\")[1].find(\"strong\").get_text().replace(\",\",\"\")\n baibai = bsObj.findAll(\"dl\",{\"class\":\"tseDtlDelay\"},\"dd\")[2].find(\"strong\").get_text().replace(\",\",\"\")\n zenhi_num = float(owarine) - float(zen_owarine)\n zenhi_per = round(((float(owarine) / float(zen_owarine))-1)*100,2)\n unit = bsObj.findAll(\"dl\",{\"class\":\"tseDtlDelay\"},\"dd\")[8].find(\"strong\").get_text()\n unit2 = float(owarine) * int(unit)\n \n flag = \"0\"\n return(flag,name,market,category,jika,dividend,per,pbr,owarine,zen_owarine,hajimene,high,low,deki,baibai,zenhi_num,zenhi_per,unit,unit2)\n\n except:\n return(flag)\n \n# 時価総額他\nbase = os.path.dirname(os.path.abspath(__name__))\nfilename = os.path.normpath(os.path.join(base,'../stocks_data/market_cmpname.txt'))\nfp = codecs.open(filename,'w','utf-8')\nwriter = csv.writer((fp),lineterminator='\\n')\n\n# 日足\nfilename1 = os.path.normpath(os.path.join(base,'../stocks_data/stock_data.txt'))\nfp1 = codecs.open(filename1,'w','utf-8')\nwriter1 = csv.writer((fp1),lineterminator='\\n')\n\nyesterday = (datetime.date.today()-datetime.timedelta(1)).isoformat()\ntoday = datetime.date.today().isoformat()\n\nfor x in range(1300,9999):\n tmpArray = YahooScrape(x)\n tmpArray1 = list(tmpArray)\n if tmpArray1[0] != \"1\" and tmpArray1[3] != u\"ETF\" and tmpArray1[3] != u\"REIT\" and tmpArray1[2] != u\"東証外国\" :\n tmpArray1[0] = x\n \n # 時価総額\n tmpArray2 = []\n tmpArray2 = tmpArray1[0:7]\n writer.writerow(tmpArray2)\n \n # 日足\n tmpArray3 = []\n tmpArray3.append(today)\n tmpArray3.append(x)\n for x in range(1,4):\n tmpArray3.append(tmpArray1[x])\n for x in range(8,19):\n tmpArray3.append(tmpArray1[x])\n writer1.writerow(tmpArray3)\n print(tmpArray3)\n","sub_path":"jika_stock_scrape.py","file_name":"jika_stock_scrape.py","file_ext":"py","file_size_in_byte":3637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"377655658","text":"import requests\nfrom urllib.parse import unquote # convert HTML format to regular text\nimport sqlite3\nimport copy\nfrom sys import stdout, stderr\nimport random\n\n#import board # WTF this doesn't work?\n\n\"\"\"BOARD MODULE\"\"\"\n\nimport copy\n\n#fout = open('debug.out', 'w')\n\nclass Board:\n def __init__(self, w, h, player0, player1, need):\n self.height = h\n self.width = w\n self.need = need\n\n self.board = []\n for i in range(w):\n self.board.append([' '] * h)\n\n self.pieces = [player0, player1]\n self.cur = 0\n self.npiece = 0\n\n self.mp = {player0 : 0, player1 : 1, ' ' : -1}\n\n def get_board(self):\n h, w = self.height, self.width\n res = []\n for i in range(h):\n res.append([' '] * w)\n\n for i in range(w):\n for j in range(h):\n res[j][i] = self.board[i][j]\n\n return res\n\n def string_version(self):\n attrs = ['height', 'width', 'need', 'board', 'pieces', 'cur', 'npiece', 'mp']\n res = ''\n for attr in attrs:\n res += str(getattr(self, attr)) + '\\n'\n return res\n\n def horizontal(self, x, y):\n if not 0 <= x <= self.width - self.need:\n return -1\n\n if not 0 <= y < self.height:\n return -1\n\n c = self.board[x][y]\n if c == ' ':\n return -1\n\n poses = []\n for i in range(x, x + self.need):\n if self.board[i][y] != c:\n stderr.write('bad %d %d' % (i, y))\n return -1\n poses.append((i, y))\n\n return (self.mp[c], tuple(poses))\n\n def vertical(self, x, y):\n if not 0 <= x < self.width:\n return -1\n\n if not 0 <= y <= self.height - self.need:\n return -1\n\n c = self.board[x][y]\n if c == ' ':\n return -1\n\n poses = []\n for j in range(y, y + self.need):\n if self.board[x][j] != c:\n return -1\n poses.append((x, j))\n\n return (self.mp[c], tuple(poses))\n\n def maindiagonal(self, x, y):\n if not 0 <= x <= self.width - self.need:\n return -1\n\n if not 0 <= y <= self.height - self.need:\n return -1\n\n c = self.board[x][y]\n if c == ' ':\n return -1\n\n poses = []\n\n i, j = x, y\n while j < y + self.need:\n if self.board[i][j] != c:\n return -1\n poses.append((i, j))\n i += 1\n j += 1\n\n return (self.mp[c], tuple(poses))\n\n def antidiagonal(self, x, y):\n if not 0 <= x <= self.width - self.need:\n return -1\n\n if not self.need - 1 <= y < self.height:\n return -1\n\n c = self.board[x][y]\n if c == ' ':\n return -1\n\n poses = []\n\n i, j = x, y\n while i < x + self.need:\n if self.board[i][j] != c:\n return -1\n poses.append((i, j))\n i += 1\n j -= 1\n\n return (self.mp[c], tuple(poses))\n\n def status(self):\n has_space = False\n for i in range(self.width):\n for j in range(self.height):\n if self.board[i][j] == ' ':\n has_space = True\n break\n\n if has_space:\n break\n\n if not has_space:\n return 'draw'\n\n for i in range(self.width):\n for j in range(self.height):\n val = self.vertical(i, j)\n if val != -1:\n return val\n\n val = self.horizontal(i, j)\n if val != -1:\n return val\n\n val = self.maindiagonal(i, j)\n if val != -1:\n return val\n\n val = self.antidiagonal(i, j)\n if val != -1:\n return val\n\n return 'continue'\n\n def put(self, x, y):\n self.board[x][y] = self.pieces[self.cur]\n self.cur ^= 1\n #stderr.write('BOARD: ' + str(self.board))\n return self.status()\n\n\n\"\"\"DATABASE STUFF\"\"\"\n\nc4_db = '__HOME__/c4.db'\nc4comm_db = '__HOME__/c4comm.db'\ntable_name = 'c4_table'\n\ndef create_database():\n conn = sqlite3.connect(c4_db) # connect to that database (will create if it doesn't already exist)\n c = conn.cursor() # make cursor into database (allows us to execute commands)\n ret = True\n try:\n c.execute('CREATE TABLE ' + table_name + '(gameid, board);') # run a CREATE TABLE command\n conn.commit()\n c.execute('INSERT into ' + table_name + ' VALUES (1,?);', (Board(7, 6, 'X', 'O', 4).string_version(), ))\n conn.commit()\n except:\n conn.commit() # commit commands\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = 1').fetchall()\n if len(things) == 0:\n c.execute('INSERT into ' + table_name + ' VALUES (1,?);', (Board(7, 6, 'X', 'O', 4).string_version(), ))\n conn.commit()\n else:\n c.execute('UPDATE ' + table_name + ' SET board = ? WHERE gameid = 1', (Board(7, 6, 'X', 'O', 4).string_version(), ))\n conn.commit()\n\n\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = 1').fetchall()\n assert(len(things) != 0)\n\n ret = False\n\n conn.commit() # commit commands\n conn.close() # close connection to database\n\n conn = sqlite3.connect(c4_db)\n c = conn.cursor()\n conn.commit()\n conn.close()\n\n # try to do this also\n conn = sqlite3.connect(c4comm_db)\n c = conn.cursor()\n try:\n c.execute('CREATE TABLE ' + table_name + '(gameid, who, putx, puty, end);') # run a CREATE TABLE command\n except:\n conn.commit()\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = 1').fetchall()\n if len(things) == 0:\n c.execute('INSERT into ' + table_name + ' VALUES (?, ?, ?, ?, ?)', (1, -1, -1, -1, -1))\n else:\n c.execute('UPDATE ' + table_name + ' SET who = ?, putx = ?, puty = ?, end = ? WHERE gameid = 1', (-1, -1, -1, -1))\n\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = 1').fetchall()\n assert(len(things) != 0)\n\n conn.commit()\n conn.close()\n return ret\n\ndef form_board(s):\n # s is the str\n cboard = Board(7, 6, 'X', 'O', 4)\n attrs = ['height', 'width', 'need', 'board', 'pieces', 'cur', 'npiece', 'mp']\n s = s.splitlines()\n assert(len(s) == 8)\n for i in range(8):\n setattr(cboard, attrs[i], eval(s[i]))\n return cboard\n\ndef get_board():\n conn = sqlite3.connect(c4_db) # connect to that database (will create if it doesn't already exist)\n c = conn.cursor() # make cursor into database (allows us to execute commands)\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = ?', (1, )).fetchall()\n conn.commit()\n assert(len(things))\n for t in things:\n t = t[1]\n stderr.write('things = ' + str(t))\n cboard = form_board(t)\n #stderr.write('--CURRENT BOARD STRING--')\n #stderr.write(cboard.string_version())\n return cboard\n assert(0)\n\ndef put(x, y):\n conn = sqlite3.connect(c4_db) # connect to that database (will create if it doesn't already exist)\n c = conn.cursor()\n cboard = get_board()\n if cboard.board[x][y] != ' ':\n conn.commit()\n conn.close()\n return None\n\n stderr.write('BEFORE call cboard.put: ' + str(cboard.get_board()))\n res = cboard.put(x, y)\n stderr.write('AFTER call cboard.put: ' + str(cboard.get_board()))\n c.execute('UPDATE ' + table_name + ' SET board = ? WHERE gameid = 1', (cboard.string_version(), ))\n conn.commit()\n conn.close()\n return res\n\ndef needs_another_player():\n conn = sqlite3.connect(c4_db)\n c = conn.cursor()\n players = [i[1] for i in c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = -1')]\n print(players)\n return len(players) != 0\n\ndef who_am_i():\n conn = sqlite3.connect(c4_db)\n c = conn.cursor()\n players = None\n try:\n players = [i[1] for i in c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = -1')]\n except sqlite3.OperationalError:\n create_database()\n players = [i[1] for i in c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = -1')]\n\n if len(players) == 1:\n # then there is another player.\n c.execute('DELETE FROM ' + table_name + ' WHERE gameid = -1')\n conn.commit()\n nw = [i for i in c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = -1')]\n assert len(nw) == 0\n res = ('X' if players[0] == 'O' else 'O')\n conn.commit()\n conn.close()\n return res\n else:\n me = 'XO'[random.randint(0, 1)]\n c.execute('INSERT into ' + table_name + ' values (-1, ?)', (me, ))\n conn.commit()\n nw = [i for i in c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = -1')]\n assert len(nw) == 1\n conn.commit()\n conn.close()\n return me\n\ndef request_handler(request):\n request = request['form']\n\n if 'reset' in request:\n create_database()\n return 'reset_successful'\n\n if 'needs_another_player' in request:\n return str(needs_another_player())\n\n if 'who_am_i' in request:\n return who_am_i()\n\n if request['update'] == 'True':\n # this is the current player\n x = int(request['putx'])\n y = int(request['puty'])\n who = request['player']\n res = put(x, y)\n if res == None:\n return 'cannot put'\n\n # otherwise put it there successful!\n conn = sqlite3.connect(c4comm_db)\n c = conn.cursor()\n\n if res == 'continue':\n c.execute('UPDATE ' + table_name + ' SET who = ?, putx = ?, puty = ?, end = ? WHERE gameid = 1', (who, x, y, 'False'))\n conn.commit()\n conn.close()\n return 'continue'\n elif res == 'draw':\n c.execute('UPDATE ' + table_name + ' SET who = ?, putx = ?, puty = ?, end = ? WHERE gameid = 1', (who, x, y, 'True'))\n conn.commit()\n conn.close()\n \n return 'draw'\n\n assert(isinstance(res, tuple))\n assert(len(res) == 2)\n assert(isinstance(res[1], tuple))\n\n # ok let's do this now\n # RETURNS\n spaces = str(res[0])\n for i in res[1]:\n spaces += ' ' + str(i)\n\n c.execute('UPDATE ' + table_name + ' SET who = ?, putx = ?, puty = ?, end = ? WHERE gameid = 1', (who, x, y, spaces))\n conn.commit()\n conn.close()\n\n return spaces\n\n # this is NOT the current player.\n conn = sqlite3.connect(c4comm_db)\n c = conn.cursor()\n\n things = c.execute('SELECT * FROM ' + table_name + ' WHERE gameid = 1').fetchall()\n ret = ''\n who = request['player']\n thing = [thing for thing in things]\n assert(len(thing) == 1)\n thing = thing[0]\n # gameid, who, putx, puty, end\n if thing[1] == who:\n ret = '-1 -1 AND -1'\n else:\n ret = str(thing[2]) + ' ' + str(thing[3]) + ' AND ' + str(thing[4])\n c.execute('UPDATE ' + table_name + ' SET putx = ?, puty = ?, end = ? WHERE gameid = 1', (-1, -1, -1))\n\n conn.commit()\n conn.close()\n return ret\n","sub_path":"Main_Menu_Final/connect4/connect4_query.py","file_name":"connect4_query.py","file_ext":"py","file_size_in_byte":11304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"322276941","text":"from data_creater.utils import db_utils, str_utils\nfrom data_creater.apis import portal_apis\nfrom time import sleep\nfrom data_creater.controllers.renewal_controller import Renewal\n\n\nif __name__ == \"__main__\":\n\n # 测试用例， 学生未续报；调账号 \n \"\"\"\n A：春 → 调入秋\n\n B：秋\n\n A班主任：分子+1 pass \n\n B班主任：不变 pass \n\n \"\"\"\n\n to_uid = 473\n # 春季班\n clazz_ids = [1793]\n\n test = Renewal()\n print(test.get_clazz_master_info_field(clazz_id=1793, master_id=10706952, field='service_num,conversion_num'))\n print(test.get_clazz_master_info_field(clazz_id=1793, master_id=10706958,field='service_num,conversion_num'))\n \n for i in range(len(clazz_ids)):\n portal_apis.apply_clazz_student_nice(to_uid, clazz_id=clazz_ids[i])\n sleep(1)\n # 先查询info表确定服务人数\n service_num,conversion_num=test.get_clazz_master_info_field(clazz_id=1793, master_id=10706952, field='service_num,conversion_num')\n print(service_num, conversion_num)\n print(test.get_clazz_master_info_field(clazz_id=1793, master_id=10706958,field='service_num,conversion_num'))\n print(\"报班成功!\")\n\n sleep(1)\n \n \n uid = 475\n clazz_id = 1987\n portal_apis.apply_clazz_student_nice(uid, clazz_id=clazz_id)\n\n sleep(1)\n\n # # # 报秋季班、五年级数学\n order_id = test.get_order_id(uid,clazz_id)\n portal_apis.exchange_order_user(order_id=order_id, to_user_id=to_uid)\n sleep(1)\n # info表分母不变算惩罚，分子不变\n new_service_num,new_conversion_num=test.get_clazz_master_info_field(clazz_id=1793, master_id=10706952, field='service_num,conversion_num')\n print(new_service_num, new_conversion_num)\n new_service_num,new_conversion_num=test.get_clazz_master_info_field(clazz_id=1793, master_id=10706958, field='service_num,conversion_num')\n print(new_service_num, new_conversion_num)\n # assert new_conversion_num-conversion_num == 0, \"调小班后分子变化了\" \n # assert new_service_num-service_num == 0, \"调小班后分母变化了\" \n # print(\"购课后调小班功能测试通过!\")\n","sub_path":"test_case48/test_case19.py","file_name":"test_case19.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"237896101","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('core', '0002_auto_20150720_1657'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='content',\n name='author',\n field=models.ForeignKey(default=None, blank=True, to=settings.AUTH_USER_MODEL, null=True, verbose_name='\\u0440\\u0435\\u0434\\u0430\\u043a\\u0442\\u043e\\u0440'),\n ),\n migrations.AlterField(\n model_name='content',\n name='pub_date',\n field=models.DateTimeField(default=datetime.datetime.now, null=True, verbose_name='\\u0434\\u0430\\u0442\\u0430 \\u0438 \\u0432\\u0440\\u0435\\u043c\\u044f \\u043f\\u0443\\u0431\\u043b\\u0438\\u043a\\u0430\\u0446\\u0438\\u0438', blank=True),\n ),\n ]\n","sub_path":"src/admin_app/core/migrations/0003_auto_20150722_1114.py","file_name":"0003_auto_20150722_1114.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"587975022","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@author:SiriYang\n@file: AlertView.py\n@time: 2020.2.2 13:44\n\"\"\"\n\nimport ui\n\nclass AlertView (ui.View):\n\t\n\tdef __init__(self,title=\"Alert\",info=\"\",act=None):\n\t\tself.value=0\n\t\tself.act=act\n\t\t\n\t\tself.name=title\n\t\tself.frame=(0,0,250,120)\n\t\tself.background_color=\"white\"\n\t\t\n\t\tself.infoLabel=ui.TextView()\n\t\tself.infoLabel.text=info\n\t\tself.infoLabel.font=(\"\",16)\n\t\tself.infoLabel.text_color=\"#5a5a5a\"\n\t\tself.infoLabel.alignment=ui.ALIGN_CENTER\n\t\tself.infoLabel.frame=(0,20,self.width,self.height-50-20)\n\t\t\n\t\tself.okBtn=ui.Button()\n\t\tself.okBtn.title=\"确定\"\n\t\tself.okBtn.border_width=1\n\t\tself.okBtn.frame=(-1,self.height-50,self.width/2+1,50+1)\n\t\tself.okBtn.background_color=\"white\"\n\t\tself.okBtn.border_color=\"#eaeaea\"\n\t\t#self.okBtn.corner_radius=5\n\t\tself.okBtn.action=self.okAct\n\t\t\n\t\tself.closeBtn=ui.Button()\n\t\tself.closeBtn.title=\"取消\"\n\t\tself.closeBtn.border_width=1\n\t\tself.closeBtn.frame=(self.width/2,self.height-50,self.width/2,50+1)\n\t\tself.closeBtn.background_color=\"white\"\n\t\tself.closeBtn.border_color=\"#eaeaea\"\n\t\t#self.closeBtn.corner_radius=5\n\t\tself.closeBtn.action=self.closeAct\n\t\t\n\t\tself.add_subview(self.infoLabel)\n\t\tself.add_subview(self.okBtn)\n\t\tself.add_subview(self.closeBtn)\n\t\t\n\t\tself.present(\"sheet\")\n\t\t\n\tdef will_close(self):\n\t\tpass\n\t\n\tdef okAct(self,sender):\n\t\tself.value=1\n\t\tif(self.act!=None):\n\t\t\tself.act()\n\t\tself.close()\n\t\n\tdef closeAct(self,sender):\n\t\tself.value=0\n\t\tself.close()\t\t\n\t\t\nif __name__ == \"__main__\":\n\tv=AlertView(\"test\",\"123456\")\n\t\n","sub_path":"UI/AlertView.py","file_name":"AlertView.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"242172136","text":"from prepro import downloadFiles, preProcess\nfrom train import train, predict\nfrom storage import uploadFileAzure\nfrom config import Config as cfg\nimport os\n\ndef pipelineRun(downloadYN, preprocessYN, predictYN, leagues, firstSeason, firstSeasonTest, lastSeason, trainData, trainYN):\n\n if downloadYN:\n downloadFiles(\n firstSeason=firstSeason, \n firstSeasonTest=firstSeasonTest, \n lastSeason=lastSeason, \n trainData=trainData, \n leagues=leagues\n )\n print('Files downloaded!')\n\n if preprocessYN:\n preProcess(\n firstSeason=firstSeason, \n firstSeasonTest=firstSeasonTest, \n lastSeason=lastSeason, \n trainData=trainData, \n leagues=leagues\n )\n print('Preprocessing done!')\n\n if trainYN:\n train(leagues=leagues)\n print('Training done!')\n\n if predictYN:\n predict(leagues=leagues)\n print('Predictions done!')\n\n uploadFileAzure(cfg.PREDICTED_FPATH, cfg.AZURE_CONNECTION_STRING, cfg.AZURE_CONTAINER_NAME)\n print('Uploaded to Azure!')\n\nif __name__ == \"__main__\":\n pipelineRun(\n leagues=cfg.LEAGUES,\n firstSeason=cfg.FIRST_SEASON, \n firstSeasonTest=cfg.FIRST_SEASON_TEST, \n lastSeason=cfg.LAST_SEASON, \n downloadYN=False,\n preprocessYN=True,\n trainData=True,\n trainYN=True,\n predictYN=True,\n )","sub_path":"update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"157384461","text":"import os\n\n\n\n# 求1-100的累加和\nsums = 0\nfor i in range(101):\n sums += i\nprint(sums)\n\n\ndef adds(n=10):\n if n == 0:\n return n\n return n + adds(n - 1)\n\n\n# 1: n=10 + adds(n=10 - 1) -> 10 + adds(9)\n# 2: 10 + n=9 + adds(n=9 - 1) ->\n# ....\n# 10: 10 + 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 + adds(0)\nprint(adds(2))\n\n\ndirC = fileC = 0\ndef getCount(path=\"/\"):\n for files in os.listdir(path):\n fileAbs = os.path.join(path, files)\n if os.path.isdir(fileAbs):\n global dirC\n dirC += 1\n getCount(fileAbs)\n else:\n global fileC\n fileC += 1\n return dirC, fileC\n\n\ndirCount, fileCount = getCount(\"/Users/liuchao/Documents\")\nprint('dirCount: {}, fileCount: {}'.format(dirCount, fileCount))\n\n","sub_path":"pythonscripts/17.dgfunctions.py","file_name":"17.dgfunctions.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"550422309","text":"\"\"\"station_system URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.11/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.conf.urls import url, include\n 2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls import url\n# from django.contrib import admin\nfrom dp import views\nfrom django.views.generic.base import RedirectView\n\nurlpatterns = [\n # url(r'^admin/', admin.site.urls),\n url(r'^$', views.home, name='home'),\n url(r'^login/', views.login, name='login'),\n # url(r'^index/', views.index, name='index'),\n # url(r'^index2/', views.index2, name='index2'),\n url(r'^check_ticket/', views.check_ticket, name='check_ticket'),\n url(r'^check_re_al_pa_info/', views.check_re_al_pa_info, name='check_re_al_pa_info'),\n url(r'^check_all_pa_ti/', views.check_all_pa_ti, name='check_all_pa_ti'),\n url(r'^buy_ticket/', views.buy_ticket, name='buy_ticket'),\n url(r'^alter_ticket/', views.alter_ticket, name='alter_ticket'),\n url(r'^return_ticket/', views.return_ticket, name='return_ticket'),\n url(r'^del_all_car/', views.del_all_car, name='del_all_car'),\n url(r'^insert_many_car/', views.insert_many_car, name='insert_many_car'),\n url(r'^del_one_car/', views.del_one_car, name='del_one_car'),\n url(r'^insert_one_car/', views.insert_one_car, name='insert_one_car'),\n url(r'^check_account_info/', views.check_account_info, name='check_account_info'),\n url(r'^change_account_password/', views.change_account_password, name='change_account_password'),\n url(r'^account_delete/', views.account_delete, name='account_delete'),\n url(r'^check_account_isExist/', views.check_account_isExist, name='check_account_isExist'),\n url(r'^create_account/', views.create_account, name='create_account'),\n\n\n url(r'^favicon\\.ico$', RedirectView.as_view(url=r'static/images/favicon.ico')),\n url(r'^\\S+/*$', views.redirect_home, name='redirect_home'),\n\n]\n","sub_path":"station_system/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"501177347","text":"############################################################################################\n#\n# Project: Peter Moss Acute Myeloid & Lymphoblastic Leukemia AI Research Project\n# Repository: ALL Detection System 2020\n# Project: AllDS2020 CNN\n#\n# Author: Adam Milton-Barker (AdamMiltonBarker.com)\n# Contributors:\n# Title: Data helper class\n# Description: Data functions for the Tensorflow 2.0 AllDS2020 CNN.\n# License: MIT License\n# Last Modified: 2020-03-12\n#\n############################################################################################\n\nimport cv2\nimport pathlib\nimport random\nimport os\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom pathlib import Path\n\nfrom numpy.random import seed\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.utils import shuffle\nfrom scipy import ndimage\nfrom skimage import transform as tm\n\nfrom Classes.Helpers import Helpers\nfrom Classes.Augmentation import Augmentation\n\n\nclass Data():\n \"\"\" Data class\n \n Data functions for the Tensorflow 2.0 AllDS2020 CNN.\n \"\"\"\n\n def __init__(self):\n \"\"\" Initializes the class. \"\"\"\n\n self.Helpers = Helpers(\"Data\", False)\n \n self.seed = self.Helpers.confs[\"cnn\"][\"data\"][\"seed\"]\n self.dim = self.Helpers.confs[\"cnn\"][\"data\"][\"dim\"]\n \n seed(self.seed)\n random.seed(self.seed)\n \n self.data = []\n self.labels = []\n\n self.Helpers.logger.info(\"Data class initialization complete.\")\n\n def do_im_process(self):\n \"\"\" Sorts the training data and labels for your model. \"\"\"\n \n aug = Augmentation()\n\n data_dir = pathlib.Path(\n self.Helpers.confs[\"cnn\"][\"data\"][\"train_dir\"])\n data = list(data_dir.glob(\n '*' + self.Helpers.confs[\"cnn\"][\"data\"][\"file_type\"]))\n\n count = 0\n neg_count = 0\n pos_count = 0\n \n augmented_data = []\n augmented_labels = []\n\n for rimage in data:\n fpath = str(rimage)\n fname = os.path.basename(rimage)\n label = 0 if \"_0\" in fname else 1\n\n image = self.resize(fpath, self.dim)\n\n if image.shape[2] == 1:\n image = np.dstack(\n [image, image, image]) \n\n augmented_data.append(image.astype(np.float32)/255.)\n augmented_labels.append(label)\n\n augmented_data.append(aug.grayscale(image))\n augmented_labels.append(label)\n \n augmented_data.append(aug.equalize_hist(image))\n augmented_labels.append(label)\n\n horizontal, vertical = aug.reflection(image)\n augmented_data.append(horizontal)\n augmented_labels.append(label)\n\n augmented_data.append(vertical)\n augmented_labels.append(label)\n\n augmented_data.append(aug.gaussian(image))\n augmented_labels.append(label)\n\n augmented_data.append(aug.translate(image))\n augmented_labels.append(label)\n\n augmented_data.append(aug.shear(image))\n augmented_labels.append(label)\n\n self.data, self.labels = aug.rotation(image, label, augmented_data, augmented_labels)\n\n if \"_0\" in fname:\n neg_count += 9\n else:\n pos_count += 9\n count += 9\n\n self.shuffle()\n self.convert_data()\n self.encode_labels()\n \n self.Helpers.logger.info(\"Raw data: \" + str(count))\n self.Helpers.logger.info(\"Raw negative data: \" + str(neg_count))\n self.Helpers.logger.info(\"Raw positive data: \" + str(count))\n self.Helpers.logger.info(\"Augmented data: \" + str(self.data.shape))\n self.Helpers.logger.info(\"Labels: \" + str(self.labels.shape))\n \n self.get_split()\n\n def convert_data(self):\n \"\"\" Converts the training data to a numpy array. \"\"\"\n\n self.data = np.array(self.data)\n self.Helpers.logger.info(\"Data shape: \" + str(self.data.shape))\n\n def encode_labels(self):\n \"\"\" One Hot Encodes the labels. \"\"\"\n\n encoder = OneHotEncoder(categories='auto')\n\n self.labels = np.reshape(self.labels, (-1, 1))\n self.labels = encoder.fit_transform(self.labels).toarray()\n self.Helpers.logger.info(\"Labels shape: \" + str(self.labels.shape))\n\n def shuffle(self):\n \"\"\" Shuffles the data and labels. \"\"\"\n\n self.data, self.labels = shuffle(self.data, self.labels, random_state=self.seed)\n\n def get_split(self):\n \"\"\" Splits the data and labels creating training and validation datasets. \"\"\"\n\n self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(\n self.data, self.labels, test_size=0.255, random_state=self.seed)\n\n self.Helpers.logger.info(\"Training data: \" + str(self.X_train.shape))\n self.Helpers.logger.info(\"Training labels: \" + str(self.y_train.shape))\n self.Helpers.logger.info(\"Validation data: \" + str(self.X_test.shape))\n self.Helpers.logger.info(\"Validation labels: \" + str(self.y_test.shape))\n\n def resize(self, path, dim):\n \"\"\" Resizes an image to the provided dimensions (dim). \"\"\"\n\n return cv2.resize(cv2.imread(path), (dim, dim))","sub_path":"CNN/Classes/Data.py","file_name":"Data.py","file_ext":"py","file_size_in_byte":5353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"443185952","text":"import pandas as pd\nimport numpy as np\nimport xgboost as xgb\nfrom xgboost import XGBClassifier\nfrom sklearn.linear_model import LogisticRegression\nimport pandas as pd\nimport pickle\nimport datetime\nimport time\nimport os\n\ndef train_with_xgboost(train_x ,train_y):\n dump_path = 'xgboost.pkl'\n param = {'max_depth': 3, 'learning_rate': 0.1,\n 'n_estimators': 100, 'silent': True,\n 'objective': \"binary:logistic\",\n 'gamma': 0, 'min_child_weight': 1, 'max_delta_step': 0, 'subsample': 1,\n 'colsample_bytree': 1, 'colsample_bylevel': 1,\n 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1,\n 'base_score': 0.5, 'seed': 123}\n train_model = XGBClassifier(**param)\n train_model.fit(X=train_x, y=train_y)\n pickle.dump(train_model, open(dump_path, 'w'))\n\n return train_model\n\ndef train_with_lr(train_x, train_y):\n train_model = LogisticRegression()\n train_model.fit(X=train_x, y=train_y)\n return train_model\n\nfrom sklearn.linear_model import Ridge\nfrom sklearn.linear_model import Lasso\n\ndef train_with_ridge(train_x, train_y):\n clf = Ridge(alpha=1.0)\n clf.fit(train_x, train_y)\n return clf\n # pred_date = clf.predict(val_x)\n\ndef train_with_lasso(train_x, train_y):\n clf = Lasso(alpha=0.1)\n clf.fit(train_x, train_y)\n# pred_date = clf.predict(val_x)\n\n\n\ndef train(f1_path, l_path, f2_path):\n\n feature_start= '2017-04-01'\n label_start = '2017-05-01'\n train_x = pd.read_csv(f1_path)\n train_y = pd.read_csv(l_path)\n val_x = pd.read_csv(f2_path)\n\n\n train = pd.merge(train_x, train_y, how='left', on='user_id')\n\n #train.drop((pd.isna(train['bought'])), inplace=True)\n\n # train['bought'].map(lambda x: ( 0 if pd.isna(x) else x))\n train = train[np.isnan(train['bought']) == False]\n clf_model = train_with_xgboost(train[['score_level_1','score_level_2',\n 'score_level_3','o_sku_sum','action_1','action_2','o_area','max_count']],train['bought'])\n #val_x = val_x[['score_level_1','score_level_2',\n # 'score_level_3','o_sku_sum','action_1','action_2','o_area','max_count']]\n val_prob = clf_model.predict_proba(val_x[['score_level_1','score_level_2',\n 'score_level_3','o_sku_sum','action_1','action_2','o_area','max_count']])[:,1]\n val_x['prob'] = pd.DataFrame(val_prob)\n val_x['bought'] = val_x['prob'].map(lambda x: 1 if x >= 0.5 else 0)\n val_x = val_x.sort_values(by = ['prob'], ascending = False)\n val_user = val_x['user_id']\n\n val_x = val_x.drop(['user_id', 'prob'], axis=1)\n #val_x = val_x.head(50000)\n # train = train.fillna(0)\n x = train.drop(['user_id','earliest_date'], axis =1)\n y = train['earliest_date'].map(\n lambda x: (datetime.datetime.strptime(x, '%Y-%m-%d') - datetime.datetime.strptime(feature_start, '%Y-%m-%d')).days if not pd.isna(x) else np.nan)\n x = x.fillna(0)\n y = y.fillna(30)\n val_x = val_x.fillna(0)\n reg_model = train_with_ridge(x,y)\n val_date = reg_model.predict(val_x)\n result = pd.concat([val_user, pd.DataFrame(val_date,columns=['date'])], axis = 1)\n result['date'] = result['date'].fillna(30)\n\n result['date'] = result['date'].map(lambda x: abs(int(x)))\n result = result.sort_values(by=['date'])\n result['date'] = result['date'].map(\n lambda x: (datetime.datetime.strptime(label_start, '%Y-%m-%d') + datetime.timedelta(days=x)).strftime(\"%Y-%m-%d\") )\n result = result.head(50000)\n\n\n result.to_csv('./submission.csv',index=False, index_label=False, header = ['user_id','pred_date'])\n\n\noutputpath = \"/Users/chenghanni/Documents/data\"\n\nf1_start = \"2016-07-01\"\nf1_end = \"2017-04-01\"\nf2_start = \"2016-08-01\"\nf2_end = \"2017-05-01\"\nl1_start = \"2017-04-01\"\nl1_end = \"2017-05-01\"\n\nf1_path = os.path.join(outputpath ,\"feat_\"+ f1_start + \"_\" + f1_end, \"f.csv\")\nf2_path = os.path.join(outputpath ,\"feat_\"+ f2_start + \"_\" + f2_end, \"f.csv\")\nl_path = os.path.join(outputpath ,\"label_\"+ l1_start + \"_\" + l1_end, \"l.csv\")\ntrain(f1_path, l_path, f2_path)\n","sub_path":"python/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"261385577","text":"#!/hpcf/apps/python/install/2.7.13/bin/python\n# exec(open(\"/home/yli11/HemTools/bin/change_seq_off_target_bed.py\").read())\n\n\nimport sys\nimport pandas as pd\nimport numpy as np\nimport os\n\nfile = sys.argv[1]\nis_PAM_end = int(sys.argv[2])\nPAM_length = int(sys.argv[3])\n\nif is_PAM_end == 0:\n\tprint (\"not implemented\")\n\texit()\n\ndf = pd.read_csv(file,sep=\"\\t\")\ndf['Site_Sequence'] = df['Site_Sequence'].fillna(\"\")\ndef get_seq(r):\n\t\n\tif r['Site_Sequence'] != \"\":\n\t\treturn r['Site_Sequence']\n\telse:\n\t\t# print (r.name,r['Site_Sequence_Gaps_Allowed'].replace(\"-\",\"\"))\n\t\treturn r['Site_Sequence_Gaps_Allowed'].replace(\"-\",\"\")\n\t\t\nprint (df.shape)\ndf['seq'] = df.apply(get_seq,axis=1)\ndf['seq'] = [x[:-PAM_length] for x in df.seq]\ndf['seq_length'] = [len(x) for x in df.seq]\n\n\n\ndef get_new_start(r,PAM_length):\n\tif r['Strand'] == \"+\":\n\t\treturn r['Start']\n\telse:\n\t\treturn r['Start']+PAM_length\n\ndf['new_start'] = df.apply(lambda r:get_new_start(r,PAM_length),axis=1)\n\ndf['new_end'] = df['new_start']+df['seq_length']\n\ndf[['#Chromosome','new_start','new_end','seq','Nuclease_Read_Count','Strand']].to_csv(\"test.bed\",sep=\"\\t\",header=False,index=False)\ncommand = \"module load bedtools/2.25.0;bedtools getfasta -fi /home/yli11/Data/Human/hg38/fasta/hg38.fa -fo test.tab -bed test.bed -s -name -tab\"\nos.system(command)\ntmp = pd.read_csv(\"test.tab\",sep=\"\\t\",header=None)\nfor a,b in tmp.values:\n\tif a.upper() != b.upper():\n\t\tprint (a,b)\n\nfor s,d in df.groupby('seq_length'):\n\tout = \"off_target_%sbp.bed\"%(s)\n\td[['#Chromosome','new_start','new_end','seq','Nuclease_Read_Count','Strand','Genomic Coordinate']].to_csv(out,sep=\"\\t\",header=False,index=False)\n\t\n\t\n\t\n\t\n\t","sub_path":"bin/change_seq_off_target_bed.py","file_name":"change_seq_off_target_bed.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"147198689","text":"#!/usr/bin/env python\n\"\"\"Status and control for the enclosure controller.\n\nTo do:\n- Enhance buttons to handle None as a state\n\nHistory:\n2005-08-02 ROwen\n2005-08-15 ROwen Modified to not show enclosure enable (it doesn't seem to do anything).\n2005-10-13 ROwen Removed unused globals.\n2006-05-04 ROwen Modified to use telmech actor instead of tcc (but not all commands supported).\n2007-06-26 ROwen Added support for Eyelid controls\n Allow group control of lights, louvers and heaters (and hiding details)\n2007-06-28 ROwen Added support for mirror covers and tertiary rotation.\n2007-07-02 ROwen TertRot widget now can track the current state even if it goes to unknown.\n Modified tertRot widget to display \"?\" if unknown.\n Both changes are due to improvements in RO.Wdg.OptionMenu.\n2007-07-05 ROwen Fix PR 630: tert rot widgets sometimes not properly enabled after rot.\n Device labels now use \" \" instead of \"_\".\n Added a small margin along the right edge.\n2008-01-04 ROwen Fix PR 701: heater All On/All Off buttons are reversed.\n2008-04-28 ROwen Display tert rot \"Home\" position correctly (and as a warning).\n2008-07-01 ROwen StatusCmdWdg no longer requires statusBar as an argument.\n Each widget is disabled while the command it triggered is running.\n Added a Cancel button to cancel all executing commands.\n2008-07-02 ROwen\tCommented out a diagnostic print statement.\n2008-07-17 ROwen Added tertiary rotation Restore button.\n2010-11-05 ROwen Eyelids now have status summary and Open All and Close All buttons.\n2012-10-26 ROwen Modified to use separate checkbuttons to toggle state and labels to display state;\n this works around a bug in Tk (indicatoron is ignored on MacOS X)\n and may slightly clarify the interface because command and state are separate.\n2012-11-13 ROwen Bug fix: the individual device widges were being mishandled for show/hide.\n This caused an incorrect display and TUI would freeze when showing devices.\n Stop using Checkbutton indicatoron=False because it is no longer supported on MacOS X.\n2014-05-19 ROwen Made detection of a running tertiary rotation command more robust.\n2015-11-02 ROwen Switched from arr.any/all to all/any(arr) for numpy arrays\n\"\"\"\nimport numpy\nimport Tkinter\nif __name__ == '__main__':\n import RO.Comm.Generic\n RO.Comm.Generic.setFramework(\"tk\")\nimport RO.Alg\nimport RO.Constants\nimport RO.Wdg\nimport TUI.TUIModel\nimport TelMechModel\n\n_HelpURL = \"Misc/EnclosureWin.html\"\n\n_ColsPerDev = 3 # number of columns for each device widget\n\nclass DevStateWdg(RO.Wdg.Label):\n \"\"\"Widget that displays a summary of device state.\n \n Note: this widget registers itself with the TelMech model\n so once created it self-updates.\n \n Inputs:\n - master: master widget\n - catInfo: category info from the TelMech model\n - onIsNormal: if True/False then severity is normal if all are on/off\n - patternDict: dict of state: (state string, severity)\n where state is a tuple of bools or ints (one per device)\n - **kargs: keyword arguments for RO.Wdg.Label\n \"\"\"\n def __init__(self,\n master,\n catInfo,\n onIsNormal = True,\n patternDict = None,\n **kargs):\n kargs.setdefault(\"borderwidth\", 2)\n kargs.setdefault(\"relief\", \"sunken\")\n kargs.setdefault(\"anchor\", \"center\")\n RO.Wdg.Label.__init__(self, master, **kargs)\n self.patternDict = patternDict or {} \n self.onIsNormal = onIsNormal\n catInfo.addCallback(self.updateState)\n\n def updateState(self, catInfo):\n isCurrent = all(catInfo.devIsCurrent)\n stateStr, severity = self.getStateStrSev(catInfo.devState, catInfo)\n self.set(stateStr, isCurrent = isCurrent, severity = severity)\n\n def getStateStrSev(self, devState, catInfo):\n \"\"\"Return state string associated with specified device state\"\"\"\n if any(numpy.isnan(devState)):\n return (\"?\", RO.Constants.sevWarning)\n\n if self.patternDict:\n statusStrSev = self.patternDict.get(tuple(devState.astype(numpy.bool)))\n if statusStrSev is not None:\n return statusStrSev\n \n if all(devState):\n if self.onIsNormal:\n severity = RO.Constants.sevNormal\n else:\n severity = RO.Constants.sevWarning\n return (\"All \" + catInfo.stateNames[1], severity)\n elif all(devState):\n return (\"Some \" + catInfo.stateNames[1], RO.Constants.sevWarning)\n\n # all are off\n if self.onIsNormal:\n severity = RO.Constants.sevWarning\n else:\n severity = RO.Constants.sevNormal\n return (\"All \" + catInfo.stateNames[0], severity)\n\nclass EyelidsStateWdg(DevStateWdg):\n def __init__(self, master, helpURL=None):\n self.model = TelMechModel.getModel()\n DevStateWdg.__init__(self,\n master = master,\n catInfo = self.model.catDict[\"Eyelids\"],\n helpText = \"State of the eyelids\",\n helpURL = helpURL,\n )\n self.catInfo = self.model.catDict[\"Eyelids\"]\n self.tertRot = self.model.tertRot\n self.model.tertRot.addCallback(self.updateState, callNow=False)\n\n def updateState(self, *args, **kargs):\n DevStateWdg.updateState(self, self.catInfo)\n\n def getStateStrSev(self, devState, catInfo):\n \"\"\"Return state string associated with specified device state\"\"\"\n devState = self.catInfo.devState\n \n if any(numpy.isnan(devState)):\n return (\"?\", RO.Constants.sevWarning)\n\n if all(devState):\n return (\"All \" + self.catInfo.stateNames[1], RO.Constants.sevNormal)\n elif not any(devState):\n return (\"All \" + self.catInfo.stateNames[0], RO.Constants.sevWarning)\n \n # indicate whether the eyelid at the current port is open\n currPortName, isCurrent = self.tertRot.getInd(0)\n if currPortName is not None:\n currPortName = currPortName.upper()\n if currPortName is None:\n return (\"Some \" + self.catInfo.stateNames[1], RO.Constants.sevNormal)\n devInd = self.catInfo.devIndDict.get(currPortName.upper())\n if devInd is None:\n return (\"Some \" + self.catInfo.stateNames[1], RO.Constants.sevWarning)\n if devState[devInd]:\n return (\"%s Open\" % (currPortName,), RO.Constants.sevNormal)\n # matching port is NOT open\n return (\"%s Closed\" % (currPortName,), RO.Constants.sevWarning)\n \n\nclass StatusCommandWdg (Tkinter.Frame):\n def __init__(self,\n master,\n **kargs):\n \"\"\"Create a new widget to show status for and configure the enclosure controller\n \"\"\"\n Tkinter.Frame.__init__(self, master, **kargs)\n self.model = TelMechModel.getModel()\n self.tuiModel = TUI.TUIModel.getModel()\n \n # dict of category: sequence of detailed controls (for show/hide)\n self.detailWdgDict ={}\n \n self.pendingCmds = []\n\n self.col = 0\n self.row = 0\n self.statusRow = 0\n \n self.addCategory(\"Shutters\")\n self.row += 1\n self.addCategory(\"Fans\")\n\n self.startNewColumn()\n RO.Wdg.StrLabel(\n master = self,\n text = \"Mir Covers\",\n helpURL = _HelpURL,\n ).grid(row=self.row, column=self.col)\n self.row += 1\n coversFrame = Tkinter.Frame(master = self)\n self.coversUserWdg = RO.Wdg.Checkbutton(\n master = coversFrame,\n autoIsCurrent = True,\n command = self.doCoversCmd,\n helpText = \"Open the primary mirror covers?\",\n helpURL = _HelpURL,\n )\n self.coversUserWdg.pack(side=\"left\")\n self.coversCurrWdg = RO.Wdg.Label(\n master = coversFrame,\n width = 6,\n anchor = \"w\",\n helpText = \"State of the primary mirror covers?\",\n helpURL = _HelpURL,\n )\n self.coversCurrWdg.pack(side=\"left\")\n coversFrame.grid(row=self.row, column=self.col, sticky=\"w\")\n self.row += 3\n self.model.covers.addIndexedCallback(self.updateCovers)\n \n RO.Wdg.StrLabel(\n master = self,\n text = \"Tert Rot\",\n helpURL = _HelpURL,\n ).grid(row=self.row, column=self.col)\n self.row += 1\n self.tertRotWdg = RO.Wdg.OptionMenu(\n master = self,\n items = self.model.catDict[\"Eyelids\"].devDict.keys(),\n noneDisplay = \"?\",\n ignoreCase = True,\n autoIsCurrent = True,\n defMenu = \"Default\",\n callFunc = self.tertRotEnable,\n helpText = \"Tertiary rotation\",\n helpURL = _HelpURL,\n )\n self.tertRotWdg.grid(row=self.row, column=self.col)\n self.row += 1\n self.tertRotApplyWdg = RO.Wdg.Button(\n master = self,\n text = \"Apply\",\n callFunc = self.doTertRotApply,\n helpText = \"Apply tertiary rotation\",\n helpURL = _HelpURL,\n )\n self.tertRotApplyWdg.grid(row=self.row, column=self.col)\n self.row += 1\n self.tertRotCurrWdg = RO.Wdg.Button(\n master = self,\n text = \"Current\",\n callFunc = self.doTertRotRestore,\n helpText = \"Show current tertiary rotation\",\n helpURL = _HelpURL,\n )\n self.tertRotCurrWdg.grid(row=self.row, column=self.col)\n self.row += 1\n self.model.tertRot.addIndexedCallback(self.updateTertRot)\n \n self.startNewColumn()\n self.eyelidsState = EyelidsStateWdg(\n master = self,\n helpURL = _HelpURL,\n )\n self.eyelidsOpenWdg = RO.Wdg.Button(\n master = self,\n text = \"Open All\",\n callFunc = self.doEyelidsOpen,\n helpText = \"Open all eyelids\",\n helpURL = _HelpURL,\n )\n self.eyelidsCloseWdg = RO.Wdg.Button(\n master = self,\n text = \"Close All\",\n callFunc = self.doEyelidsClose,\n helpText = \"Close all eyelids\",\n helpURL = _HelpURL,\n )\n self.addCategory(\n catName = \"Eyelids\",\n extraWdgs = (self.eyelidsState, self.eyelidsOpenWdg, self.eyelidsCloseWdg),\n )\n \n self.startNewColumn()\n self.lightsState = DevStateWdg(\n master = self,\n catInfo = self.model.catDict[\"Lights\"],\n onIsNormal = False,\n patternDict = {\n (0, 0, 0, 0, 0, 0, 1, 0): (\"Main Off\", RO.Constants.sevNormal),\n },\n helpText = \"State of the lights\",\n helpURL = _HelpURL,\n )\n self.lightsMainOffWdg = RO.Wdg.Button(\n master = self,\n text = \"Main Off\",\n callFunc = self.doLightsMainOff,\n helpText = \"Turn off all lights except int. incandescents\",\n helpURL = _HelpURL,\n )\n self.addCategory(\"Lights\", extraWdgs=(self.lightsState, self.lightsMainOffWdg))\n \n self.startNewColumn()\n self.louversState = DevStateWdg(\n master = self,\n catInfo = self.model.catDict[\"Louvers\"],\n onIsNormal = True,\n helpText = \"State of the louvers\",\n helpURL = _HelpURL,\n )\n self.louversOpenWdg = RO.Wdg.Button(\n master = self,\n text = \"Open All\",\n callFunc = self.doLouversOpen,\n helpText = \"Open all louvers\",\n helpURL = _HelpURL,\n )\n self.louversCloseWdg = RO.Wdg.Button(\n master = self,\n text = \"Close All\",\n callFunc = self.doLouversClose,\n helpText = \"Close all louvers\",\n helpURL = _HelpURL,\n )\n self.addCategory(\n catName = \"Louvers\",\n extraWdgs = (self.louversState, self.louversOpenWdg, self.louversCloseWdg),\n )\n \n self.startNewColumn()\n self.heatersState = DevStateWdg(\n master = self,\n catInfo = self.model.catDict[\"Heaters\"],\n onIsNormal = False,\n helpText = \"State of the roof heaters\",\n helpURL = _HelpURL,\n )\n self.heatersOffWdg = RO.Wdg.Button(\n master = self,\n text = \"All Off\",\n callFunc = self.doHeatersOff,\n helpText = \"Turn off all roof heaters\",\n helpURL = _HelpURL,\n )\n self.heatersOnWdg = RO.Wdg.Button(\n master = self,\n text = \"All On\",\n callFunc = self.doHeatersOn,\n helpText = \"Turn on all roof heaters\",\n helpURL = _HelpURL,\n )\n self.addCategory(\n catName = \"Heaters\",\n extraWdgs = (self.heatersState, self.heatersOffWdg, self.heatersOnWdg),\n )\n\n self.statusBar = RO.Wdg.StatusBar(\n master = self,\n dispatcher = self.tuiModel.dispatcher,\n prefs = self.tuiModel.prefs,\n playCmdSounds = True,\n summaryLen = 20,\n helpURL = _HelpURL,\n )\n self.statusBar.grid(\n column=0,\n row=self.statusRow + 1,\n columnspan = self.col - 1,\n sticky=\"sew\",\n )\n \n self.cancelBtn = RO.Wdg.Button(\n master = self,\n text = \"Cancel\",\n callFunc = self.cancelCmds,\n helpText = \"Cancel all executing commands\",\n helpURL = _HelpURL,\n )\n self.cancelBtn.setEnable(False)\n self.cancelBtn.grid(\n column = self.col,\n row = self.statusRow + 1,\n columnspan = _ColsPerDev,\n )\n \n def addCategory(self, catName, extraWdgs=None):\n \"\"\"Add a set of widgets for a category of devices\"\"\"\n catInfo = self.model.catDict[catName]\n\n hasDetails = bool(extraWdgs)\n self.addCategoryLabel(catName, hasDetails)\n \n if extraWdgs:\n for ctrl in extraWdgs:\n ctrl.grid(column=self.col, row=self.row, columnspan=_ColsPerDev, sticky=\"ew\")\n self.row += 1\n\n self.addDevWdgs(catInfo, doHide=bool(extraWdgs))\n \n def addCategoryLabel(self, catName, hasDetails):\n \"\"\"Add a label for a category of devices\"\"\"\n if hasDetails:\n labelWdg = RO.Wdg.Checkbutton(\n master = self,\n text = catName,\n callFunc = self.showHideDetails,\n helpText = \"show/hide detailed info\",\n )\n else:\n labelWdg = RO.Wdg.StrLabel(self, text=catName)\n labelWdg.grid(\n row = self.row,\n column = self.col,\n columnspan = _ColsPerDev,\n )\n self.row += 1\n \n def addDevWdgs(self, catInfo, doHide):\n \"\"\"Add a set of widgets to control one device.\n \"\"\"\n# print \"addDevWdgs(catInfo=%r, doHide=%r)\" % (catInfo.catName, doHide)\n stateWidth = max([len(name) for name in catInfo.stateNames])\n \n wdgList = []\n\n for devName, keyVar in catInfo.devDict.iteritems():\n colInd = self.col\n\n devLabel = devName.replace(\"_\", \" \")\n labelWdg = RO.Wdg.StrLabel(\n master = self,\n text = devLabel,\n anchor = \"e\",\n helpText = None,\n helpURL = _HelpURL,\n )\n wdgList.append(labelWdg)\n labelWdg.grid(row = self.row, column = colInd, sticky=\"e\")\n colInd += 1\n \n ctrlStateFrame = Tkinter.Frame(self)\n\n if not catInfo.readOnly:\n ctrlWdg = RO.Wdg.Checkbutton(\n master = ctrlStateFrame,\n onvalue = catInfo.stateNames[1],\n offvalue = catInfo.stateNames[0],\n autoIsCurrent = True,\n helpText = \"Toggle %s %s\" % (devLabel, catInfo.catNameSingular.lower()),\n helpURL = _HelpURL,\n )\n ctrlWdg[\"command\"] = RO.Alg.GenericCallback(self._doCmd, catInfo, devName, ctrlWdg)\n keyVar.addROWdg(ctrlWdg, setDefault=True)\n keyVar.addROWdg(ctrlWdg)\n ctrlWdg.pack(side=\"left\")\n\n stateWdg = RO.Wdg.Label(\n master = ctrlStateFrame,\n width = stateWidth,\n anchor = \"w\",\n helpText = \"State of %s %s\" % (devLabel, catInfo.catNameSingular.lower()),\n helpURL = _HelpURL,\n )\n stateWdg.pack(side=\"left\")\n\n wdgList.append(ctrlStateFrame)\n ctrlStateFrame.grid(row = self.row, column = colInd, sticky=\"w\")\n colInd += 1\n self.row += 1\n \n def updateWdg(value, isCurrent, keyVar=keyVar, stateWdg=stateWdg, stateNames=catInfo.stateNames):\n if value is None:\n str = \"?\"\n else:\n ind = 1 if value else 0\n str = stateNames[ind]\n stateWdg.set(str, isCurrent=isCurrent)\n keyVar.addIndexedCallback(updateWdg)\n \n if doHide:\n for wdg in wdgList:\n wdg.grid_remove()\n\n self.detailWdgDict[catInfo.catName] = wdgList\n \n def cancelCmds(self, wdg=None):\n \"\"\"Cancel all executing commands\"\"\"\n locPendingCmds = self.pendingCmds[:]\n for cmd in locPendingCmds:\n try:\n cmd.abort()\n except Exception:\n pass\n # paranoia\n self.pendingCmds = []\n self.cancelBtn.setEnable(False)\n \n def startCmd(self, wdg, cmdStr, cmdCallback=None):\n \"\"\"Start a command\n \n Enables the Cancel button, disables the appropriate widget\n and sets up a callback that will re-enable it when done\n \"\"\"\n wdg.setEnable(False)\n if cmdCallback is None:\n cmdCallback = self.cmdDone\n cmdVar = RO.KeyVariable.CmdVar(\n actor = self.model.actor,\n cmdStr = cmdStr,\n callFunc = RO.Alg.GenericCallback(self.cmdDone, wdg),\n callTypes = RO.KeyVariable.DoneTypes,\n )\n self.pendingCmds.append(cmdVar)\n self.cancelBtn.setEnable(True)\n self.statusBar.doCmd(cmdVar)\n \n def cmdDone(self, wdg, msgType, msgDict, cmdVar):\n \"\"\"A command finished. Re-enable the widget.\n If the command failed then restore the default value.\n Use after because a simple callback will not have the right effect\n if the command fails early during the command button callback.\n \"\"\"\n wdg.setEnable(True)\n if cmdVar.didFail():\n self.after(10, wdg.restoreDefault)\n self.pendingCmds.remove(cmdVar)\n if not self.pendingCmds:\n self.cancelBtn.setEnable(False)\n \n def doCoversCmd(self):\n \"\"\"Open or close the primary mirror covers\"\"\"\n boolVal = self.coversUserWdg.getBool()\n verbStr = {True: \"open\", False: \"close\"}[boolVal]\n cmdStr = \"covers %s\" % verbStr\n self.coversCurrWdg.setIsCurrent(False)\n self.startCmd(\n wdg = self.coversUserWdg,\n cmdStr = cmdStr,\n )\n \n def doEyelidsClose(self, wdg=None):\n \"\"\"Close all eyelids\"\"\"\n self.startCmd(\n cmdStr = \"eyelids all close\",\n wdg = self.eyelidsCloseWdg,\n )\n \n def doEyelidsOpen(self, wdg=None):\n \"\"\"Open all eyelids\"\"\"\n self.startCmd(\n cmdStr = \"eyelids all open\",\n wdg = self.eyelidsOpenWdg,\n )\n \n def doHeatersOff(self, wdg=None):\n \"\"\"Turn off all roof heaters\"\"\"\n self.startCmd(\n cmdStr = \"heaters all off\",\n wdg = self.heatersOffWdg,\n )\n\n def doHeatersOn(self, wdg=None):\n \"\"\"Turn on all roof heaters\"\"\"\n self.startCmd(\n cmdStr = \"heaters all on\",\n wdg = self.heatersOnWdg,\n )\n\n def doLightsMainOff(self, wdg=None):\n \"\"\"Turn off main lights\"\"\"\n self.startCmd(\n cmdStr = \"lights fhalides rhalides incand platform catwalk stairs int_fluor off\",\n wdg = self.lightsMainOffWdg,\n )\n \n def doLouversClose(self, wdg=None):\n \"\"\"Close all louvers\"\"\"\n self.startCmd(\n cmdStr = \"louvers all close\",\n wdg = self.louversCloseWdg,\n )\n \n def doLouversOpen(self, wdg=None):\n \"\"\"Open all louvers\"\"\"\n self.startCmd(\n cmdStr = \"louvers all open\",\n wdg = self.louversOpenWdg,\n )\n\n def isTertRotCmdRunning(self):\n \"\"\"Return True if a tertiary rotation command is running\n \"\"\"\n for cmdVar in self.pendingCmds:\n if not cmdVar.isDone() and cmdVar.cmdStr.startswith(\"tertrot\"):\n return True\n return False\n \n def doTertRotApply(self, wdg=None):\n \"\"\"Apply tertiary rotation command\"\"\"\n desTertRot = self.tertRotWdg.getString().lower()\n cmdStr = \"tertrot %s\" % desTertRot\n self.startCmd(\n cmdStr = cmdStr,\n wdg = self.tertRotWdg,\n cmdCallback = self.tertRotCmdCallback,\n )\n self.tertRotEnable()\n \n def doTertRotRestore(self, wdg=None):\n \"\"\"Restore tertRot to current value\"\"\"\n self.tertRotWdg.restoreDefault()\n self.tertRotEnable()\n \n def showHideDetails(self, wdg):\n \"\"\"Show or hide detailed controls for a category\"\"\"\n catName = wdg[\"text\"]\n doShow = wdg.getBool()\n detailWdgs = self.detailWdgDict[catName]\n if doShow:\n for wdg in detailWdgs:\n wdg.grid()\n else:\n for wdg in detailWdgs:\n wdg.grid_remove()\n \n def tertRotCmdCallback(self, msgType, msgDict, cmdVar):\n \"\"\"Tertiary rotation command callback function\"\"\"\n if cmdVar.isDone():\n self.tertRotWdg.setEnable(True)\n self.tertRotEnable()\n \n def tertRotEnable(self, wdg=None):\n \"\"\"Enable or disable tertiary rotation Apply and Restore buttons\n\n Note: the Tert Rot option menu is handled separately.\n \"\"\"\n isDefault = self.tertRotWdg.isDefault()\n cmdRunning = self.isTertRotCmdRunning()\n# print \"tertRotEnable; isDefault=%s, cmdRunning=%s\" % (isDefault, cmdRunning)\n enableBtns = not isDefault and not cmdRunning\n self.tertRotApplyWdg.setEnable(enableBtns)\n self.tertRotCurrWdg.setEnable(enableBtns)\n \n def updateCovers(self, value, isCurrent, keyVar=None):\n \"\"\"Handle covers keyword data\"\"\"\n# print \"updateCovers(value=%r, isCurrent=%r)\" % (value, isCurrent)\n if value is None:\n severity = RO.Constants.sevWarning\n strValue = \"?\"\n else:\n if value:\n severity = RO.Constants.sevNormal\n strValue = \"Open\"\n else:\n severity = RO.Constants.sevWarning\n strValue = \"Closed\"\n self.coversCurrWdg.set(strValue, severity=severity, isCurrent=isCurrent)\n self.coversUserWdg.set(value)\n self.coversUserWdg.setDefault(value, severity=severity, isCurrent=isCurrent)\n \n def updateTertRot(self, value, isCurrent, keyVar=None):\n \"\"\"Handle tertRot keyword data\"\"\"\n if value is None or value.lower() == \"home\":\n severity = RO.Constants.sevWarning\n else:\n severity = RO.Constants.sevNormal\n self.tertRotWdg.setDefault(value, severity=severity, doCheck=False)\n self.tertRotEnable()\n \n def _doCmd(self, catInfo, devName, ctrlWdg):\n \"\"\"Change the state of a device with category info\"\"\"\n# print \"_doCmd(catInfo=%r, devName=%r, ctrlWdg=%r)\" % (catInfo, devName, ctrlWdg)\n\n boolVal = ctrlWdg.getBool()\n verbStr = catInfo.getVerbStr(boolVal)\n cmdStr = \"%s %s %s\" % (catInfo.catName, devName, verbStr)\n cmdStr = cmdStr.lower()\n self.startCmd(cmdStr = cmdStr, wdg=ctrlWdg)\n \n def startNewColumn(self):\n \"\"\"Start a new column of controls\"\"\"\n self.statusRow = max(self.statusRow, self.row)\n self.row = 0\n self.col += _ColsPerDev\n # create narrow blank column\n RO.Wdg.StrLabel(self, text=\" \").grid(row=self.row, column=self.col)\n self.col += 1\n\n \nif __name__ == '__main__':\n import TestData\n root = TestData.tuiModel.tkRoot\n root.resizable(width=0, height=0)\n \n testFrame = StatusCommandWdg (root)\n testFrame.pack()\n\n Tkinter.Button(root, text=\"Demo\", command=TestData.runDemo).pack()\n \n TestData.init()\n \n root.mainloop()\n","sub_path":"TUI/Misc/TelMech/StatusCommandWdg.py","file_name":"StatusCommandWdg.py","file_ext":"py","file_size_in_byte":25118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"493925433","text":"#\n# Copyright 2021 Splunk Inc.\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\"\"\"\nReplaced value of tokens\n\"\"\"\nFIELD_MAPPING = {\n \"src\": {\n \"token\": [\n \"src\",\n \"srcaddr\",\n \"src_addr\",\n \"src-addr\",\n \"srcip\",\n \"src_ip\",\n \"src-ip\",\n \"srcaddress\",\n \"src_address\",\n \"src-address\",\n \"source\",\n \"sourceaddr\",\n \"source_addr\",\n \"source-addr\",\n \"sourceip\",\n \"source_ip\",\n \"source-ip\",\n \"sourceaddress\",\n \"source_address\",\n \"source-address\",\n \"srcfqdn\",\n \"src_fqdn\",\n \"src-fqdn\",\n \"sourcefqdn\",\n \"source_fqdn\",\n \"source-fqdn\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"src[]\",\n \"field\": \"src\",\n \"possible_replacement\": [\"ipv4\", \"ipv6\", \"host\", \"fqdn\"],\n },\n \"dest\": {\n \"token\": [\n \"dest\",\n \"destaddr\",\n \"dest_addr\",\n \"dest-addr\",\n \"destip\",\n \"dest_ip\",\n \"dest-ip\",\n \"destaddress\",\n \"dest_address\",\n \"dest-address\",\n \"destination\",\n \"destinationaddr\",\n \"destination_addr\",\n \"destination-addr\",\n \"destinationip\",\n \"destination_ip\",\n \"destination-ip\",\n \"destinationaddress\",\n \"destination_address\",\n \"destination-address\",\n \"destfqdn\",\n \"dest_fqdn\",\n \"dest-fqdn\",\n \"destinationfqdn\",\n \"destination_fqdn\",\n \"destination-fqdn\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"dest[]\",\n \"field\": \"dest\",\n \"possible_replacement\": [\"ipv4\", \"ipv6\", \"host\", \"fqdn\"],\n },\n \"user\": {\n \"token\": [\"user\", \"username\", \"usr\", \"user_name\", \"user-name\", \"users\"],\n \"replacementType\": \"random\",\n \"replacement\": \"user[]\",\n \"field\": \"user\",\n \"possible_replacement\": [\"name\", \"email\", \"domain_user\", \"distinquised_name\"],\n },\n \"src_port\": {\n \"token\": [\n \"src_port\",\n \"src-port\",\n \"source_port\",\n \"source-port\",\n \"sourceport\",\n \"srcport\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"src_port\",\n \"field\": \"src_port\",\n },\n \"dest_port\": {\n \"token\": [\n \"dest_port\",\n \"dest-port\",\n \"destination_port\",\n \"destination-port\",\n \"destinationport\",\n \"destport\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"dest_port\",\n \"field\": \"dest_port\",\n },\n \"dvc\": {\n \"token\": [\"dvc\"],\n \"replacementType\": \"random\",\n \"replacement\": \"dvc[]\",\n \"field\": \"dvc\",\n \"possible_replacement\": [\"ipv4\", \"ipv6\", \"host\", \"fqdn\"],\n },\n \"url\": {\n \"token\": [\"url\", \"uri\"],\n \"replacementType\": \"random\",\n \"replacement\": \"url[]\",\n \"field\": \"url\",\n \"possible_replacement\": [\"ip_host\", \"fqdn_host\", \"path\", \"query\", \"protocol\"],\n },\n \"guid\": {\"token\": [\"guid\"], \"replacementType\": \"random\", \"replacement\": \"guid\"},\n \"host\": {\n \"token\": [\n \"host\",\n \"hostaddr\",\n \"host_addr\",\n \"host-addr\",\n \"hostaddress\",\n \"host_address\",\n \"host-address\",\n \"httphost\",\n \"http_host\",\n \"http-host\",\n \"hostname\",\n \"host_name\",\n \"host-name\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"host[] # REVIEW : \",\n \"field\": \"host\",\n \"possible_replacement\": [\"host\", \"ipv4\", \"ipv6\", \"fqdn\"],\n },\n \"ipv4\": {\n \"token\": [\n \"ip\",\n \"ipv4\",\n \"ipaddr\",\n \"ip_addr\",\n \"ip-addr\",\n \"ipaddress\",\n \"ip_address\",\n \"ip-address\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"ipv4\",\n },\n \"ipv6\": {\"token\": [\"ipv6\"], \"replacementType\": \"random\", \"replacement\": \"ipv6\"},\n \"hex\": {\n \"token\": [\"hex\", \"puid\"],\n \"replacementType\": \"random\",\n \"replacement\": \"hex(20)\",\n },\n \"email\": {\n \"token\": [\n \"email\",\n \"e-mail\",\n \"e_mail\",\n \"mail\",\n \"mailid\",\n \"mail_id\",\n \"mail-id\",\n \"emailid\",\n \"email_id\",\n \"email-id\",\n \"emailaddr\",\n \"email_addr\",\n \"email-addr\",\n \"emailaddress\",\n \"email_address\",\n \"email-address\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"email\",\n },\n \"mac\": {\n \"token\": [\n \"mac\",\n \"macaddr\",\n \"mac_addr\",\n \"mac-addr\",\n \"macaddress\",\n \"mac_address\",\n \"mac-address\",\n \"macname\",\n \"mac_name\",\n \"mac-name\",\n ],\n \"replacementType\": \"random\",\n \"replacement\": \"mac\",\n },\n}\n\n\nFILE_MAPPING = {\n \"ipv4\": [\n \"anomalous.ip_address.sample\",\n \"ip_address.sample\",\n \"webhosts.sample\",\n \"ip.sample\",\n \"ipaddress.sample\",\n ],\n \"mac\": [\n \"anomalous.mac_address.sample\",\n \"mac_address.sample\",\n \"mac.sample\",\n \"remote_mac.sample\",\n ],\n \"host\": [\n \"anomalous.hostname.sample\",\n \"hostname.sample\",\n \"linux.host.sample\",\n \"computer_name.sample\",\n \"host_name.sample\",\n ],\n \"user\": [\"userName.sample\", \"mac_user.sample\", \"user_name.sample\"],\n \"dvc\": [\"dvc.sample\", \"dvc_ids.sample\"],\n \"url\": [\"uri.sample\", \"url.sample\"],\n \"email\": [\"email_address.sample\"],\n}\n","sub_path":"pytest_splunk_addon/standard_lib/utilities/mapping.py","file_name":"mapping.py","file_ext":"py","file_size_in_byte":6496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"49424184","text":"#\n# @lc app=leetcode id=48 lang=python3\n#\n# [48] Rotate Image\n#\n\n# @lc code=start\nfrom typing import List\nclass Solution:\n def rotate(self, matrix: List[List[int]]) -> None:\n \"\"\"\n Do not return anything, modify matrix in-place instead.\n \"\"\"\n #!!! modify self\n # (i, j) -> (j, n-1-i) -> (n-1-i, n-1-j) -> (n-1-j, i) -> (i, j)\n sz = len(matrix[0])\n for i in range(sz // 2):\n for j in range((sz + 1) // 2):\n tmp = matrix[i][j]\n matrix[i][j] = matrix[sz-1-j][i]\n matrix[sz-1-j][i] = matrix[sz-1-i][sz-1-j]\n matrix[sz-1-i][sz-1-j] = matrix[j][sz-1-i]\n matrix[j][sz-1-i] = tmp\n \n# @lc code=end\n\n","sub_path":"Difficulty/Medium/48.rotate-image.py","file_name":"48.rotate-image.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"547802373","text":"import darknet\nimport cv2\nfrom sys import argv\nfrom threading import Thread\nimport time\nimport test_upload\n\n\ndef saveVideo(array_image, label_class):\n filename = '/data/www/ia/_www/movies/' + argv[1] + '_' + str(time.time()) + '.avi'\n if len(array_image) > 0:\n out = cv2.VideoWriter(filename, cv2.VideoWriter_fourcc(*'DIVX'), 8,\n (darknet.network_width(network), darknet.network_height(network)))\n for i in range(len(array_image)):\n out.write(array_image[i])\n test_upload.send(filename, argv[1], label_class)\n out.release()\n print(\"video uploaded\")\n\n\ndef getLabel(detection):\n for label, confidence, bbox in detection:\n return label\n return False\n\n\n# Sort Dictionary in descending order\ndef sortDict(dict_class):\n return {k: v for k, v in sorted(dict_class.items(), key=lambda item: item[1], reverse=True)}\n\n\n# ------------ load Yolov4 network ------------\nnetwork, class_names, class_colors = darknet.load_network(\n config_file='/home/gael/darknet/cfg/yolo-obj.cfg',\n data_file='/home/gael/darknet/build/darknet/x64/data/obj.data',\n weights='/home/gael/darknet/backup/yolo-obj_best.weights')\n\n# ------------ Connect to stream ------------\nurl = \"rtmp://91.121.83.50:1935/live/\" + argv[1]\nprint(\"Connect to: \" + url)\ncap = cv2.VideoCapture(url)\n\ncount = 0\nimg_array = []\ncount_detections = 0\nfirst_detection = True\ntime_last_video = 0\ndict_classes = {}\n\nwhile True:\n ret, frame = cap.read()\n\n if not ret:\n break\n else:\n count = (count + 1) % 3\n if count == 0:\n width = darknet.network_width(network)\n height = darknet.network_height(network)\n darknet_image = darknet.make_image(width, height, 3)\n\n image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n image_resized = cv2.resize(image_rgb, (width, height),\n interpolation=cv2.INTER_LINEAR)\n\n darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())\n detections = darknet.detect_image(network, class_names, darknet_image, thresh=0.5)\n if len(detections) > 0:\n count_detections += 1\n # add the label in the dict if it's not already there and increase by 1 the value dict = {label: counter}\n label = getLabel(detections)\n if label not in dict_classes.keys():\n dict_classes[label] = 1\n else:\n dict_classes[label] += 1\n darknet.free_image(darknet_image)\n image = darknet.draw_boxes(detections, image_resized, class_colors)\n new_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n img_array.append(new_image)\n # cv2.imshow('inference', new_image)\n if count_detections >= 15 and (time.time() - time_last_video) > 120:\n # sort the dictionary and return the key of the first element\n dict_classes = sortDict(dict_classes)\n class_name = list(dict_classes.keys())[0]\n print(class_name)\n img_array_copy = img_array\n saveVideo(img_array, class_name)\n img_array.clear()\n count_detections = 0\n time_last_video = time.time()\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n else:\n img_array.append(frame)\n\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"37735445","text":"from tool.runners.python import SubmissionPy\nfrom itertools import product\nfrom operator import add\n\n\nclass LucasSubmission(SubmissionPy):\n\n def run(self, s):\n # :param s: input in string format\n data = [list(line) for line in s.split('\\n')]\n \n\n deltas = list(product((-1, 0, 1), repeat=3))\n cells = {}\n for i in range(len(data)):\n for j in range(len(data[0])):\n if data[i][j] == '#':\n coord = (i, j) + (0,) * (3 - 2)\n cells[coord] = 1\n \n for i in range(6):\n temp = {}\n for coord in cells.keys():\n n = sum(cells.get(tuple(map(add, coord, delta)), 0) for delta in deltas)\n if n == 3 or n == 4:\n temp[coord] = 1\n \n for neighbour in deltas:\n neighbour = tuple(map(add, coord, neighbour))\n if neighbour not in cells:\n n = sum(cells.get(tuple(map(add, neighbour, delta)), 0) for delta in deltas)\n if n == 3:\n temp[neighbour] = 1\n cells = temp\n \n return len(cells)\n ","sub_path":"day-17/part-1/lucas.py","file_name":"lucas.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"176016823","text":"# coding=utf-8\nimport sys\nimport re\nimport os\n\npath = './ATs'\ndestination_directory = './Components/'\nfile_name = 'prompts'\nfw = open(destination_directory + file_name, 'w')\nif os.path.exists(path):\n print('Loading...')\n for file_or_folder in os.listdir(path):\n full_path = os.path.join(path, file_or_folder)\n # judge wheather one object in the directory is a file.\n if os.path.isfile(full_path):\n fw.write('*/')\n f_name, f_extension = file_or_folder.split('.')\n fw.write(f_name+' ')\n ff = open(full_path, 'r')\n contents = list(ff)\n fw.write(contents[0])\n ff.close()\n fw.write('\\n')\nelse:\n print('Wrong path!')\n## read text file by console\n#fr = open(sys.argv[1],'r')\n## set the extension name as tt\n#file_name = str(sys.argv[1]) + '.tt'\n#fw = open(file_name, 'w')\n## load file\n#textList = list(fr)\n\n\nprint('---------------------------------------')\nprint('Executing ...\\n')\nprint('Congratuation, new file ' + file_name + ' has been generated.')\nprint('---------------------------------------')\n#fr.close()\n#fw.close()\n","sub_path":"promptsGenerator.py","file_name":"promptsGenerator.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"469004141","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n# 2018/4/11 17:29\n__Author__ = 'zb'\n\nfrom coreweb import get, post\nfrom models import User, Comment, Blog, next_id\n\n@get('/')\nasync def index(request):\n users = await User.findAll()\n return {\n '__template__':'test.html',\n 'users':users\n }","sub_path":"www/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"653307309","text":"#\n# Copyright (c) 2008-2010 Stefan Krah. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions\n# are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS \"AS IS\" AND\n# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS\n# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF\n# SUCH DAMAGE.\n#\n\n\n#\n# Test formatting using random format strings. This must be run\n# in a UFT-8 terminal.\n#\n# Usage: python3.2 genrandformat.py | ../runtest -\n#\n\n\nimport random\nfrom formathelper import rand_format, rand_fillchar, integers, printit\nfrom randdec import un_incr_digits\nprint(\"rounding: half_even\")\n\n\ntestno = 0\nfor x in range(1000):\n for sign in ('', '-'):\n intpart = fracpart = ''\n while (not intpart) and (not fracpart):\n intpart = random.choice(integers)\n fracpart = random.choice(integers)\n s = ''.join((sign, intpart, '.', fracpart))\n fmt = rand_format(rand_fillchar())\n testno += 1\n printit(testno, s, fmt, 'utf-8')\n for s in un_incr_digits(15, 384, 30):\n fmt = rand_format(rand_fillchar())\n testno += 1\n printit(testno, s, fmt, 'utf-8')\n","sub_path":"cdecimal-2.3/python/genrandformat.py","file_name":"genrandformat.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"321264264","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport tensorflow as tf\n\nimport os\nimport tensorflow_datasets as tfds\nimport numpy as np\n\ntpu_address = input(\"What is the name of the TPU address?\")\n\nresolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + tpu_address)\ntf.config.experimental_connect_to_cluster(resolver)\ntf.tpu.experimental.initialize_tpu_system(resolver)\n\ndef create_model():\n return tf.keras.Sequential(\n [tf.keras.layers.Reshape((256, 256, 1)),\n tf.keras.layers.Conv2D(32, 2, activation='relu', input_shape=(-1, 256, 256, 1)),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=4),\n tf.keras.layers.Conv2D(92, 2, activation='relu'),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2),\n tf.keras.layers.Conv2D(182, 2, activation='relu'),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(1024, activation='relu'),\n tf.keras.layers.Dropout(rate=.4, noise_shape=None, seed=None),\n tf.keras.layers.Dense(2)])\n\ndef get_dataset(batch_size=200):\n print(\"loading train data....\")\n # features = np.load(\"X_train_1.npy\")\n #labels = np.zeros(shape=(1000,1))\n #labels = tf.cast(labels, tf.float32)\n #labels = np.load(\"test_labels.npy\")\n #train_dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n \n features_name = \"X_train_\"\n features = np.load(\"/home/rydagostino/data/train2/X_train_1.npy\")\n filenames = []\n for i in range(2,7,1):\n tmp = np.load(\"/home/rydagostino/data/train2/\"+features_name+str(i)+\".npy\")\n features = np.concatenate((features,tmp)) \t\n #filenames.append(os.getcwd()+\"/\"+features_name+str(i)+\".npy\")\n \n labels_name = \"y_train_\"\n labels = np.load(\"/home/rydagostino/data/train2/y_train_1.npy\")\n filenames = []\n for i in range(2,7,1):\n tmp = np.load(\"/home/rydagostino/data/train2/\"+labels_name+str(i)+\".npy\")\n labels = np.concatenate((labels,tmp)) \n # labels = np.zeros(shape=(7000,1))\n #labels[4000:7000] = 1\n #features = np.zeros(shape=(7000,65536))\n\n #labels = labels.astype('float32')\n train_dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n \n train_dataset = train_dataset.shuffle(1000).batch(batch_size)\n\n features_name = \"X_test_\"\n features = np.load(\"/home/rydagostino/data/test2/X_test_1.npy\")\n #filenames = []\n #for i in range(2,7,1):\n # tmp = np.load(\"/home/rydagostino/data/test2/\"+features_name+str(i)+\".npy\")\n #features = np.concatenate((features,tmp)) \n #filenames.append(os.getcwd()+\"/\"+features_name+str(i)+\".npy\")\n\n labels_name = \"y_test_\"\n labels = np.load(\"/home/rydagostino/data/test2/y_test_1.npy\")\n #filenames = []\n #for i in range(2,7,1):\n # tmp = np.load(\"/home/rydagostino/data/test2/\"+labels_name+str(i)+\".npy\")\n # labels = np.concatenate((labels,tmp))\n\n#test_dataset = tf\n test_dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n \n test_dataset = test_dataset.shuffle(1000).batch(batch_size)\n\n return train_dataset, test_dataset\n\nstrategy = tf.distribute.experimental.TPUStrategy(resolver)\nwith strategy.scope():\n model = create_model()\n model.compile(optimizer='adam',\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n metrics=['sparse_categorical_accuracy'])\n\ntrain_dataset, test_dataset = get_dataset()\n\ncheckpoint_path = \"gs://tpu_models_1/model_2/\"\n#checkpoint_dir = os.path.dirname(checkpoint_path)\n\n# Create a callback that saves the model's weights\ncp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n save_weights_only=True,\n verbose=1)\n\n\nmodel.fit(train_dataset,\n epochs=40,\n validation_data=test_dataset,\n callbacks=[cp_callback])\n\nmodel.save('cnn2_model.h5') \n","sub_path":"src/second_model.py","file_name":"second_model.py","file_ext":"py","file_size_in_byte":3937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"602076608","text":"import numpy as np\n\ndef fillRectangle( pt1, pt2, ppe, patch, dw, matid, density ):\n nn = pCeil( (pt2-pt1) / (patch.dX/ppe) )\n ps = (pt2-pt1)/nn\n vol = patch.thick * ps[0] * ps[1]\n for jj in range(int(nn[1])):\n for ii in range(int(nn[0])):\n ns = np.array([ii+0.5,jj+0.5])\n pt = pt1 + ps*ns\n if patch.inPatch( pt ):\n dw.addParticle( matid, pt, vol*density, vol )\n\n\ndef fillAnnulus( pt1, r, ppe, patch, dw, matid, density ):\n nn = pCeil( 2*r[1] / (patch.dX/ppe) )\n ps = 2.0*r[1]/nn\n vol = patch.thick * ps[0] * ps[1]\n for jj in range(int(nn[1])):\n for ii in range(int(nn[0])):\n ns = np.array([ii+0.5,jj+0.5])\n pt = pt1 - r[1] + ps*ns\n if patch.inPatch( pt ):\n if ( r[0] <= np.linalg.norm( pt1 - pt ) <= r[1] ):\n dw.addParticle( matid, pt, vol*density, vol ) \n\n\ndef pCeil( x ):\n tol = 1.e-14\n return np.ceil(x-tol)","sub_path":"mpm_old/src/geomutils.py","file_name":"geomutils.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"507061665","text":"import matplotlib.pyplot as plt\n\nimport numpy as np\n\nimport serial\n\nimport time\n\n\nFs = 100.0; # sampling rate\n\nTs = 10.0/Fs; # sampling interval\n\nt = np.arange(0,10,Ts) # time vector; create Fs samples between 0 and 1.0 sec.\n\nx = np.arange(0,10,Ts) # signal vector; create Fs samples\n\ny = np.arange(0,10,Ts) # signal vector; create Fs samples\n\nz = np.arange(0,10,Ts) # signal vector; create Fs samples\n\ntilt = np.arange(0,10,Ts) # signal vector; create Fs samples\n\n\nserdev = '/dev/ttyACM0'\n\ns = serial.Serial(serdev, 115200)\n\nfor i in range(0, int(Fs)):\n\n line=s.readline() \n\n x[i] = float(line)\n\n line=s.readline() \n\n y[i] = float(line)\n\n line=s.readline() \n\n z[i] = float(line)\n\n line=s.readline() \n\n tilt[i] = float(line)\n \n\n\nfig, ax = plt.subplots(2, 1)\n\nax[0].plot(t,x, color=\"red\", linewidth=2.5, linestyle=\"-\", label=\"x\")\n\nax[0].plot(t,y, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"y\")\n\nax[0].plot(t,z, color=\"green\", linewidth=2.5, linestyle=\"-\", label=\"z\")\n\nax[0].legend(loc='lower left', frameon=True)\n\nax[0].set_xlabel('Time')\n\nax[0].set_ylabel('Acc Vector')\n\nax[1].stem(t,tilt)\n\nax[1].set_xlabel('Time')\n\nax[1].set_ylabel('Tilt')\n\nplt.show()\n\ns.close()","sub_path":"hw3/FFT.py","file_name":"FFT.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"68063607","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('management', '0033_merge'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='airbornnumber',\n name='equipment_type',\n field=models.CharField(null=True, verbose_name='设备型号', max_length=16, choices=[('Sensor', '传感器'), ('Box', '采集机箱'), ('Card', '采集板卡')]),\n ),\n migrations.AlterField(\n model_name='parameter',\n name='status',\n field=models.CharField(verbose_name='参数状态', max_length=20, default='request_validating', choices=[('request_validating', '需求分析'), ('request_published', '需求发布'), ('sensor_published', '传感器选型发布'), ('acquisitioncard_published', '采集板卡发布'), ('acquisitionbox_published', '采集机箱发布')]),\n ),\n ]\n","sub_path":"management/migrations/0034_auto_20180607_1339.py","file_name":"0034_auto_20180607_1339.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"371395032","text":"import os\r\nimport sys\r\nimport time\r\nimport pandas as pd\r\nimport numpy as np\r\nimport xml.etree.ElementTree as ET\r\nimport re\r\nimport subprocess\r\n\r\ndef select_rows_old(df,search_strings):\r\n\t# This function is to return the rows which contain all of the strings in search_strings\r\n\tunq,IDs = np.unique(df,return_inverse=True)\r\n\tunqIDs = np.searchsorted(unq,search_strings)\r\n\tdf_out1 = df[((IDs.reshape(df.shape) == unqIDs[:,None,None]).any(-1)).all(0)]\r\n\tdf_out2 = df[~((IDs.reshape(df.shape) == unqIDs[:,None,None]).any(-1)).all(0)]\r\n\treturn df_out1, df_out2\r\n\r\ndef is_number(s):\r\n\ttry:\r\n\t\tf = float(s)\r\n\t\tif f!=f or f == float('inf') or f == float('-inf'):\r\n\t\t\treturn False\r\n\t\treturn True\r\n\texcept ValueError:\r\n\t\treturn False\r\n\r\ndef infer_dataType(s):\r\n\tif s.isdigit():\r\n\t\tif int(s) == 0:\r\n\t\t\tdata_type = 'zero integer'\r\n\t\telse:\r\n\t\t\tdata_type = 'integer'\r\n\telif is_number(s):\r\n\t\tif float(s) >= 0:\r\n\t\t\tdata_type = 'number'\r\n\t\telse:\r\n\t\t\tdata_type = 'negative number'\r\n\telse:\r\n\t\tdata_type = 'text'\r\n\treturn data_type\r\n\r\ndef string_finder(row, words, waived_list):\r\n\t# row is a type of Series.\r\n\trow1 = row.tolist()\r\n\trow2 = [v for u, v in enumerate(row1) if u not in waived_list]\r\n\tif any(word == field for field in row2 for word in words):\r\n\t\treturn True\r\n\treturn False\r\n\r\ndef select_rows(df, match, waived_col_id_list):\r\n\tids = df.apply(string_finder, words=match, waived_list=waived_col_id_list, axis=1)\r\n\tdf_out1 = df[ids]\r\n\tdf_out2 = df[~ids]\r\n\treturn df_out1, df_out2\r\n\r\ndef check_missing(listToCheck):\r\n\t# If status is True, that means listToCheck has '' or missing values exist.\r\n\tstatus = False\r\n\tfor item in listToCheck:\r\n\t\tif item == '':\r\n\t\t\tstatus = True\r\n\t\t\tbreak\r\n\treturn status\r\n\r\ndef locate_missing(valueList, colNameList, match, waived_list):\r\n\toutList = [colNameList[index] for index, value in enumerate(valueList) if (value in match) and (index not in waived_list)]\r\n\treturn \"Error\", \"Attribute\", \"Essential attribute(s) has (have) missing values, including \"+'; '.join(outList)+'.'\r\n\r\ndef identify_uniProtKB_entryID(proteinName):\r\n# This function is to extract uniProtKB_entryID from the protein name.\r\n\tif '|' in proteinName:\r\n\t\ttmp = proteinName.split('|')\r\n\t\tuniProtKB_entryID_tmp = tmp[1]\r\n\t\t# Judge whether uniProtKB_entryID is a legal uniProtKB entry ID based on its pattern using regular expression. Please refer to https://www.uniprot.org/help/accession_numbers\r\n\t\tpattern = re.compile('[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}')\r\n\t\tmatch = pattern.match(uniProtKB_entryID_tmp)\r\n\t\tif match:\r\n\t\t\tuniProtKB_entryID = uniProtKB_entryID_tmp\r\n\t\telse:\r\n\t\t\tuniProtKB_entryID = proteinName\r\n\telse:\r\n\t\tuniProtKB_entryID = proteinName\r\n\treturn uniProtKB_entryID\r\n\r\ndef qcAnalysisGlobal(experiment_type, skyTsvDirList, fileNameList, required_col_dic, waived_col_dic, fileNameSkylineTmpTypeDic, col_dataType_dic):\r\n\t# Check all the *.tsv files in skyTsvDirList to make sure all of them exist,\r\n\t# Otherwise, it means some *.sky.zip files are not successfully transformed into *.tsv file via SkylineCMd and an error will be yielded.\r\n\t#failedList = [fileNameList[i] for i, item in enumerate(skyTsvDirList) if not os.path.isfile(item)]\r\n\t#if len(failedList) >0:\r\n\t#\tprint >> sys.stderr, \"The file(s) which is(are): %s can't be opened by Skyline. Please check the version compatibility between Skyline document and Skyline program.\"%(\", \".join(failedList))\r\n\t#\tsys.exit(1)\r\n\t# Only keep the required columns based on required_col_dic\r\n\tif experiment_type in ['exp1', 'exp2']:\r\n\t\tfileNameSelceted = fileNameSkylineTmpTypeDic.keys()[0]\r\n\t\tskylineTemp_type = fileNameSkylineTmpTypeDic[fileNameSelceted]\r\n\t\trequired_col_list = required_col_dic[experiment_type][skylineTemp_type]\r\n\t\twaived_col_list = waived_col_dic[experiment_type][skylineTemp_type]\r\n\t\tcol_dataType_forCheck_dic = col_dataType_dic[experiment_type][skylineTemp_type]\r\n\telse:\r\n\t\trequired_col_list = required_col_dic[experiment_type]\r\n\t\twaived_col_list = waived_col_dic[experiment_type]\r\n\t\tcol_dataType_forCheck_dic = col_dataType_dic[experiment_type]\r\n\t# touch the first skyFile\r\n\tdfTemplate = pd.read_csv(skyTsvDirList[0], sep='\\t', header=0, converters={i: str for i in range(0, 100)})\r\n\tdfTemplate = dfTemplate[required_col_list]\r\n\t# Because the exported .tsv files are based on the skyline report template, they must have the same column information.\r\n\t# If one sky document file lacks one column data, the exported .tsv file will assign missing values to this this column.\r\n\terrorDf = pd.DataFrame(columns=['SkyDocumentName', 'IssueType', 'IssueSubtype', 'IssueReason']+list(dfTemplate.columns.values))\r\n\tnormalDf = pd.DataFrame(columns=['SkyDocumentName']+list(dfTemplate.columns.values))\r\n\tpeptide_excluded_in_Rscript_Df = pd.DataFrame(columns=['peptide', 'precursorCharge', 'isotopeLabelType', 'transition', 'uniProtKBID', 'proteinName', 'SkyDocumentName'])\r\n\t# Step 1: detect missing values \"\" without regard to the waived_col of the specific the experiment type\r\n\tsearch_strings = ['']\r\n\t#waived_col_list = waived_col_dic[experiment_type]\r\n\tfor i, skyFileDir in enumerate(skyTsvDirList):\r\n\t\t# Read the *.tsv file into dataframe and keep all the values in the format of string. \r\n\t\tdf = pd.read_csv(skyFileDir, sep='\\t', header=0, converters={j: str for j in range(0, 100)})\r\n\t\tdf = df[required_col_list]\r\n\t\t# Add 'SkyDocumentName' into df as the first column\r\n\t\tcol_name1 = df.columns.tolist()\r\n\t\tcol_name1.insert(0, 'SkyDocumentName')\r\n\t\tdf = df.reindex(columns=col_name1)\r\n\t\tdf['SkyDocumentName'] = fileNameList[i]\r\n\t\twaived_col_id_list = [s for s, item in enumerate(df.columns.tolist()) if item in waived_col_list]\r\n\t\t#print waived_col_id_list\r\n\t\tdfTmp1, dfTmp2 = select_rows(df, search_strings, waived_col_id_list)\r\n\t\t#print dfTmp1.shape\r\n\t\tremovedPeptideList = []\r\n\t\tif dfTmp1.shape[0] > 0:\r\n\t\t\tcol_name = dfTmp1.columns.tolist()\r\n\t\t\tcol_name.insert(1,'IssueType')\r\n\t\t\tcol_name.insert(2,'IssueSubtype')\r\n\t\t\tcol_name.insert(3,'IssueReason')\r\n\t\t\tdfTmp1 = dfTmp1.reindex(columns=col_name)\r\n\t\t\twaived_col_id_list_update = [s for s, item in enumerate(dfTmp1.columns.tolist()) if item in waived_col_list]\r\n\t\t\t#print dfTmp1\r\n\t\t\t#print waived_col_id_list_update\r\n\t\t\tresultTmp= dfTmp1.apply(lambda row: locate_missing([row[colName] for colName in dfTmp1.columns.values], list(dfTmp1.columns.values), search_strings, waived_col_id_list_update), axis=1)\r\n\t\t\t# resultTmp is a Series\r\n\t\t\tindexTmp1 = 0\r\n\t\t\t# suppress SettingWithCopyWarning\r\n\t\t\tpd.options.mode.chained_assignment = None\r\n\t\t\tfor indexTmp, itemTmp in resultTmp.iteritems():\r\n\t\t\t\tdfTmp1['IssueType'][dfTmp1.index[indexTmp1]] = itemTmp[0]\r\n\t\t\t\tdfTmp1['IssueSubtype'][dfTmp1.index[indexTmp1]] = itemTmp[1]\r\n\t\t\t\tdfTmp1['IssueReason'][dfTmp1.index[indexTmp1]] = itemTmp[2]\r\n\t\t\t\tindexTmp1 = indexTmp1 + 1\r\n\t\t\terrorDf = pd.concat([errorDf, dfTmp1], ignore_index=True)\r\n\t\t\t# Deduplicate the 'PeptideModifiedSequence' with the PrecursorCharge\r\n\t\t\tpeptideList = list(set(dfTmp1['PeptideModifiedSequence']+'$$$$'+dfTmp1['PrecursorCharge']))\r\n\t\t\t#peptideList = list(set(dfTmp1['PeptideModifiedSequence']))\r\n\t\t\tremovedPeptideList = removedPeptideList + peptideList\r\n\t\t# The peptides in dfTmp1[''] will be removed from dfTmp2, because the peptide information is \r\n\t\t# incomplete and these peptides should not be exported into *.tsv file for downstream R code.\r\n\t\t# Pay attention: If part of data for one peptide sequence have missing value and the rest part don't have missing value, all the rows of this peptide with specific precursor charge will be removed.\r\n\t\t#\r\n\t\t#for removedPeptide in removedPeptideList:\r\n\t\t#\tremovedPeptide1 = removedPeptide.split('$$$$')[0]\r\n\t\t#\tremovedPeptide2 = removedPeptide.split('$$$$')[1]\r\n\t\t#\tdfTmp2 = dfTmp2[~((dfTmp2['PeptideModifiedSequence'] == removedPeptide1) & (dfTmp2['PrecursorCharge'] == removedPeptide2))]\r\n\t\t#\r\n\t\t# Look into dfTmp2 to detect potential missing values and incorrect data type for some columns.\r\n\t\tfileNameSelceted = fileNameList[i]\r\n\t\tskylineTemp_type = fileNameSkylineTmpTypeDic[fileNameSelceted]\r\n\t\t# Traverse PeptideModifiedSequence in dfTmp2\r\n\t\tPeptideModifiedSequenceList = set(dfTmp2['PeptideModifiedSequence'].tolist())\r\n\t\tfor peptideSeq in PeptideModifiedSequenceList:\r\n\t\t\tdfTmp2_1 = dfTmp2[dfTmp2['PeptideModifiedSequence'] == peptideSeq]\r\n\t\t\t# Traverse PrecursorCharge in dfTmp2_1\r\n\t\t\tPrecursorChargeList = set(dfTmp2_1['PrecursorCharge'].tolist())\r\n\t\t\tfor precursorCharge in PrecursorChargeList:\r\n\t\t\t\tdfTmp2_2 = dfTmp2_1[dfTmp2_1['PrecursorCharge'] == precursorCharge]\r\n\t\t\t\t#print dfTmp2_2\r\n\t\t\t\t# Step 1: If experiment_type is 'exp1', check whether there are missing values in ISSpike or PeptideConcentrationIS, Concentration or PeptideConcentration and MultiplicationFactor\r\n\t\t\t\tif experiment_type == 'exp1' and skylineTemp_type == 'old':\r\n\t\t\t\t\tiSSpikeStatus = check_missing(dfTmp2_2['ISSpike'].tolist())\r\n\t\t\t\t\tpeptideConcentrationISStatus = check_missing(dfTmp2_2['PeptideConcentrationIS'].tolist())\r\n\t\t\t\t\tconcentrationStatus = check_missing(dfTmp2_2['Concentration'].tolist())\r\n\t\t\t\t\tpeptideConcentrationStatus = check_missing(dfTmp2_2['PeptideConcentration'].tolist())\r\n\t\t\t\t\tmultiplicationFactorStatus = check_missing(dfTmp2_2['MultiplicationFactor'].tolist())\r\n\t\t\t\t\tif (iSSpikeStatus and peptideConcentrationISStatus) or (concentrationStatus and (peptideConcentrationStatus or multiplicationFactorStatus)):\r\n\t\t\t\t\t\terrorDfTmp = pd.DataFrame(columns=['SkyDocumentName', 'IssueType', 'IssueSubtype', 'IssueReason']+list(dfTemplate.columns.values))\r\n\t\t\t\t\t\tskyDocumentName = dfTmp2_2['SkyDocumentName'].tolist()[0]\r\n\t\t\t\t\t\tissueType = \"Error\"\r\n\t\t\t\t\t\tissueSubtype = \"Attribute\"\r\n\t\t\t\t\t\tproteinName = dfTmp2_2['ProteinName'].tolist()[0]\r\n\t\t\t\t\t\tcolumnsWithIssue = []\r\n\t\t\t\t\t\tif iSSpikeStatus and peptideConcentrationISStatus:\r\n\t\t\t\t\t\t\tcolumnsWithIssue.append('ISSpike or PeptideConcentrationIS')\r\n\t\t\t\t\t\tif concentrationStatus and (peptideConcentrationStatus or multiplicationFactorStatus):\r\n\t\t\t\t\t\t\tcolumnsWithIssue.append('Concentration or PeptideConcentration and MultiplicationFactor')\r\n\t\t\t\t\t\tissueReason = \"Essential attribute(s) has (have) missing values, including \"+'; '.join(columnsWithIssue)+'.'\r\n\t\t\t\t\t\terrorDfTmp.loc[len(errorDfTmp)] = [skyDocumentName, issueType, issueSubtype, issueReason, proteinName, peptideSeq, '', precursorCharge] + ['']*(errorDfTmp.shape[1]-8)\r\n\t\t\t\t\t\terrorDf = pd.concat([errorDf, errorDfTmp], ignore_index=True)\r\n\t\t\t\t\t\t# Deduplicate the 'PeptideModifiedSequence' with the PrecursorCharge\r\n\t\t\t\t\t\tpeptideList = list(set(dfTmp2_2['PeptideModifiedSequence']+'$$$$'+dfTmp2_2['PrecursorCharge']))\r\n\t\t\t\t\t\t#peptideList = list(set(dfTmp2_2['PeptideModifiedSequence']))\r\n\t\t\t\t\t\tfor item in peptideList:\r\n\t\t\t\t\t\t\tif item not in removedPeptideList:\r\n\t\t\t\t\t\t\t\tremovedPeptideList.append(item)\r\n\t\t\t\t# Step 2: Check the data type of some required columns. This need to be done later.\r\n\t\t\t\tcol_dataType_forCheck_list = col_dataType_forCheck_dic.keys()\r\n\t\t\t\tcol_with_wrong_dataType_list = []\r\n\t\t\t\tfor col_name in col_dataType_forCheck_list:\r\n\t\t\t\t\tcol1 = set([infer_dataType(item) for item in dfTmp2_2[col_name].tolist() if item != ''])\r\n\t\t\t\t\tcol2 = col_dataType_forCheck_dic[col_name]\r\n\t\t\t\t\tif all([item in col2 for item in col1]):\r\n\t\t\t\t\t\tpass\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\t# This column has unqualified data type.\r\n\t\t\t\t\t\tunqualified_dataType = [item for item in col1 if item not in col2]\r\n\t\t\t\t\t\tunqualified_dataType_infor = 'Attribute \"' + col_name + '\" is annotated using ' + ', '.join(unqualified_dataType)\r\n\t\t\t\t\t\tcol_with_wrong_dataType_list.append(unqualified_dataType_infor)\r\n\t\t\t\tif len(col_with_wrong_dataType_list) > 0:\r\n\t\t\t\t\terrorDfTmp = pd.DataFrame(columns=['SkyDocumentName', 'IssueType', 'IssueSubtype', 'IssueReason']+list(dfTemplate.columns.values))\r\n\t\t\t\t\tskyDocumentName = dfTmp2_2['SkyDocumentName'].tolist()[0]\r\n\t\t\t\t\tissueType = \"Error\"\r\n\t\t\t\t\tissueSubtype = \"Attribute\"\r\n\t\t\t\t\tproteinName = dfTmp2_2['ProteinName'].tolist()[0]\r\n\t\t\t\t\tissueReason = \"Essential attribute(s) has (have) unqualified data types: \" + '; '.join(col_with_wrong_dataType_list)+'.'\r\n\t\t\t\t\terrorDfTmp.loc[len(errorDfTmp)] = [skyDocumentName, issueType, issueSubtype, issueReason, proteinName, peptideSeq, '', precursorCharge] + ['']*(errorDfTmp.shape[1]-8)\r\n\t\t\t\t\terrorDf = pd.concat([errorDf, errorDfTmp], ignore_index=True)\r\n\t\t\t\t\t# Deduplicate the 'PeptideModifiedSequence' with the PrecursorCharge\r\n\t\t\t\t\tpeptideList = list(set(dfTmp2_2['PeptideModifiedSequence']+'$$$$'+dfTmp2_2['PrecursorCharge']))\r\n\t\t\t\t\tfor item in peptideList:\r\n\t\t\t\t\t\tif item not in removedPeptideList:\r\n\t\t\t\t\t\t\tremovedPeptideList.append(item)\r\n\t\tfor removedPeptide in removedPeptideList:\r\n\t\t\tremovedPeptide1 = removedPeptide.split('$$$$')[0]\r\n\t\t\tremovedPeptide2 = removedPeptide.split('$$$$')[1]\r\n\t\t\tdfTmp2 = dfTmp2[~((dfTmp2['PeptideModifiedSequence'] == removedPeptide1) & (dfTmp2['PrecursorCharge'] == removedPeptide2))]\r\n\t\t# Add the dfTmp2 into normalDf\r\n\t\tnormalDf = pd.concat([normalDf, dfTmp2], ignore_index=True)\r\n\t\t# Extract the information of the removed peptides.\r\n\t\tfor removedPeptide in removedPeptideList:\r\n\t\t\tremovedPeptide1 = removedPeptide.split('$$$$')[0]\r\n\t\t\tremovedPeptide2 = removedPeptide.split('$$$$')[1]\r\n\t\t\tdfTmp3 = df[(df['PeptideModifiedSequence']==removedPeptide1) & (df['PrecursorCharge']==removedPeptide2)]\r\n\t\t\tdfTmp3['fragment_ion_complete'] = dfTmp3['FragmentIon']+\" (\"+dfTmp3['ProductCharge']+\"+)\"\r\n\t\t\tpeptide_list = dfTmp3['PeptideModifiedSequence'].unique()\r\n\t\t\tfor input_peptide_sequence in peptide_list:\r\n\t\t\t\tdfTmp4 = dfTmp3[(dfTmp3['PeptideModifiedSequence']==input_peptide_sequence)]\r\n\t\t\t\tprotein_list = dfTmp3['ProteinName'].unique()\r\n\t\t\t\tprotein_uniProtID_list = [identify_uniProtKB_entryID(protein_tmp) for protein_tmp in protein_list]\r\n\t\t\t\tfor indexLabel, protein_tmp in enumerate(protein_list):\r\n\t\t\t\t\tprotein_uniProtID = protein_uniProtID_list[indexLabel]\r\n\t\t\t\t\tdfTmp5 = dfTmp4[(dfTmp4['ProteinName']==protein_tmp)]\r\n\t\t\t\t\tfor precursorchargeTmp in dfTmp5['PrecursorCharge'].unique():\r\n\t\t\t\t\t\tdfTmp6 = dfTmp5[(dfTmp5['PrecursorCharge']==precursorchargeTmp)]\r\n\t\t\t\t\t\tisotopeLabelType_list = dfTmp6['IsotopeLabelType'].unique()\r\n\t\t\t\t\t\tisotopeLabelType_list.sort()\r\n\t\t\t\t\t\tisotopeLabelTypeTmp = '|'.join(isotopeLabelType_list)\r\n\t\t\t\t\t\ttransitionTmp = []\r\n\t\t\t\t\t\tfor isotopeLabelTypeSubtmp in isotopeLabelType_list:\r\n\t\t\t\t\t\t\tfragmentIon_list = dfTmp6[(dfTmp6['IsotopeLabelType']==isotopeLabelTypeSubtmp)]['fragment_ion_complete'].unique()\r\n\t\t\t\t\t\t\tfragmentIon_list.sort()\r\n\t\t\t\t\t\t\ttransitionTmp.append(':'.join([isotopeLabelTypeSubtmp, '|'.join(fragmentIon_list)]))\r\n\t\t\t\t\t\ttransitionTmp = ';'.join(transitionTmp)\r\n\t\t\t\t\t\tpeptide_excluded_in_Rscript_Df_tmp = pd.DataFrame({'peptide':[input_peptide_sequence], 'precursorCharge':[precursorchargeTmp], 'isotopeLabelType':[isotopeLabelTypeTmp], 'transition':[transitionTmp], 'uniProtKBID':[protein_uniProtID], 'proteinName':[protein_tmp], 'SkyDocumentName':[fileNameList[i]]}, columns=['peptide', 'precursorCharge', 'isotopeLabelType', 'transition', 'uniProtKBID', 'proteinName', 'SkyDocumentName'])\r\n\t\t\t\t\t\tpeptide_excluded_in_Rscript_Df = pd.concat([peptide_excluded_in_Rscript_Df, peptide_excluded_in_Rscript_Df_tmp], ignore_index=True)\r\n\treturn errorDf, normalDf, peptide_excluded_in_Rscript_Df\r\n\r\ndef detectIS(skyFileDir, fileName, experiment_type, error_report_path, errorDfColNumber):\r\n\t# Parse the *.sky file whose format is XML.\r\n\tfor event, elem in ET.iterparse(skyFileDir):\r\n\t\t\tif event == 'end':\r\n\t\t\t\tif elem.tag == 'peptide_modifications':\r\n\t\t\t\t\tinternal_standard= elem.get('internal_standard', default=None)\r\n\t\t\t\t\tif internal_standard == 'none':\r\n\t\t\t\t\t# This means that the user doesn't set the Internal Standard Type when preparing data for upload.\r\n\t\t\t\t\t# This situation works for experiment 1, 2, 3, 4 and 5.\r\n\t\t\t\t\t \t#print >> sys.stderr, \"Internal_standard value in the peptide_modifications underneath peptide_settings of the *.sky file of %s is unset. Please check it.\"%(skyFileDir)\r\n\t\t\t\t\t\tif experiment_type=='exp1' or experiment_type=='exp2':\r\n\t\t\t\t\t\t\terrorInfor = '\\t'.join([os.path.basename(fileName), 'Error', 'Internal standard', 'The internal standard in the skyline file is set to be none.']+['']*(errorDfColNumber-4))+'\\n'\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\terrorInfor = '\\t'.join([os.path.basename(fileName), 'Error', 'Internal standard', 'The internal standard in the skyline file is set to be none.']+['']*(errorDfColNumber-4))+'\\n'\r\n\t\t\t\t\t\twith open(error_report_path, 'a') as outfTmp:\r\n\t\t\t\t\t\t\toutfTmp.write(errorInfor)\r\n\t\t\t\t\t\tinternal_standard_type = 'none'\r\n\t\t\t\t\telif internal_standard is None:\r\n\t\t\t\t\t\t# This means that internal_standard is set to be heavy by default.\r\n\t\t\t\t\t\tinternal_standard_type = 'heavy'\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tinternal_standard_type = internal_standard\r\n\t\t\t\t\t# In exp3,exp4 and exp5, the default internal standard should always be heavy. A check will be performed in experiment3_qc.R, experiment4_qc.R and experiment5_qc.R\r\n\t\t\t\t\t#if experiment_type=='exp3' or experiment_type=='exp4' or experiment_type == 'exp5':\r\n\t\t\t\t\t\t# If internal_standard is not heavy, that means the user forgets to set it to be a correct value when preparing data for upload. \r\n\t\t\t\t\t#\tif internal_standard != 'heavy':\r\n\t\t\t\t\t#\t\terrorInfor = '\\t'.join([os.path.basename(fileName), 'Error', 'Internal standard','Internal standard type in the peptide_modifications underneath peptide_settings is incorrect. Please set it to be heavy']+['']*(errorDfColNumber-4))+'\\n'\r\n\t\t\t\t\t#\twith open(error_report_path, 'a') as outfTmp:\r\n\t\t\t\t\t#\t\toutfTmp.write(errorInfor)\r\n\t\t\telem.clear()\r\n\treturn internal_standard_type\r\n\r\ndef qcAnalysisRcode(experiment_type, error_report_path, dataset_path, fileList_path, mypeptideType_file_path, RscriptBinary, rScript, plot_output, plot_output_dir):\r\n\tif experiment_type == 'exp2' or experiment_type == 'exp5' or experiment_type == 'exp3' or experiment_type == 'exp4':\r\n\t\t#os.system('\"%s\" %s %s %s %s %s >> %s'%(RscriptBinary, rScript, dataset_path, fileList_path, plot_output, plot_output_dir, error_report_path))\r\n\t\tsubprocess.call([RscriptBinary, rScript, dataset_path, fileList_path, str(plot_output), plot_output_dir, \">>\", error_report_path])\r\n\tif experiment_type == 'exp1':\r\n\t\t#os.system('\"%s\" %s %s %s %s %s %s >> %s'%(RscriptBinary, rScript, dataset_path, fileList_path, plot_output, plot_output_dir, mypeptideType_file_path, error_report_path))\r\n\t\tsubprocess.call([RscriptBinary, rScript, dataset_path, fileList_path, str(plot_output), plot_output_dir, mypeptideType_file_path, \">>\", error_report_path])","sub_path":"src/MSInspector/utils/qcAnalysis.py","file_name":"qcAnalysis.py","file_ext":"py","file_size_in_byte":18399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"173028806","text":"def permute(nums):\n res = []\n dfs(nums, [], res)\n return res\n\n\ndef dfs(nums, path, res):\n if not nums:\n res.append(path)\n #print(\"res\",res)\n # return # backtracking\n for i in range(len(nums)):\n print(nums[:i],nums[i + 1:],path+[nums[i]],res)\n dfs(nums[:i] + nums[i + 1:], path + [nums[i]], res)\n\nprint(permute([1,2,3]))","sub_path":"problem46.py","file_name":"problem46.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"637994105","text":"import os, time, argparse\nimport util\n\nparser = argparse.ArgumentParser(description=\"info\")\nparser.add_argument(\"--year\", type=int, default=2021)\nparser.add_argument(\"--month\", type=int, default=6)\nparser.add_argument(\"--startday\", type=int, default=16)\nparser.add_argument(\"--endday\", type=int, default=17)\nparser.add_argument(\"--duration\", type=int, default=2)\nparser.add_argument(\"--src_dir\", type=str, default=\"/home_nfs/jiangyue/s3drive\")\nparser.add_argument(\"--dst_dir\", type=str, default=\"/home_nfs/jiangyue/data/carparking\")\nargs = parser.parse_args()\n\nyear, month = args.year, args.month\nstartday, endday, duration = args.startday, args.endday, args.duration\n\nsrc_dir = args.src_dir\ndst_dir = args.dst_dir\nmonth_dir = os.path.join(src_dir, f\"{year}/{month:02}\")\nprint(f\"Reading from {month_dir}...\")\n## constructing carpark_meta\ncarpark = util.read_carpark_json_days(month_dir, range(startday, startday+duration))\ncarpark_meta_path = os.path.join(dst_dir, \"carpark_meta_present.geojson\")\nif not os.path.exists(carpark_meta_path):\n carpark_meta = util.save_carpark_meta(carpark, carpark_meta_path)\nelse:\n carpark_meta = util.load_carpark_meta(carpark_meta_path)\n## constructing carpark_meta \n\n## saving carpark_data\ntic = time.time()\ncarpark_data_name = f\"carpark_data_{year}_{month:02}_{startday:02}-{min(endday, startday+duration-1):02}.json\"\nprint(f\"To save {carpark_data_name}\")\nutil.save_carpark_data(carpark, carpark_meta, os.path.join(dst_dir, carpark_data_name))\nprint(f\"save_carpark_data took {time.time() - tic} seconds.\")\n\nfor day in range(startday+duration, endday, duration):\n carpark = None # release the memory of carpark (large object)\n carpark = util.read_carpark_json_days(month_dir, range(day, day+duration))\n carpark_data_name = f\"carpark_data_{year}_{month:02}_{day:02}-{min(endday, day+duration-1):02}.json\"\n print(f\"To save {carpark_data_name}\")\n util.save_carpark_data(carpark, carpark_meta, os.path.join(dst_dir, carpark_data_name))\n","sub_path":"transform_carpark_prediction.py","file_name":"transform_carpark_prediction.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"164971669","text":"import logging\nfrom django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin\nfrom breathecode.admissions.admin import CohortAdmin\nfrom django.contrib.auth.models import User\nfrom django.contrib import messages\nfrom .models import Task, UserProxy, CohortProxy\nfrom .actions import sync_student_tasks, sync_cohort_tasks\n# Register your models here.\nlogger = logging.getLogger(__name__)\n\n\ndef sync_tasks(modeladmin, request, queryset):\n\n for u in queryset:\n try:\n Task.objects.filter(user_id=u.id).delete()\n sync_student_tasks(u)\n except Exception as e:\n logger.exception(f'There was a problem syncronizing tassks for student {u.email}')\n\n\nsync_tasks.short_description = 'Delete and sync Tasks'\n\n\n@admin.register(UserProxy)\nclass UserAdmin(UserAdmin):\n list_display = ('username', 'email', 'first_name', 'last_name')\n actions = [sync_tasks]\n\n\ndef sync_cohort_tasks(modeladmin, request, queryset):\n\n for c in queryset:\n try:\n Task.objects.filter(cohort__id=c.id).delete()\n sync_cohort_tasks(c)\n except Exception as e:\n pass\n\n\nsync_cohort_tasks.short_description = 'Delete AND SYNC Tasks for all students of this cohort'\n\n\ndef delete_cohort_tasks(modeladmin, request, queryset):\n\n for c in queryset:\n try:\n Task.objects.filter(cohort__id=c.id).delete()\n except Exception as e:\n pass\n\n\ndelete_cohort_tasks.short_description = 'Delete tasks for all students of this cohort'\n\n\n@admin.register(CohortProxy)\nclass CohortAdmin(CohortAdmin):\n list_display = ('id', 'slug', 'stage', 'name', 'kickoff_date', 'syllabus_version', 'specialty_mode')\n actions = [sync_cohort_tasks, delete_cohort_tasks]\n\n\ndef mark_as_delivered(modeladmin, request, queryset):\n queryset.update(task_status='DONE')\n\n\nmark_as_delivered.short_description = 'Mark task status as DONE'\n\n\ndef mark_as_approved(modeladmin, request, queryset):\n queryset.update(revision_status='APPROVED')\n\n\nmark_as_approved.short_description = 'Mark revision status as APPROVED'\n\n\ndef mark_as_rejected(modeladmin, request, queryset):\n queryset.update(revision_status='REJECTED')\n\n\nmark_as_rejected.short_description = 'Mark revision status as REJECTED'\n\n\n@admin.register(Task)\nclass TaskAdmin(admin.ModelAdmin):\n search_fields = ['title', 'associated_slug', 'user__first_name', 'user__last_name', 'user__email']\n list_display = ('title', 'task_type', 'associated_slug', 'task_status', 'revision_status', 'user')\n list_filter = ['task_type', 'task_status', 'revision_status']\n actions = [mark_as_delivered, mark_as_approved, mark_as_rejected]\n","sub_path":"breathecode/assignments/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"503629875","text":"\n\nclass Snake:\n COORDINATES = None\n\n SEGMENTS = None\n\n X = None\n Y = None\n\n DIRECTION = None\n\n DIRECTION_UP = 'up'\n DIRECTION_DOWN = 'down'\n DIRECTION_LEFT = 'left'\n DIRECTION_RIGHT = 'right'\n\n def __init__(self, coordinates, x=0, y=0, direction=DIRECTION_LEFT):\n print(\"[Snake][info] Initialising Snake\")\n\n self.COORDINATES = coordinates\n self.SEGMENTS = []\n self.add_segment((x, y))\n self.X = x\n self.Y = y\n self.DIRECTION = direction\n\n def set_coordinates(self, coordinates):\n print(\"[Snake][info] Setting Snake coordinates\")\n\n self.COORDINATES = coordinates\n\n def set_direction(self, direction):\n print(\"[Snake][info] Setting Snake direction\")\n\n self.DIRECTION = direction\n\n def set_x(self, x):\n print(\"[Snake][info] Setting Snake X position\")\n\n if self.length() > 1:\n self.remove_segment(-1)\n self.add_segment((x, self.SEGMENTS[0][1]), 0)\n else:\n self.SEGMENTS[0] = (x, self.SEGMENTS[0][1])\n\n self.X = x\n\n def set_y(self, y):\n print(\"[Snake][info] Setting Snake Y position\")\n\n if self.length() > 1:\n self.remove_segment(-1)\n self.add_segment((self.SEGMENTS[0][0], y), 0)\n else:\n self.SEGMENTS[0] = (self.SEGMENTS[0][0], y)\n\n self.Y = y\n\n def set_position(self, coordinates):\n print(\"[Snake][info] Setting Snake X, Y position\")\n\n x, y = coordinates\n\n if self.length() > 1:\n self.remove_segment(-1)\n self.add_segment(coordinates, 0)\n else:\n self.SEGMENTS[0] = coordinates\n\n self.X = x\n self.Y = y\n\n def add_segment(self, coordinates, position=-1):\n print(\"[Snake][info] Creating Snake segment at index %d\" % (position))\n\n self.SEGMENTS.insert(position if position != -1 else len(self.SEGMENTS), coordinates)\n\n def remove_segment(self, position=-1):\n print(\"[Snake][info] Removing Snake segment at index %d\" % (position))\n\n self.SEGMENTS.pop(position)\n\n def coordinates(self, columns=0, rows=0):\n print(\"[Snake][info] Getting Snake coordinates\")\n\n if columns == 0 and rows == 0:\n return self.COORDINATES\n\n coordinates = [\n [False for x in range(columns)]\n for y in range(rows)\n ]\n\n for segment in self.SEGMENTS:\n coordinates[segment[1]][segment[0]] = self.COORDINATES[0][0]\n\n return coordinates\n\n def direction(self):\n print(\"[Snake][info] Getting Snake direction\")\n\n return self.DIRECTION\n\n def x(self):\n print(\"[Snake][info] Getting Snake X position\")\n\n return self.X\n\n def y(self):\n print(\"[Snake][info] Getting Snake Y position\")\n\n return self.Y\n\n def position(self):\n print(\"[Snake][info] Getting Snake X,Y position\")\n\n return (self.X, self.Y)\n\n def segments(self):\n print(\"[Snake][info] Getting all Snake segments\")\n\n return self.SEGMENTS\n\n def head(self):\n print(\"[Snake][info] Getting Snake head\")\n\n return self.SEGMENTS[0]\n\n def body(self):\n print(\"[Snake][info] Getting Snake body\")\n\n if len(self.SEGMENTS) < 2:\n return None\n\n return self.SEGMENTS[1:]\n\n def tail(self):\n print(\"[Snake][info] Getting Snake tail\")\n\n return self.SEGMENTS[-1]\n\n def length(self):\n print(\"[Snake][info] Getting Snake length\")\n\n return len(self.SEGMENTS)\n\n def clear(self):\n print(\"[Snake][info] Clearing Snake\")\n\n self.COORDINATES = None\n self.SEGMENTS = None\n self.X = None\n self.Y = None\n self.DIRECTION = None\n\n def cleanup(self):\n print(\"[Snake][info] Snake clean up\")\n\n self.clear()\n\n def __exit__(self):\n print(\"[Snake][info] Snake exit\")\n\n self.cleanup()\n","sub_path":"sensehatsnake/lib/snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":3945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"224378120","text":"import contextlib\nimport os\nimport sys\nimport shlex\nimport pytest\n\n\nclass FailedTestCasesPlugin:\n def __init__(self):\n self.failed_test_cases = []\n\n def pytest_runtest_logreport(self, report):\n if report.when == \"call\" and report.outcome == 'failed':\n nodeid = report.nodeid\n self.failed_test_cases.append(nodeid)\n\n\ndef run_tests_return_failed_cases(args):\n with open(os.devnull, 'w') as devnull:\n with contextlib.redirect_stdout(devnull):\n with contextlib.redirect_stderr(devnull):\n plugin = FailedTestCasesPlugin()\n exit_code = pytest.main(\n args,\n plugins=[plugin],\n )\n\n return exit_code, plugin.failed_test_cases\n\n\nif __name__ == \"__main__\":\n exit_code, failed_cases = run_tests_return_failed_cases(sys.argv[1:])\n print(\"\\n\".join(failed_cases))\n print()\n exit(exit_code)","sub_path":"mutmut/pytest_wrapper.py","file_name":"pytest_wrapper.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"247608874","text":"import numpy as np\nimport argparse\nimport matplotlib.pyplot as plt\nimport cv2\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.layers import MaxPooling2D\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n# command line argument\nargum = argparse.ArgumentParser()\nargum.add_argument(\"--mode\",help=\"train/display\")\nmode = argum.parse_args().mode\n\n# plots accuracy and loss curves\ndef plot_model_history(model):\n \"\"\"\n Plot Accuracy and Loss curves given the model_history\n \"\"\"\n fig, axis = plt.subplots(1,2,figsize=(15,5))\n # summarize history for accuracy\n axis[0].plot(range(1,len(model.history['accuracy'])+1),model.history['accuracy'])\n axis[0].plot(range(1,len(model.history['val_accuracy'])+1),model.history['val_accuracy'])\n axis[0].set_title('Model Accuracy')\n axis[0].set_ylabel('Accuracy')\n axis[0].set_xlabel('Epoch')\n axis[0].set_xticks(np.arange(1,len(model.history['accuracy'])+1),len(model.history['accuracy'])/10)\n axis[0].legend(['train', 'val'], loc='best')\n # summarize history for loss\n axis[1].plot(range(1,len(model.history['loss'])+1),model.history['loss'])\n axis[1].plot(range(1,len(model.history['val_loss'])+1),model.history['val_loss'])\n axis[1].set_title('Model Loss')\n axis[1].set_ylabel('Loss')\n axis[1].set_xlabel('Epoch')\n axis[1].set_xticks(np.arange(1,len(model.history['loss'])+1),len(model.history['loss'])/10)\n axis[1].legend(['train', 'val'], loc='best')\n fig.savefig('plot.png')\n plt.show()\n\n# Define data generators\ntrain = 'data/train'\nval = 'data/test'\n\nno_of_train = 28709\nno_of_val = 7178\nbatch_size = 64\nno_of_epoch = 50\n\ntrain_datagenerator = ImageDataGenerator(rescale=1./255)\nval_datagenenerator = ImageDataGenerator(rescale=1./255)\n\ntrain_generator = train_datagenerator.flow_from_directory(\n train,\n target_size=(48,48),\n batch_size=batch_size,\n color_mode=\"grayscale\",\n class_mode='categorical')\n\nvalidation_generator = val_datagenerator.flow_from_directory(\n val,\n target_size=(48,48),\n batch_size=batch_size,\n color_mode=\"grayscale\",\n class_mode='categorical')\n\n# Create the model\nmdl = Sequential()\n\nmdl.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))\nmdl.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\nmdl.add(MaxPooling2D(pool_size=(2, 2)))\nmdl.add(Dropout(0.25))\nmdl.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\nmdl.add(MaxPooling2D(pool_size=(2, 2)))\nmdl.add(Dropout(0.25))\n\nmdl.add(Flatten())\nmdl.add(Dense(1024, activation='relu'))\nmdl.add(Dropout(0.5))\nmdl.add(Dense(7, activation='softmax'))\n\n# If you want to train the same model or try other models, go for this\nif mode == \"train\":\n mdl.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])\n mdl_info = mdl.fit_generator(\n train_generator,\n steps_per_epoch=no_of_train // batch_size,\n epochs=no_of_epoch,\n validation_data=validation_generator,\n validation_steps=no_of_val // batch_size)\n plot_mdl_history(model_info)\n mdl.save_weights('model.h5')\n\n# emotions will be displayed on your face from the webcam feed\nelif mode == \"display\":\n mdl.load_weights('model.h5')\n\n # dictionary which assigns each label an emotion (alphabetical order)\n emotion_dict = {0: \"Angry\", 1: \"Disgusted\", 2: \"Fearful\", 3: \"Happy\", 4: \"Neutral\", 5: \"Sad\", 6: \"Surprised\"}\n\n # start the webcam feed\n capturevideo = cv2.VideoCapture(0)\n while True:\n # Find haar cascade to draw bounding box around face\n ret, frame = capturevideo.read()\n if not ret:\n break\n casc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n faces = casc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)\n\n for (x, y, w, h) in faces:\n cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)\n roi_gray = gray[y:y + h, x:x + w]\n crop_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)\n prediction = mdl.predict(crop_img)\n maxindex = int(np.argmax(prediction))\n cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n\n cv2.imshow('Video', cv2.resize(frame,(1600,960),interpolation = cv2.INTER_CUBIC))\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n capturevideo.release()\n cv2.destroyAllWindows()","sub_path":"emotions.py","file_name":"emotions.py","file_ext":"py","file_size_in_byte":4822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"404587316","text":"import requests\n\ndef main(): \n \n res = requests.get(\"https://www.goodreads.com/book/review_counts.json\", \n params={\"key\":\"FxtMDTXu2B4X5nFYVfKfg\",\"isbns\":\"0553293427\"})\n \n if res.status_code != 200:\n raise Exception(\"Error: API request unsuccessful\")\n #isbns = '0553293427' \n data = res.json()\n #print(data)\n onebook=data['books'][0]\n count = onebook['ratings_count']\n #average_rating=data[\"books\"][\"average_rating\"]\n print(f\" {isbns} has rating_count {count} \")\n \nif __name__ == \"__main__\":\n main() \n ","sub_path":"goodreadAPI.py","file_name":"goodreadAPI.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"67154047","text":"\r\nfrom __future__ import unicode_literals\r\n# import youtube_dl\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nimport urllib.request\r\nfrom urllib.request import urlopen\r\nfrom bs4 import BeautifulSoup\r\nimport time\r\nimport os\r\nimport subprocess\r\nfrom bs4 import BeautifulSoup as bs\r\nimport requests\r\n#=================================== 여기 부터는 동영상 다운로드 관련 모듈설치 ==============================================\r\nimport os\r\nimport subprocess\r\nimport pytube\r\nfrom pytube import YouTube\r\n\r\nsearch = (input(\"검색어를 입력하세요.\"))\r\nsearch = str(search.encode('utf-8'))\r\nsearch = search.replace(\"\\\\\", '%').replace('x', '').upper().replace(\"'\", \"\")[1:]\r\ndriver = webdriver.Chrome(r'C:\\Users\\sub_account\\Desktop\\python\\chromedriver')\r\n# url = input(\"검색 \")\r\ndriver.get('https://www.youtube.com/results?search_query=' + search)\r\n# 'https://www.youtube.com/results?search_query=%EC%A0%95%EB%8F%99%EC%84%9D%EB%AA%A9%EC%82%AC%EC%84%A4%EA%B5%90/')\r\n\r\nbody = driver.find_element_by_tag_name(\"body\")\r\nnum_of_pagedowns = 0 # 50\r\ntime.sleep(1.5)\r\n\r\nwhile num_of_pagedowns:\r\n body.send_keys(Keys.PAGE_DOWN)\r\n time.sleep(1.2)\r\n num_of_pagedowns -= 1\r\n try:\r\n driver.find_element_by_xpath().click()\r\n except:\r\n None\r\n\r\nhtml = driver.page_source\r\n\r\nsoup = BeautifulSoup(html, 'lxml')\r\n\r\narr_txt = list()\r\n\r\nfor sour in soup.find_all('a', href=True, title=True):\r\n time.sleep(0.7)\r\n # d = {'title' : sour['title'], 'url' : 'https://www.youtube.com' + sour['href'], 'aria' : sour.get('aria-label')}\r\n\r\n tmp_str_0 = sour.get('aria-label')\r\n print(tmp_str_0)\r\n\r\n d = {}\r\n\r\n if (tmp_str_0 != None):\r\n T1 = tmp_str_0.find('게시자: ');\r\n tmp_str_1 = tmp_str_0[T1 + 5:];\r\n T2 = tmp_str_1.find(' ');\r\n tmp_str_2 = tmp_str_1[T2:];\r\n pubr = tmp_str_1[0:T2]; #\r\n T3 = tmp_str_2.find('전');\r\n tmp_str_3 = tmp_str_2[T3:];\r\n pdate = tmp_str_2[0:T3]; #\r\n T4 = tmp_str_3.find('조회수 ');\r\n tmp_str_4 = tmp_str_3[T4:];\r\n run_time = tmp_str_3[1:T4]; #\r\n tmp_str_5 = tmp_str_4[4:-1];\r\n tmp_v = tmp_str_5;\r\n tmp_v = tmp_v.replace(\",\", \"\");\r\n viewer = int(tmp_v);\r\n\r\n d = {'title': sour['title'], 'url': 'https://www.youtube.com' + sour['href'], \\\r\n 'Pubr': pubr, 'Pdate': pdate, 'Run_time': run_time, 'Viewer': viewer}\r\n\r\n print('http://www.youtube.com' + sour['href'])\r\n\r\n arr_txt.append(d)\r\n time.sleep(0.5)\r\n response = 'https://www.youtube.com' + sour['href']\r\n driver.get(response)\r\n time.sleep(0.5)\r\n soup_link = BeautifulSoup(driver.page_source, 'html.parser')\r\n time.sleep(0.5)\r\n\r\n sour2 = soup_link.find_all('span', slot=\"date\")\r\n print(\"\\n영상 정보=======================================================================\\n\")\r\n x = str(sour2).find('게시일: ')\r\n print(str(sour2)[x:].replace('', '').replace(']', ''))\r\n # print(sour2)\r\n\r\n '''r= requests.get('https://www.youtube.com' + sour['href'])\r\n\r\n r.text\r\n\r\n haha=bs(r.text,'html.parser')\r\n\r\n'''\r\n# 걸러내기전에 프린트하는 loop이라서 주석처리 해놨습니다.\r\n# for a in arr_txt:\r\n# print(a['title'])\r\n# print(a['url'])\r\n# print(a['Pubr'])\r\n# print(a['Pdate'])\r\n# print(a['Run_time'])\r\n# print(a['Viewer'])\r\n# print()\r\n\r\nprint('end')\r\n\r\nvalue1 = []\r\nvalue2 = []\r\nurl = []\r\nnew_arr = []\r\n\r\nfor d in arr_txt:\r\n if d['Run_time'] in value1 and d['title'] in value2:\r\n print('중복 삭제');\r\n else:\r\n new_arr.append(d)\r\n value1.append(d['Run_time'])\r\n value2.append(d['title'])\r\n\r\n'''ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'outtmpl': 'E:\\python\\mp3s\\%(title)s.%(ext)s', # 여기서 원하시는 위치 설정하시면 됩니다.\r\n 'postprocessors': [{ # 폴더가 존제하지 않으면 프로그램이 만들어줍니다.\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n}'''\r\n\r\n'''\r\nfor a in new_arr:\r\n print('??')\r\n print(a['title'])\r\n print(a['url'])\r\n print(a['Pubr'])\r\n print(a['Run_time'])\r\n print(a['Viewer'])\r\n'''\r\n\r\n# with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n# ydl.download(url)\r\n\r\n# ///////////////////////////////////////////////////////////////////////////////////////////////////////////\r\n# /////////////////////파일다운로드하는 코드////////////////////////////////////////////////\r\n# ///////////////////////////////////////////////////////////////////////////////////////////////////////////\r\n# ///////////////////////////////////////////////////////////////////////////////////////////////////////////\r\n# lists = []\r\n#\r\n# while True:\r\n# link = input(\"다운로드 받고 싶은 링크 url을 입력해주세요.:\\n 더 이상 없으시면 '종료'을 입력해주세요.: \")\r\n# if 'youtube.' in link:\r\n# lists.append(link)\r\n# elif '종료' in link:\r\n# print('파일 다운로드를 시작합니다!')\r\n# break\r\n# else:\r\n# print('잘못된 형식입니다. 요청에 맞게 입력해주세요.')\r\n#\r\n# for video_url in lists:\r\n# yt = YouTube(video_url)\r\n# print(yt.title)\r\n# print('다운로드 중입니다......')\r\n# yt.streams.first().download()\r\n#\r\n# print('다운로드 완료!')","sub_path":"ctube/Form/modules/rawctube.py","file_name":"rawctube.py","file_ext":"py","file_size_in_byte":5423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"618677236","text":"from openeye.oechem import *\nfrom openeye.oedocking import *\n\nfrom .intercalator import Intercalator\n\n\nclass DNA:\n def __init__(self, dna: OEMolBase, box: OEBox):\n self._dna: OEMolBase = dna\n self._receptor: OEMolBase = OEGraphMol()\n OEMakeReceptor(self._receptor, dna, box)\n\n self._dock: OEDock = OEDock(OEDockMethod_Chemgauss4, OESearchResolution_High)\n self._dock.Initialize(self._receptor)\n\n @property\n def dna(self):\n return self._dna\n\n @classmethod\n def with_original_intercalator(cls, dna: OEMolBase, original_intercalator: Intercalator, padding: float=2.0):\n\n box = OEBox(original_intercalator.mol)\n\n max_side = max(cls.get_box_dims(box))\n\n extend = [(max_side-c)/2 + padding for c in cls.get_box_dims(box)]\n\n OEBoxExtend(box, *extend)\n\n return cls(dna, box)\n\n @classmethod\n def with_intercalators(cls, dna: OEMolBase, original_intercalator: Intercalator, intercalators: [Intercalator],\n padding: float=2.0):\n \"\"\"Convenience initializer for multiple intercalators and when there was an original intercalator\n inside the crystal structure.\n\n :param dna: The DNA structure only.\n :param original_intercalator: The original intercalator that was in the crystal structure.\n :param intercalators: The list of intercalator you will want to dock\n :param padding: padding to add to the box created around the original intercalator\n :return: a DNA instance correctly initialized for docking\n \"\"\"\n # This is the box of the original intercalator. We want to dock somewhere around here.\n box = OEBox(original_intercalator.mol)\n\n max_side = max(d for xyz in [cls.get_box_dims(OEBox(mol.mol)) for mol in (intercalators + [original_intercalator])] for d in xyz)\n\n extend = [(max_side-c)/2 + padding for c in cls.get_box_dims(box)]\n\n OEBoxExtend(box, *extend)\n\n return cls(dna, box)\n\n @staticmethod\n def get_box_dims(box):\n return OEBoxXDim(box), OEBoxYDim(box), OEBoxZDim(box)\n\n def dock_intercalator(self, intercalator: Intercalator):\n \"\"\"\n Dock molecule into DNA base-pair sequence.\n :param intercalator: molecule to be docked\n :return: docked molecule, rmsd\n Note, rmsd might only be relevant if the original molecule was, let's say, at a crystal structure\n docked site. Otherwise the rmsd from some arbitrary position is irrelevant.\n \"\"\"\n docked_mol = OEGraphMol()\n self._dock.DockMultiConformerMolecule(docked_mol, intercalator.mol)\n sd_tag = OEDockMethodGetName(OEDockMethod_Chemgauss4)\n OESetSDScore(docked_mol, self._dock, sd_tag)\n self._dock.AnnotatePose(docked_mol)\n\n return Intercalator(docked_mol), OERMSD(intercalator.mol, docked_mol)\n\n","sub_path":"dnatools/dna.py","file_name":"dna.py","file_ext":"py","file_size_in_byte":2879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"579172068","text":"#!/usr/bin/env python\n__author__ = 'Sergei F. Kliver'\nimport os\nimport shutil\nimport numpy as np\nimport multiprocessing as mp\n\nimport matplotlib\nmatplotlib.use('Agg')\nos.environ['MPLCONFIGDIR'] = '/tmp/'\nimport matplotlib.pyplot as plt\nplt.ioff()\n\ntry:\n from aindex import *\nexcept:\n print(\"WARNING!!! aindex package was not found! Related code will not work\")\n\nfrom RouToolPa.Tools.Abstract import Tool\nfrom RouToolPa.Parsers.Sequence import CollectionSequence\n\n\nclass AIndexRoutines(Tool):\n\n def __init__(self, path=\"\", max_threads=4):\n Tool.__init__(self, \"aindex\", path=path, max_threads=max_threads)\n\n def scan_for_contamination(self,\n sequence_file,\n index_prefix,\n kmer_length,\n output_file,\n aindex_prefix=None,\n reads_file=None,\n parsing_mode=\"parse\",\n external_process_pool=None,\n threads=None):\n\n index_settings = {\n \"index_prefix\": index_prefix,\n \"aindex_prefix\": aindex_prefix,\n \"reads_file\": reads_file,\n }\n\n print(\"Parsing fasta file...\")\n\n sequence_collection = CollectionSequence(in_file=sequence_file, parsing_mode=parsing_mode)\n sequence_collection.get_stats_and_features(count_gaps=False, sort=False, min_gap_length=1)\n\n index = load_aindex(index_settings,\n skip_reads=False if reads_file else True,\n skip_aindex=False if reads_file else True)\n\n results = []\n counter = 0\n for seq_id in sequence_collection.scaffolds:\n #print sequence_collection.seq_lengths.at[seq_id, \"length\"]\n results.append((seq_id, [index[sequence_collection.records[seq_id][i:i + kmer_length]] \\\n for i in xrange(sequence_collection.seq_lengths.at[seq_id, \"length\"] - kmer_length + 1)]))\n counter += 1\n if counter % 1000 == 0:\n print(\"%i sequences were handled...\" % counter)\n \"\"\"\n def get_kmer_coverage(seq_tuple):\n return (seq_tuple[0], [index[seq_tuple[1][i:i + kmer_length]] \\\n for i in xrange(len(seq_tuple[1]) - kmer_length + 1)])\n\n process_pool = external_process_pool if external_process_pool else mp.Pool(threads if threads else self.threads)\n\n results = process_pool.map(get_kmer_coverage, sequence_collection.scaffolds, chunksize=1)\n \"\"\"\n print(\"Analyzing results...\")\n with open(output_file, \"w\") as out_fd:\n out_fd.write(\"#record_id\\tkmer_number\\tcovered_positions\\tcovered_positions,%\\t\"\n \"kmer_mean_coverage\\tkmer_median_coverage\\tdescription\\n\")\n for seq_id, kmer_coverage_list in results:\n kmer_number = sequence_collection.seq_lengths.at[seq_id, \"length\"]-kmer_length+1\n covered_positions = kmer_number - kmer_coverage_list.count(0)\n\n mean_kmer_coverage = np.mean(kmer_coverage_list)\n median_kmer_coverage = np.median(kmer_coverage_list)\n\n out_fd.write(\"%s\\t%i\\t%i\\t%.2f\\t%.2f\\t%.2f\\t%s\\n\" % (seq_id,\n kmer_number,\n covered_positions,\n 100*float(covered_positions)/float(kmer_number),\n mean_kmer_coverage,\n median_kmer_coverage,\n sequence_collection.description[seq_id]))\n\n","sub_path":"KRATER/Routines/AIndexRoutines.py","file_name":"AIndexRoutines.py","file_ext":"py","file_size_in_byte":3985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"184122507","text":"import os\r\n\r\n\r\ndef main():\r\n print_title()\r\n file_path = input(\"Please enter the file path you'd like to use: \")\r\n while not os.path.isfile(file_path):\r\n file_path = input(\"Whoops! No such file! Please enter the name of the file you'd like to use: \")\r\n another_round = 1\r\n while another_round == 1:\r\n index = input(\"choose a random number for your random secret word: \")\r\n while index.isnumeric() == False:\r\n index = input(\"choose a random NUMBER!! NUMBER for your random secret word. type again: \")\r\n secret_word = choose_word(file_path, index)\r\n old_letters_guessed = []\r\n print(\"lets start!\")\r\n hidden_word = show_hidden_word(secret_word, old_letters_guessed)\r\n num_of_tries = get_num_of_tries(hidden_word, old_letters_guessed)\r\n print_hangman(num_of_tries)\r\n print(hidden_word)\r\n\r\n game_result = check_win(secret_word, old_letters_guessed)\r\n failed_tries = get_num_of_tries(hidden_word, old_letters_guessed)\r\n MAX_TRIES = 6\r\n\r\n while game_result == False and failed_tries != MAX_TRIES:\r\n letter_guessed = input(\"guess a letter: \")\r\n\r\n while try_update_letter_guessed(letter_guessed, old_letters_guessed) == False:\r\n letter_guessed = input(\"invalid letter, please type again: \")\r\n\r\n word_round_1 = show_hidden_word(secret_word, old_letters_guessed)\r\n if word_round_1 == hidden_word:\r\n print(\":(\")\r\n lenOfMistakes = get_num_of_tries(hidden_word, old_letters_guessed)\r\n print_hangman(lenOfMistakes)\r\n\r\n hidden_word = word_round_1\r\n print(hidden_word)\r\n\r\n failed_tries = get_num_of_tries(hidden_word, old_letters_guessed)\r\n game_result = check_win(secret_word, old_letters_guessed)\r\n\r\n if game_result == True:\r\n print(\"\\nWIN\\n\") \r\n elif failed_tries == MAX_TRIES:\r\n print(\"\\nLOSE\\n\")\r\n\r\n #when game ends- option to play another round\\end game.\r\n another_round = input(\"do you want to play again ?\\n1 = yes\\n2 = no\\n\")\r\n if another_round.isnumeric() == True:\r\n another_round = int(another_round)\r\n\r\n if another_round == 2:\r\n print(\"thank you for playing!\")\r\n break\r\n\r\n while another_round != 1 and another_round != 2:\r\n another_round = input(\"you have only 2 options, 1 or 2. go ahead: \\n\")\r\n if another_round.isnumeric() == True:\r\n another_round = int(another_round)\r\n\r\n if another_round == 2:\r\n print(\"thank you for playing!\")\r\n break\r\n\r\n #clears screen for new game\r\n os.system('clear')\r\n print_title()\r\n\r\n\r\ndef print_title():\r\n \"\"\"printing the title of the game\r\n return:none\"\"\"\r\n HANGMAN_ASCII_ART = \"\"\"welcome to the game hangman\r\n _ _ \r\n | | | | \r\n | |__| | __ _ _ __ __ _ _ __ ___ __ _ _ __ \r\n | __ |/ _` | '_ \\ / _` | '_ ` _ \\ / _` | '_ \\ \r\n | | | | (_| | | | | (_| | | | | | | (_| | | | |\r\n |_| |_|\\__,_|_| |_|\\__, |_| |_| |_|\\__,_|_| |_|\r\n __/ | \r\n |___/\r\n\"\"\"\r\n print(HANGMAN_ASCII_ART)\r\n\r\n\r\ndef choose_word(file_path, index):\r\n \"\"\"the function choosing a word according to the index number from user.\r\n :param file_path: path to file that contains words divided by \" \".\r\n :type file_Path: string\r\n :param index: a number choosed by the user.\r\n :type index: int\r\n :return: word from file_path\r\n :rtype: str.\"\"\"\r\n\r\n opened_file = open(file_path, \"r\")\r\n readed_file = opened_file.read()\r\n opened_file.close()\r\n readed_file = readed_file.replace(\"\\n\", \"\")\r\n random_words = readed_file.split(\" \")\r\n\r\n random_words_no_duplications = []\r\n for item in random_words:\r\n if item not in random_words_no_duplications:\r\n random_words_no_duplications.append(item)\r\n else:\r\n continue\r\n\r\n number_of_words_from_original = int(len(random_words))\r\n index = int(index) - 1\r\n\r\n new_index = index % number_of_words_from_original\r\n\r\n secret_word = random_words[new_index]\r\n\r\n return secret_word\r\n\r\n\r\ndef show_hidden_word(secret_word, old_letters_guessed):\r\n \"\"\"replace letter with \"_\" if its not in old_letter_guessed.\r\n :param secret_word: word choosed from file_path.\r\n :type secret_word: str.\r\n :param old_letters_guessed: list of valid guessed letters.\r\n :type old_letters_guessed: list.\r\n :return: string of secret word with '_' where letter not guessed.\r\n :rtype: str.\"\"\"\r\n copy_secret_word = secret_word\r\n for letter in secret_word:\r\n if letter not in old_letters_guessed:\r\n copy_secret_word = copy_secret_word.replace(letter, \" _ \")\r\n return copy_secret_word\r\n\r\n\r\ndef get_num_of_tries(return_show_hidden_word, old_letters_guessed):\r\n \"\"\"the function get the number of wrong tries.\r\n :param return_show_hidden_word: the correct letters guessed from secret word.\r\n :type return_show_hidden_word: str.\r\n :param old_letters_guessed: all the guessed letters included the correct letters.\r\n :type old_letters_guessed: list.\r\n :return: the number of wrong tries from the list of old_letters_guessed.\r\n :rtype: int\"\"\"\r\n wrong_tries = []\r\n for letter in old_letters_guessed:\r\n if letter not in return_show_hidden_word:\r\n wrong_tries.append(letter)\r\n num_of_tries = len(wrong_tries)\r\n return num_of_tries\r\n\r\n\r\ndef print_hangman(num_of_tries):\r\n \"\"\"the funtion return the picture of hangman according to number of tries.\r\n :param num_of_tries: the number of wrong tries.\r\n :type num_of_tries: int.\r\n :return: the picture of hangman according to the num_of_tries from dict.\r\n :rtype: str.\"\"\"\r\n\r\n picture_1 = \"\"\"x-------x\"\"\"\r\n\r\n picture_2 = \"\"\"\r\n x-------x\r\n |\r\n |\r\n |\r\n |\r\n |\"\"\"\r\n\r\n picture_3 = \"\"\"\r\n x-------x\r\n | |\r\n | 0\r\n |\r\n |\r\n |\"\"\"\r\n\r\n picture_4 = \"\"\"\r\n x-------x\r\n | |\r\n | 0\r\n | |\r\n |\r\n |\"\"\"\r\n\r\n picture_5 = \"\"\"\r\n x-------x\r\n | |\r\n | 0\r\n | /|\\\\\r\n |\r\n |\"\"\"\r\n\r\n picture_6 = \"\"\"\r\n x-------x\r\n | |\r\n | 0\r\n | /|\\\\\r\n | /\r\n |\"\"\"\r\n\r\n picture_7 = \"\"\"\r\n x-------x\r\n | |\r\n | 0\r\n | /|\\\\\r\n | / \\\\\r\n |\"\"\"\r\n HANGMAN_PHOTOS = {0: picture_1, 1: picture_2, 2: picture_3, 3: picture_4, 4: picture_5, 5: picture_6, 6: picture_7}\r\n print(HANGMAN_PHOTOS[num_of_tries])\r\n\r\n\r\ndef is_valid_input(guess_letter):\r\n \"\"\"the function returns false if the letter is invalid\r\n or true if its valid(only 1 letter).\r\n :param guess_letter: the input of guessed letter.\r\n :type guess_letter: str.\r\n :return: true if its valid, fales if its invalid.\r\n :rtype: boolean.\"\"\"\r\n length = len(guess_letter)\r\n\r\n if length > 1 and not guess_letter.isalpha():\r\n return False\r\n elif not guess_letter.isalpha():\r\n return False\r\n elif length > 1:\r\n return False\r\n else:\r\n return True\r\n\r\n\r\ndef check_valid_input(letter_guessed, old_letters_guessed):\r\n \"\"\"the function returns true if the letter typed is valid\r\n and not typed before.\r\n :param letter_guessed: the input of letter_guessed from user.\r\n :type letter_guessed: str.\r\n :param old_letters_guessed: all the guessed letters included the correct letters.\r\n :type old_letters_guessed: list.\r\n :return: true if the letter typed is valid and not typed before\\ False.\r\n :rtype: boolean.\"\"\"\r\n if is_valid_input(letter_guessed) == False:\r\n return False\r\n elif letter_guessed in old_letters_guessed:\r\n return False\r\n elif is_valid_input(letter_guessed) == True and letter_guessed not in old_letters_guessed:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef try_update_letter_guessed(letter_guessed, old_letters_guessed):\r\n \"\"\"function adds valid letter to old_letter_gussed list,\r\n in addition print 'X' and list of sorted old_letters_gussed with -> if not valid\r\n and returns true if valid or flase if not valid.\r\n :param letter_guessed: the input of letter_guessed from user.\r\n :type letter_guessed: str.\r\n :param old_letters_guessed: all the guessed letters included the correct letters.\r\n :type old_letters_guessed: list.\r\n :return:True if check_valid_input true, else false\r\n :rtype: boolean\"\"\"\r\n letter_guessed = letter_guessed.lower()\r\n if check_valid_input(letter_guessed, old_letters_guessed) == True:\r\n old_letters_guessed.append(letter_guessed)\r\n return True\r\n else:\r\n print(\"X\")\r\n old_letters_guessed.sort()\r\n print(\"->\".join(old_letters_guessed))\r\n return False\r\n\r\n\r\ndef check_win(secret_word, old_letters_guessed):\r\n \"\"\"the func returns True if the player guessed all the letters in the secret word.\r\n else, returns False.\r\n :param secret_word: secret word choosed.\r\n :type secret_word: str.\r\n :param old_letters_guessed: list of all guessed letters.\r\n :type old_letters_guessed: list.\r\n :return: true if hidden word = secret_word, false if not.\r\n :rtype: boolean.\"\"\"\r\n if show_hidden_word(secret_word, old_letters_guessed) == secret_word:\r\n return True\r\n else:\r\n return False\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","sub_path":"the_hangman_game.py","file_name":"the_hangman_game.py","file_ext":"py","file_size_in_byte":9531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"76237084","text":"#!/usr/bin/python3\n\nimport sys\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, Gdk, GObject\n\nclr = ( \"00\", \"33\", \"66\", \"99\", \"CC\", \"FF\" )\nCOLOR = 0\n\nclass AppWindow(Gtk.ApplicationWindow):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.set_border_width(10)\n self.set_size_request(250, 175)\n treeview = Gtk.TreeView.new()\n self.setup_tree_view(treeview)\n store = Gtk.ListStore.new((GObject.TYPE_STRING, \n GObject.TYPE_STRING, GObject.TYPE_STRING))\n for var1 in clr:\n for var2 in clr:\n for var3 in clr:\n color = \"#\" + var1 + var2 + var3\n iter = store.append()\n store.set(iter, (COLOR,), (color,))\n treeview.set_model(store)\n scrolled_win = Gtk.ScrolledWindow.new(None, None)\n scrolled_win.set_policy(Gtk.PolicyType.AUTOMATIC, Gtk.PolicyType.AUTOMATIC)\n scrolled_win.add(treeview)\n self.add(scrolled_win)\n\n def setup_tree_view(self, treeview):\n renderer = Gtk.CellRendererText.new()\n column = Gtk.TreeViewColumn.new()\n column.pack_start(renderer, True)\n column.add_attribute(renderer, \"text\", COLOR)\n column.set_title(\"Standard Colors\")\n treeview.append_column(column)\n column.set_cell_data_func(renderer, self.cell_data_func, None)\n\n def cell_data_func(self, column, renderer, model, iter, data):\n # Get the color string stored by the column and make it the foreground color.\n (text,) = model.get(iter, COLOR)\n renderer.props.foreground_rgba = Gdk.RGBA(red=1.0, green=1.0, blue=1.0, alpha=1.0)\n red = int(text[1:3], 16) / 255\n green = int(text[3:5], 16) / 255\n blue = int(text[5:7], 16) / 255\n renderer.props.background_rgba = Gdk.RGBA(red=red, green=green, blue=blue, alpha=1.0)\n renderer.props.text = text\n\nclass Application(Gtk.Application):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, application_id=\"org.example.myapp\",\n **kwargs)\n self.window = None\n\n def do_activate(self):\n if not self.window:\n self.window = AppWindow(application=self, \n title=\"Color List\")\n self.window.show_all()\n self.window.present()\n\nif __name__ == \"__main__\":\n app = Application()\n app.run(sys.argv)\n","sub_path":"book/09-Tree_View_Widget/Using_Cell_Data_Methods.py","file_name":"Using_Cell_Data_Methods.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487036037","text":"#!/usr/bin/env python\n\nimport rospy\nfrom geometry_msgs.msg import Point32, Point, Pose, Quaternion, Twist, Vector3\nfrom nav_msgs.msg import Odometry\nimport tf\n\nodom_broadcaster = tf.TransformBroadcaster()\nodom_pub = None\n\ndef callback(msg):\n current_time = rospy.Time.now()\n odom_quat = tf.transformations.quaternion_from_euler(0, 0, msg.z)\n\n odom_broadcaster.sendTransform(\n (msg.x, msg.y, 0.),\n odom_quat,\n current_time,\n \"base_link\",\n \"odom\"\n )\n\n odom = Odometry()\n odom.header.stamp = current_time\n odom.header.frame_id = \"odom\"\n\n odom.pose.pose = Pose(Point(msg.x, msg.y, 0.), Quaternion(*odom_quat))\n\n odom.child_frame_id = \"base_link\"\n odom.twist.twist = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0))\n\n # publish the message\n odom_pub.publish(odom)\n\nif __name__ == '__main__':\n rospy.init_node('odometry_publisher')\n\n odom_pub = rospy.Publisher(\"odom\", Odometry, queue_size=50)\n rospy.Subscriber('odom_pose', Point32, callback, queue_size = 10)\n rospy.spin()\n","sub_path":"metal_tank_software/scripts/odometry_pub.py","file_name":"odometry_pub.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"646048092","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 5 01:33:02 2017\n\n@author: ASSG\n\"\"\"\n# In[1]:\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns; sns.set()\nimport matplotlib.pyplot as plt\n#%matplotlib qt5\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn import linear_model as lm\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder, Imputer\nfrom sklearn.metrics import mean_squared_error\n\ndf = pd.read_csv('train.csv')\ndftest = pd.read_csv(\"test.csv\")\n\n#Preproces the data\n# Use the log of saleprice!!\ndf[\"logSalePrice\"] = np.log(df.SalePrice)\n#select only relevant feats. (numeric except the outputs)\nnumdf = df.select_dtypes(include=[np.number]).drop([\"Id\",\"logSalePrice\",\"SalePrice\"],1)\nnumdf_test = dftest.select_dtypes(include=[np.number]).drop([\"Id\"],1)\nimputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)\n#train the imputer (in this case get the mean):\nimputer = imputer.fit(numdf.values)\n#apply the imputer transformation, i.e. replace NaN by the mean, don't forget to apply exactly the same transf to the test set!!!\nx = imputer.transform(numdf.values)\nxt = imputer.transform(numdf_test.values)\n\ndfclean = pd.DataFrame(x)\ndf_testclean = pd.DataFrame(xt)\n\ny = df[\"logSalePrice\"]\n\n#%% Function that receives vector of features to use to train the linear regr. model and returns R2\nlr = lm.LinearRegression()\n\ndef calc_r2(vfeats):\n x = dfclean[vfeats]\n model = lr.fit(x,y)\n r2 = model.score(x,y)\n n = len(x)\n k = len(x.columns)\n return 1 - (1-r2)*(n-1)/(n-k-1)\n\n#%%\n\nvfeats = []\nres= []\nm = len(dfclean.columns)\nfor num_feats in range(m):\n l_r2 = []\n for i in range(m):\n if not i in vfeats:\n l_r2.append([i,calc_r2(vfeats + [i])])\n nplr2 = np.array(l_r2)\n R2_max = nplr2[:,1].max()\n arg_R2_max = nplr2[np.argmax(nplr2[:,1]),0].astype(int)\n print(\"{} features. Max adj R2 of {}. New feat added was {}:{}\".format(num_feats + 1, R2_max,arg_R2_max,numdf.columns[arg_R2_max]))\n vfeats.append(arg_R2_max)\n res.append([num_feats,R2_max])\n\nres = np.array(res)\nplt.scatter(res[:,0],res[:,1])\nX=vfeats[:19]\n\n#%% predict using 19 first features\nFinalModel = lr.fit(dfclean[X],y)\n\npred=np.exp(lr.predict(df_testclean[vfeats[:14]]))\n\ndfpred = pd.concat([dftest.Id,pd.DataFrame(pred,columns=[\"SalePrice\"])],axis=1)\ndfpred.to_csv(\"submission_14feats.csv\", index = False)\n### \"Your submission scored 0.14355, \" 1083rd place!!!!\n\n#%% predict using ridge regression\nreg = lm.RidgeCV(alphas = [0.01,0.02,0.03,0.5,0.7,.9],normalize=True)\nreg.fit(dfclean[X],y)\nreg.alpha_\n\npred=np.exp(reg.predict(df_testclean[vfeats[:19]]))\n\ndfpred = pd.concat([dftest.Id,pd.DataFrame(pred,columns=[\"SalePrice\"])],axis=1)\ndfpred.to_csv(\"submission_ridge.csv\", index = False)\n# score=0.14298 even worse booo\n#%% Let's try Lasso\nregl = lm.LassoCV(alphas = [1e-5,1e-4,1e-3,1e-2,1e-1],normalize=True)\nregl.fit(dfclean[X],y)\nregl.alpha_\n\npred=np.exp(regl.predict(df_testclean[vfeats[:19]]))\n\ndfpred = pd.concat([dftest.Id,pd.DataFrame(pred,columns=[\"SalePrice\"])],axis=1)\ndfpred.to_csv(\"submission_Lasso.csv\", index = False)\n\n#cross validation\ndef crossValidation(inputSet):\n kf = KFold(n_splits=10)\n for train, test in kf.split(inputSet):\n #print(\"%s %s\" % (train, test))\n lr.fit(inputSet.loc[train], y.loc[train])\n pred_cv = lr.predict(inputSet.loc[test])\n mean_squared_error(y.loc[test], pred_cv)\n\ncrossValidation(dfclean[vfeats[:14]])\n","sub_path":"Feature_Engineer.py","file_name":"Feature_Engineer.py","file_ext":"py","file_size_in_byte":3439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"219199728","text":"#!/usr/bin/env python\n\nfrom argparse import ArgumentParser\n\ndef main():\n from io import BytesIO\n args = _ArgumentParser().parse_args()\n b = BytesIO()\n from_ = args.from_\n if from_:\n b.write(r'from ')\n b.write(from_)\n b.write(' ')\n b.write(r'import ')\n help_ = args.help\n b.write(br', '.join(help_))\n b.write('\\n')\n for w in help_:\n b.write(r'help(')\n b.write(w)\n b.write(')\\n')\n exec(b.getvalue()) # pylint: disable=exec-used\n\nclass _ArgumentParser(ArgumentParser):\n def __init__(self):\n super(_ArgumentParser, self).__init__()\n self.add_argument(r'-f', r'--from', dest=r'from_')\n self.add_argument(r'help', nargs='+')\n\nif __name__ == r'__main__':\n main()\n","sub_path":"pyhelp.py","file_name":"pyhelp.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"87344094","text":"#!/usr/bin/env python\n\nimport sys\nimport subprocess\nimport i3ipc\n\ni3 = i3ipc.Connection()\n\n\ndef on(i3, e):\n if e.container.window_class == 'Gnome-terminal':\n e.container.command('floating enable')\n\n e.container.command(\"resize set 748 px 460 px, move window to position 347 px 230 px\")\n sys.exit(0)\n\n\nworkspace_empty = i3.get_tree().find_focused().type == 'workspace'\nsubprocess.Popen(['gnome-terminal'], close_fds=True)\n\nif not workspace_empty:\n sys.exit(0)\n\ni3.on('window::new', on)\ntry:\n i3.main()\nfinally:\n i3.main_quit()\n","sub_path":"scripts/term.py","file_name":"term.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"164037030","text":"import datetime as dt\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as md\nimport os\nimport pandas as pd\nimport numpy as np\n\ndef gainstab_csv(inPath,csv_file):\n '''\n Description: Returns matplotlib figure from .csv\n :param inPath: /path/to/file\n :param csv_file: name of .csv file\n\n :return: matplotlib figure of K40 & Th232 channel\n '''\n df = pd.read_csv(inPath+csv_file,header=0)\n fig, ax = plt.subplots(nrows=3,sharex=True)\n dates = [dt.datetime.fromtimestamp(ts) for ts in df.time]\n datetimes = np.array([md.date2num(v) for v in dates])\n ax[0].plot(datetimes,df['total_counts'],label='Avg.10s CPS')\n ax[0].set_ylabel('CPS')\n ax[0].grid(alpha=0.5)\n xfmt = md.DateFormatter('%m/%d %H:%M')\n ax[0].xaxis.set_major_formatter(xfmt)\n ax[0].legend(loc='upper right')\n ax[1].plot(datetimes,df['k40_peak'],label='K-40')\n ax[1].set_ylabel('Peak Channel No.')\n ax[1].xaxis.set_major_formatter(xfmt)\n ax[1].grid(alpha=0.5)\n ax[1].legend(loc='upper right')\n ax[2].plot(datetimes,df['th232_peak'],label='Th-232')\n ax[2].set_ylabel('Peak Channel No.')\n ax[2].xaxis.set_major_formatter(xfmt)\n ax[2].grid(alpha=0.5);\n ax[2].legend(loc='upper right')\n ax[2].set_xlabel('Date (Month/Day Hour:Minute)')\n plt.setp( ax[1].xaxis.get_majorticklabels(), rotation=25 )\n plt.subplots_adjust(hspace=0.)\n return fig\n\nif __name__ == '__main__':\n inPath = '/Users/i6o/Downloads/'\n dbFiles = [x for x in os.listdir(inPath) if '.csv' in x]\n fig = gainstab_csv(inPath,dbFiles[0])\n plt.show()","sub_path":"MINOS/python/readGainstabCSV.py","file_name":"readGainstabCSV.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"269360076","text":"#!/usr/bin/env python3\n\nfrom autopa_modules import platesolve\nfrom autopa_modules import indi\nfrom autopa_modules import LX200\nimport argparse\nimport subprocess\n\ndef adjustAltAz(result, serialport, backlashCorrection):\n #Verify error correction values can be handled by AutoPA hardware (assuming it is in home/centered position)\n moveAz = \"N\"\n if abs(result[0]) > 120:\n moveAz = input(\"Azimuth error may be out of bounds of hardware capabilities if not in home position. Continue? (Y/N): \") \n else:\n moveAz = \"Y\"\n if moveAz.upper() == \"Y\":\n if result[0] < 0:\n correction = result[0] - backlashCorrection[0]\n reply = LX200.sendCommand(f\":MAL{correction}#\", serialport)\n\n moveAlt = \"N\"\n if result[1] > 168:\n moveAz = input(\"Altitude error may be out of bounds of hardware capabilities if not in home position. Continue? (Y/N): \")\n elif result[1] > 432:\n moveAz = input(\"Altitude error may be out of bounds of hardware capabilities if not in home position. Continue? (Y/N): \")\n else:\n moveAlt = \"Y\"\n if moveAlt.upper() == \"Y\":\n if result[1] < 0:\n correction = result[1] - backlashCorrection[1]\n reply = LX200.sendCommand(f\":MAZ{correction}#\", serialport)\n return\n\nparser = argparse.ArgumentParser(usage='%(prog)s [mylat] [mylong] [myelev] [time]', description='OpenAstroTracker AutoPA Manual Calculation: \\\n This tool will output the altitude/azimuth difference between the current alignment and polar alignment.\\\n The input RA/DEC values are determined by manually capturing three photos 30 degrees apart on the RA axis and plate solving them.')\nparser.add_argument(\"mylat\", help=\"Your latitude in degrees\", type=float)\nparser.add_argument(\"mylong\", help=\"Your longitude in degrees\", type=float)\nparser.add_argument(\"myelev\", help=\"Your elevation in metres\", type=float)\nparser.add_argument(\"time\", help=\"The time the images were captured (In YYYY-MM-DD HH:MM:SS UTC format.\", type=str)\nparser.add_argument(\"p1RA\", help=\"First point RA (in degrees)\", type=float)\nparser.add_argument(\"p1DEC\", help=\"First point DEC (in degrees)\", type=float)\nparser.add_argument(\"p2RA\", help=\"Second point RA (in degrees)\", type=float)\nparser.add_argument(\"p2DEC\", help=\"Second point DEC (in degrees)\", type=float)\nparser.add_argument(\"p3RA\", help=\"Third point RA (in degrees)\", type=float)\nparser.add_argument(\"p3DEC\", help=\"Third point DEC (in degrees)\", type=float)\nparser.add_argument(\"--serialport\", help=\"Serial port address for the OAT (default is /dev/ttyACM0)\", type=str)\nparser.add_argument(\"--telescope\", help=\"Name of INDI telescope (default assumes INDI is not running)\", type=str)\nargs = parser.parse_args()\nmylat = args.mylat\nmylong = args.mylong\nmyelev = args.myelev\ntime = args.time\np1RA = args.p1RA\np1DEC = args.p1DEC\np2RA = args.p2RA\np2DEC = args.p2DEC\np3RA = args.p3RA\np3DEC = args.p3DEC\nif args.serialport:\n serialport = args.serialport\nelse:\n\tserialport = \"/dev/ttyACM0\"\nif args.telescope:\n telescope = args.telescope\nelse:\n\ttelescope = \"\"\n\n#Calculate polar alignment error\nresult = platesolve.polarCalc(mylat, mylong, myelev, time, p1RA, p1DEC, p2RA, p2DEC, p3RA, p3DEC)\nprint(f\"Azimuth error correction is: {result[0]:.4f} arcminutes.\")\nprint(f\"Altitude error correction is: {result[1]:.4f} arcminutes.\")\n\nprint(\"Note: Stepper backlash has not been accounted for and may cause up to 4 arcminutes and 0.2 arcminutes error in azimuth and altitude respectively.\")\nif input(\"Adjust the OAT altitude/azimuth motors? (Y/N): \").upper() != \"Y\":\n exit()\n\n#Backlash correction in arcminutes (approximately 16 full steps of each axis)\nbacklashCorrection = (0,0)\n\nif telescope != \"\":\n #Connect to indi server\n indiclient, blobEvent = indi.indiserverConnect()\n\n #Disconnect telescope mount from INDI to free up serial port for Alt/Az adjustments\n print (f\"Disconnecting {telescope} from INDI server\")\n indi.disconnectScope(indiclient, telescope)\n print (\"Disconnected.\")\n\ntry:\n #Adjust alt/az axis\n print (\"Adjusting altitude/azimuth axes.\")\n platesolve.adjustAltAz(result, serialport, backlashCorrection)\nexcept Exception as err:\n print (err)\n print (\"Error accessing serial port. If INDI is running, please specify the telescope name to disconnect INDI from the device.\")\n\nif telescope != \"\":\n #Re-connect telescope mount to INDI before disconnecting from the INDI server\n indi.connectScope(indiclient, telescope)\n\n #Disconnect from indi server\n indi.indiserverDisconnect(indiclient)","sub_path":"AutoPA/Software/polaralign_manual_calc.py","file_name":"polaralign_manual_calc.py","file_ext":"py","file_size_in_byte":4562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"31348959","text":"#\r\n# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)\r\n# See AUTHORS.txt\r\n#\r\n# This Source Code Form is subject to the terms of the Apache License, version 2.0.\r\n# If a copy of the Apache License, version 2.0 was not distributed with this file,\r\n# you can obtain one at http://www.apache.org/licenses/LICENSE-2.0.\r\n# SPDX-License-Identifier: Apache-2.0\r\n#\r\n# This file is part of [R#SPACE], [IEC61850 Digital Contronl System testing.\r\n#\r\n\r\n##\r\n# \\b TraceLevel: Defines 4 levels of traces\r\n#\r\n# Defines 4 levels of traces from 0: NO_TRACE, DETAIL, GENERAL and ERROR.\r\n# The initialisation of the Trace System give the value of the level activated.\r\n# The call to the trace function inserted in the code, are preset to a certain level (most often \"DETAIL\").\r\n# @param NOTRACE No trace are emitted.\r\n# @param DETAIL Detailed traces are emitted.\r\n# @param GENERAL On the general traces are emitted (usual mode)\r\n# @param ERROR Only the ERROR traces are emitted.\r\nclass Level:\r\n ## NOTRACE No trace are emitted.\r\n NOTRACE = 0\r\n ## DETAIL Detailed traces are emitted\r\n DETAIL = 1\r\n ## GENERAL On the general traces are emitted (usual mode)\r\n GENERAL = 2\r\n ## ERROR Only the ERROR traces are emitted.\r\n ERROR = 3\r\n\r\n##\r\n# \\b Trace: class to handle trace to the console and/or to file\r\n# @brief\r\n# This class defines a set of methods to trace information to the console or to file, according to a Level.\r\n# This is mainly for debugging purpose.\r\n# \r\nclass Trace:\r\n ##\r\n # \\b Console: output traces to the Python console\r\n # @brief\r\n # Tracing to the Python Console.\r\n # \r\n class Console:\r\n ## \\b Description\r\n #\r\n # Constructor for Console\r\n #\r\n # @param _Level : Session Level value\r\n # @var Level : Keep the level of Trace\r\n # @var FileId : File id once opened\r\n def __init__(self, _Level): ## Constructor for Console\r\n self.Level = _Level ## Level : Keep the level of Trace\r\n self.FileId = None ## FileId : File id once opened\r\n ##\r\n # Closing the channel\r\n def TraceClose(self):\r\n self.FileId.close()\r\n\r\n ##\r\n # Actual trace output according to the activated level.\r\n def Trace(self, msg, msgLevel ):\r\n if self.Level == 0:\r\n return\r\n if msgLevel >= self.Level:\r\n print(msg)\r\n return\r\n\r\n ## \\b Description\r\n # \\b TraceFile: output traces to a file\r\n # @brief\r\n # Tracing to a defined file.\r\n #\r\n class File:\r\n ##\r\n # Constructor for file based traces.\r\n #\r\n # @param _Level : Session Level value\r\n # @param _FileName : File to be used as an output\r\n # @var Level : Keep the level of Trace\r\n # @var FileId : ID of the trace file\r\n def __init__(self, _Level, _FileName): ## Constructor for file logging\r\n self.Level = _Level ## Level : Keep the level of Trace\r\n self.FileId = None ## FileId : File id once opened\r\n \r\n if _FileName != '' and _FileName is not None:\r\n self.FileName =_FileName\r\n self.FileId = open(_FileName, \"w\")\r\n ##\r\n # Closing the file\r\n def Close(self):\r\n self.FileId.close()\r\n \r\n ##\r\n # Actual trace output according to the activated level.\r\n def Trace(self, msg, msgLevel ):\r\n if msgLevel == 0:\r\n return\r\n if msgLevel >= self.Level:\r\n if self.FileId is not None:\r\n txt = msg\r\n self.FileId.write(txt)\r\n return\r\n##\r\n# \\b IEC_Trace: unitary test for Tracing\r\nif __name__ == '__main__':\r\n\r\n TR = Trace.File(Trace.Level.DETAIL,\"toto.txt\")\r\n TR.Trace(\"XXXXXXXXX\",Trace.Level.DETAIL)\r\n TR.Trace.Close()\r\n\r\n TR = Trace.File(Trace.Level.DETAIL,\"SCL_files/tata.txt\")\r\n TR.Trace(\"XXXXXXXXX1\\n\",Trace.Level.DETAIL)\r\n TR.Trace(\"XXXXXXXXX2\\n\",Trace.Level.DETAIL)\r\n TR.Trace(\"XXXXXXXXX3\\n\",Trace.Level.DETAIL)\r\n TR.Trace(\"XXXXXXXXX4\\n\",Trace.Level.DETAIL)\r\n TR.Trace.Close()\r\n\r\n\r\n TRX = Trace.Console(Trace.Level.DETAIL)\r\n print(\"DETAIL\")\r\n TRX.Trace((\"0 TEST Level Detail trace NO TRACE\"),Trace.Level.NOTRACE)\r\n TRX.Trace((\"1 TEST Level Detail trace DETAIL \"),Trace.Level.DETAIL)\r\n TRX.Trace((\"2 TEST Level Detail trace GENERAL\"),Trace.Level.GENERAL)\r\n TRX.Trace((\"3 TEST Level Detail trace ERROR \"),Trace.Level.ERROR)\r\n\r\n TRX = Trace.Console(Trace.Level.GENERAL)\r\n print(\"GENERAL\")\r\n TRX.Trace((\"0 TEST Level Detail trace NO TRACE\"),Trace.Level.NOTRACE)\r\n TRX.Trace((\"1 TEST Level GENERAL trace DETAIL \"),Trace.Level.DETAIL)\r\n TRX.Trace((\"2 TEST Level GENERAL trace GENERAL\"),Trace.Level.GENERAL)\r\n TRX.Trace((\"3 TEST Level GENERAL trace ERROR \"),Trace.Level.ERROR)\r\n\r\n TRX = Trace.Console(Trace.Level.ERROR)\r\n print(\"ERROR\")\r\n TRX.Trace((\"0 TEST Level Detail trace NO TRACE\"),Trace.Level.NOTRACE)\r\n TRX.Trace((\"1 TEST Level ERROR trace DETAIL \"),Trace.Level.DETAIL)\r\n TRX.Trace((\"2 TEST Level ERROR trace GENERAL\"),Trace.Level.GENERAL)\r\n TRX.Trace((\"3 TEST Level ERROR trace ERROR \"),Trace.Level.ERROR)\r\n\r\n TRX = Trace.Console(Trace.Level.NOTRACE)\r\n print(\"NOTRACE\")\r\n TRX.Trace((\"0 TEST Level Detail trace NO TRACE\"),Trace.Level.NOTRACE)\r\n TRX.Trace((\"1 TEST Level ALARME trace DETAIL \"),Trace.Level.DETAIL)\r\n TRX.Trace((\"2 TEST Level ALARME trace GENERAL\"),Trace.Level.GENERAL)\r\n TRX.Trace((\"3 TEST Level ALARME trace ERROR \"),Trace.Level.ERROR)\r\n","sub_path":"IEC_Trace.py","file_name":"IEC_Trace.py","file_ext":"py","file_size_in_byte":5698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"182666098","text":"# Copyright 2019 the ProGraML authors.\n#\n# Contact Chris Cummins .\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\"\"\"A gated graph neural network classifier.\"\"\"\nimport typing\nfrom typing import Callable\nfrom typing import Iterable\nfrom typing import List\nfrom typing import NamedTuple\nfrom typing import Optional\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom deeplearning.ml4pl.graphs.labelled import graph_batcher\nfrom deeplearning.ml4pl.graphs.labelled import graph_database_reader\nfrom deeplearning.ml4pl.graphs.labelled import graph_tuple\nfrom deeplearning.ml4pl.graphs.labelled import graph_tuple_database\nfrom deeplearning.ml4pl.graphs.unlabelled.llvm2graph import node_encoder\nfrom deeplearning.ml4pl.models import batch as batches\nfrom deeplearning.ml4pl.models import classifier_base\nfrom deeplearning.ml4pl.models import epoch\nfrom deeplearning.ml4pl.models import run\nfrom deeplearning.ml4pl.models.ggnn.pygeom_ggnn_config import GGNNConfig\nfrom deeplearning.ml4pl.models.ggnn.pygeom_ggnn_modules import GGNNModel\nfrom labm8.py import app\nfrom labm8.py import progress\n\nFLAGS = app.FLAGS\n\napp.DEFINE_float(\n \"label_conv_threshold\",\n 0.995,\n \"convergence interval: fraction of labels that need to be stable\",\n)\napp.DEFINE_integer(\n \"label_conv_stable_steps\",\n 1,\n \"required number of consecutive steps within the convergence interval\",\n)\n\napp.DEFINE_boolean(\n \"cuda\", True, \"Use cuda if available? CPU-only mode otherwise.\"\n)\n\napp.DEFINE_list(\n \"layer_timesteps\",\n [\"2\", \"2\", \"2\"],\n \"A list of layers, and the number of steps for each layer.\",\n)\napp.DEFINE_float(\"learning_rate\", 0.001, \"The initial learning rate.\")\n\n\napp.DEFINE_float(\"clamp_gradient_norm\", 6.0, \"Clip gradients to L-2 norm.\")\n\n\napp.DEFINE_integer(\"hidden_size\", 200, \"The size of hidden layer(s).\")\napp.DEFINE_string(\n \"inst2vec_embeddings\",\n \"random\",\n \"The type of per-node inst2vec embeddings to use. One of {zero, constant, random, random_const, finetune}\",\n)\napp.DEFINE_string(\n \"unroll_strategy\",\n \"none\",\n \"The unroll strategy to use. One of: \"\n \"{none, constant, edge_count, data_flow_max_steps, label_convergence} \"\n \"constant: Unroll by a constant number of steps. The total number of steps is \"\n \"defined in FLAGS.test_layer_timesteps\",\n)\n\napp.DEFINE_list(\n \"test_layer_timesteps\",\n [\"0\"],\n \"Set when unroll_strategy is 'constant'. Assumes that the length <= len(layer_timesteps).\"\n \"Unrolls the GGNN proper for a fixed number of timesteps during eval().\",\n)\n\napp.DEFINE_boolean(\n \"limit_max_data_flow_steps_during_training\",\n True,\n \"If set, limit the size of dataflow-annotated graphs used to train and \"\n \"validate models to only those with data_flow_steps <= sum(layer_timesteps). \"\n \"This has no effect for graph databases with no dataflow annotations, or \"\n \"for testing epochs.\",\n)\n# We assume that position_embeddings exist in every dataset.\n# the flag now only controls whether they are used or not.\n# This could be nice for ablating our model and also debugging with and without.\n\napp.DEFINE_boolean(\n \"position_embeddings\",\n True,\n \"Whether to use position embeddings as signals for edge order.\"\n \"We expect them to be part of the ds anyway, but you can toggle off their effect.\",\n)\n\napp.DEFINE_boolean(\"use_edge_bias\", True, \"\")\n\napp.DEFINE_boolean(\n \"msg_mean_aggregation\",\n True,\n \"If true, normalize incoming messages by the number of incoming messages.\",\n)\napp.DEFINE_float(\n \"graph_state_dropout\", 0.0, \"Graph state dropout rate.\",\n)\napp.DEFINE_float(\n \"edge_weight_dropout\", 0.0, \"Edge weight dropout rate.\",\n)\napp.DEFINE_float(\n \"output_layer_dropout\", 0.0, \"Dropout rate on the output layer.\",\n)\napp.DEFINE_float(\n \"intermediate_loss_weight\",\n 0.2,\n \"The actual loss is computed as loss + factor * intermediate_loss\",\n)\napp.DEFINE_integer(\n \"aux_in_layer_size\",\n 32,\n \"Size for MLP that combines graph_features and aux_in features\",\n)\napp.DEFINE_boolean(\n \"log1p_graph_x\",\n True,\n \"If set, apply a log(x + 1) transformation to incoming graph-level features.\",\n)\n\n####### DEBBUGING HELPERS ##########################\nDEBUG = False\n\n\ndef assert_no_nan(tensor_list):\n for i, t in enumerate(tensor_list):\n assert not torch.isnan(t).any(), f\"{i}: {tensor_list}\"\n\n\ndef nan_hook(self, inp, output):\n \"\"\"Checks return values of any forward() function for NaN\"\"\"\n if not isinstance(output, tuple):\n outputs = [output]\n else:\n outputs = output\n\n for i, out in enumerate(outputs):\n nan_mask = torch.isnan(out)\n if nan_mask.any():\n print(\"In\", self.__class__.__name__)\n raise RuntimeError(\n f\"Found NAN in output {i} at indices: \",\n nan_mask.nonzero(),\n \"where:\",\n out[nan_mask.nonzero()[:, 0].unique(sorted=True)],\n )\n\n\n##########################################\n\n\nclass GgnnBatchData(NamedTuple):\n \"\"\"The model-specific data generated for a batch.\"\"\"\n\n # A combination of one or more graphs into a single disconnected graph.\n disjoint_graph: graph_tuple.GraphTuple\n # A list of graphs that were used to construct the disjoint graph.\n graphs: List[graph_tuple_database.GraphTuple]\n\n\nclass Ggnn(classifier_base.ClassifierBase):\n \"\"\"A gated graph neural network.\"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"Constructor.\"\"\"\n super(Ggnn, self).__init__(*args, **kwargs)\n\n # set some global config values\n self.dev = (\n torch.device(\"cuda\")\n if torch.cuda.is_available() and FLAGS.cuda\n else torch.device(\"cpu\")\n )\n app.Log(\n 1, \"Using device %s with dtype %s\", self.dev, torch.get_default_dtype()\n )\n\n # Instantiate model\n config = GGNNConfig(\n num_classes=self.y_dimensionality,\n has_graph_labels=self.graph_db.graph_y_dimensionality > 0,\n )\n\n inst2vec_embeddings = node_encoder.GraphNodeEncoder().embeddings_tables[0]\n inst2vec_embeddings = torch.from_numpy(\n np.array(inst2vec_embeddings, dtype=np.float32)\n )\n self.model = GGNNModel(\n config,\n pretrained_embeddings=inst2vec_embeddings,\n test_only=FLAGS.test_only,\n )\n\n if DEBUG:\n for submodule in self.model.modules():\n submodule.register_forward_hook(nan_hook)\n\n self.model.to(self.dev)\n\n def MakeBatch(\n self,\n epoch_type: epoch.Type,\n graphs: Iterable[graph_tuple_database.GraphTuple],\n ctx: progress.ProgressContext = progress.NullContext,\n ) -> batches.Data:\n \"\"\"Create a mini-batch of data from an iterator of graphs.\n\n Returns:\n A single batch of data for feeding into RunBatch(). A batch consists of a\n list of graph IDs and a model-defined blob of data. If the list of graph\n IDs is empty, the batch is discarded and not fed into RunBatch().\n \"\"\"\n # TODO(github.com/ChrisCummins/ProGraML/issues/24): The new graph batcher\n # implementation is not well suited for reading the graph IDs, hence this\n # somewhat clumsy iterator wrapper. A neater approach would be to create\n # a graph batcher which returns a list of graphs in the batch.\n class GraphIterator(object):\n \"\"\"A wrapper around a graph iterator which records graph IDs.\"\"\"\n\n def __init__(self, graphs: Iterable[graph_tuple_database.GraphTuple]):\n self.input_graphs = graphs\n self.graphs_read: List[graph_tuple_database.GraphTuple] = []\n\n def __iter__(self):\n return self\n\n def __next__(self):\n graph: graph_tuple_database.GraphTuple = next(self.input_graphs)\n self.graphs_read.append(graph)\n return graph.tuple\n\n graph_iterator = GraphIterator(graphs)\n\n # Create a disjoint graph out of one or more input graphs.\n batcher = graph_batcher.GraphBatcher.CreateFromFlags(\n graph_iterator, ctx=ctx\n )\n\n try:\n disjoint_graph = next(batcher)\n except StopIteration:\n # We have run out of graphs.\n return batches.EndOfBatches()\n\n # Workaround for the fact that graph batcher may read one more graph than\n # actually gets included in the batch.\n if batcher.last_graph:\n graphs = graph_iterator.graphs_read[:-1]\n else:\n graphs = graph_iterator.graphs_read\n\n # Discard single-graph batches during training when there are graph\n # features. This is because we use batch normalization on incoming features,\n # and batch normalization requires > 1 items to normalize.\n if (\n len(graphs) <= 1\n and epoch_type == epoch.Type.TRAIN\n and disjoint_graph.graph_x_dimensionality\n ):\n return batches.EmptyBatch()\n\n return batches.Data(\n graph_ids=[graph.id for graph in graphs],\n data=GgnnBatchData(disjoint_graph=disjoint_graph, graphs=graphs),\n )\n\n def GraphReader(\n self,\n epoch_type: epoch.Type,\n graph_db: graph_tuple_database.Database,\n filters: Optional[List[Callable[[], bool]]] = None,\n limit: Optional[int] = None,\n ctx: progress.ProgressContext = progress.NullContext,\n ) -> graph_database_reader.BufferedGraphReader:\n \"\"\"Construct a buffered graph reader.\n\n Args:\n epoch_type: The type of graph reader to return a graph reader for.\n graph_db: The graph database to read graphs from.\n filters: A list of filters to impose on the graph database reader.\n limit: The maximum number of rows to read.\n ctx: A logging context.\n\n Returns:\n A buffered graph reader instance.\n \"\"\"\n filters = filters or []\n\n # Only read graphs with data_flow_steps <= message_passing_step_count if\n # --limit_max_data_flow_steps_during_training is set and we are not\n # in a test epoch.\n if (\n FLAGS.limit_max_data_flow_steps_during_training\n and self.graph_db.has_data_flow\n and (epoch_type == epoch.Type.TRAIN or epoch_type == epoch.Type.VAL)\n ):\n filters.append(\n lambda: graph_tuple_database.GraphTuple.data_flow_steps\n <= self.message_passing_step_count\n )\n\n # If we are batching my maximum node count and skipping graphs that are\n # larger than this, we can apply that filter to the SQL query now, rather\n # than reading the graphs and ignoring them later. This ensures that when\n # --max_{train,val}_per_epoch is set, the number of graphs that get used\n # matches the limit.\n if (\n FLAGS.graph_batch_node_count\n and FLAGS.max_node_count_limit_handler == \"skip\"\n ):\n filters.append(\n lambda: (\n graph_tuple_database.GraphTuple.node_count\n <= FLAGS.graph_batch_node_count\n )\n )\n\n return super(Ggnn, self).GraphReader(\n epoch_type=epoch_type,\n graph_db=graph_db,\n filters=filters,\n limit=limit,\n ctx=ctx,\n )\n\n @property\n def message_passing_step_count(self) -> int:\n return self.layer_timesteps.sum()\n\n @property\n def layer_timesteps(self) -> np.array:\n return np.array([int(x) for x in FLAGS.layer_timesteps])\n\n def get_unroll_steps(\n self, epoch_type: epoch.Type, batch: batches.Data, unroll_strategy: str,\n ) -> int:\n \"\"\"Determine the unroll factor from the --unroll_strategy flag, and the batch log.\"\"\"\n # Determine the unrolling strategy.\n if unroll_strategy == \"none\":\n # Perform no unrolling. The inputs are processed according to layer_timesteps\n return 0\n elif unroll_strategy == \"constant\":\n # Unroll by a constant number of steps according to test_layer_timesteps\n return 0\n elif unroll_strategy == \"data_flow_max_steps\":\n max_data_flow_steps = max(\n graph.data_flow_steps for graph in batch.data.graphs\n )\n app.Log(3, \"Determined max data flow steps to be %d\", max_data_flow_steps)\n return max_data_flow_steps\n elif unroll_strategy == \"edge_count\":\n max_edge_count = max(graph.edge_count for graph in batch.data.graphs)\n app.Log(3, \"Determined max edge count to be %d\", max_edge_count)\n return max_edge_count\n elif unroll_strategy == \"label_convergence\":\n return 0\n else:\n raise app.UsageError(f\"Unknown unroll strategy '{unroll_strategy}'\")\n\n def RunBatch(\n self,\n epoch_type: epoch.Type,\n batch,\n ctx: progress.ProgressContext = progress.NullContext,\n ) -> batches.Results:\n\n\n with ctx.Profile(5, \"Sent data to GPU\"):\n vocab_ids = batch.x.to(\n self.dev, torch.long\n )\n # since we have no\n selector_ids = torch.zeros_like(batch.x).to(\n self.dev, torch.long\n )\n # we need those as a result on cpu and can save device i/o\n cpu_labels = (\n batch.y\n #if disjoint_graph.has_node_y\n #else disjoint_graph.graph_y\n )\n labels = batch.y.to(self.dev)\n edge_lists = [\n batch.edge_index[:, batch.batch == i].to(self.dev, torch.long)\n for i in range(batch.batch.max(dim=0).item())\n ]\n\n edge_positions = [\n torch.zeros_like(edge_lists[i]).to(self.dev, torch.long)\n for i in range(batch.batch.max(dim=0).item())\n ]\n\n model_inputs = (vocab_ids, selector_ids, labels, edge_lists, edge_positions)\n\n\n assert epoch_type != epoch.Type.TRAIN or batch.batch.max(dim=0).item() > 0, f\"graph_count is {batch.batch.max(dim=0).item() + 1}\"\n\n num_graphs = torch.tensor(batch.batch.max(dim=0).item() + 1).to(\n self.dev, torch.long\n )\n graph_nodes_list = batch.batch.to(self.dev, torch.long)\n\n #aux_in = torch.from_numpy(disjoint_graph.graph_x).to(\n # self.dev, torch.get_default_dtype()\n #)\n model_inputs = model_inputs + (num_graphs, graph_nodes_list, None,)\n\n # maybe calculate manual timesteps\n if epoch_type != epoch.Type.TRAIN and FLAGS.unroll_strategy in [\n \"constant\",\n \"edge_count\",\n \"data_flow_max_steps\",\n \"label_convergence\",\n ]:\n time_steps_cpu = np.array(\n self.get_unroll_steps(epoch_type, batch, FLAGS.unroll_strategy),\n dtype=np.int64,\n )\n time_steps_gpu = torch.from_numpy(time_steps_cpu).to(self.dev)\n else:\n time_steps_cpu = 0\n time_steps_gpu = None\n\n # RUN MODEL FORWARD PASS\n # enter correct mode of model\n if epoch_type == epoch.Type.TRAIN:\n if not self.model.training:\n self.model.train()\n outputs = self.model(*model_inputs, test_time_steps=time_steps_gpu)\n else: # not TRAIN\n if self.model.training:\n self.model.eval()\n self.model.opt.zero_grad()\n with torch.no_grad(): # don't trace computation graph!\n outputs = self.model(*model_inputs, test_time_steps=time_steps_gpu)\n\n (\n logits,\n accuracy,\n logits,\n correct,\n targets,\n graph_features,\n *unroll_stats,\n ) = outputs\n\n loss = self.model.loss((logits, graph_features), targets)\n\n if epoch_type == epoch.Type.TRAIN:\n loss.backward()\n # TODO(github.com/ChrisCummins/ProGraML/issues/27):: Clip gradients\n # (done). NB, pytorch clips by norm of the gradient of the model, while\n # tf clips by norm of the grad of each tensor separately. Therefore we\n # change default from 1.0 to 6.0.\n # TODO(github.com/ChrisCummins/ProGraML/issues/27):: Anyway: Gradients\n # shouldn't really be clipped if not necessary?\n if self.model.config.clip_grad_norm > 0.0:\n nn.utils.clip_grad_norm_(\n self.model.parameters(), self.model.config.clip_grad_norm\n )\n self.model.opt.step()\n self.model.opt.zero_grad()\n\n # tg = targets.numpy()\n # tg = np.vstack(((tg + 1) % 2, tg)).T\n # assert np.all(labels.numpy() == tg), f\"labels sanity check failed: labels={labels.numpy()}, tg={tg}\"\n\n # TODO(github.com/ChrisCummins/ProGraML/issues/27): Learning rate schedule\n # will change this value.\n learning_rate = self.model.config.lr\n\n model_converged = unroll_stats[1] if unroll_stats else False\n iteration_count = unroll_stats[0] if unroll_stats else time_steps_cpu\n\n loss_value = loss.item()\n assert not np.isnan(loss_value), loss\n return batches.Results.Create(\n targets=cpu_labels,\n predictions=logits.detach().cpu().numpy(),\n model_converged=model_converged,\n learning_rate=learning_rate,\n iteration_count=iteration_count,\n loss=loss_value,\n )\n\n\ndef main():\n \"\"\"Main entry point.\"\"\"\n run.Run(Ggnn)\n\n\nif __name__ == \"__main__\":\n app.Run(main)\n","sub_path":"deeplearning/ml4pl/models/bazel_ggnn/pygeom_bazel_ggnn.py","file_name":"pygeom_bazel_ggnn.py","file_ext":"py","file_size_in_byte":16681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"187771664","text":"\"\"\"\nDavidsonClassifier.py: It trains or tests a model.\n\nUsage:\n DavidsonClassifier.py [options]\n\nOptions:\n --train-path= training file\n --test-path= testing file\n --classifier-name= name of classifier\n --model-path= training model file\n --export-results-path= testing report file\n --prediction-mode= prediction mode\n\"\"\"\n\n#===========================#\n# Imports #\n#===========================#\n\nfrom warnings import filterwarnings\nfilterwarnings(\"ignore\", category=UserWarning)\nfilterwarnings(\"ignore\", category=FutureWarning)\nimport pandas as pd\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport nltk\nfrom nltk.stem.porter import *\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as VS\nfrom textstat.textstat import *\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold, GridSearchCV\nfrom sklearn.pipeline import Pipeline\nimport sys\nfrom docopt import docopt\nimport os\nimport joblib\nimport pickle\n\n#===========================#\n# Variables #\n#===========================#\n\nPREDICTION_MODE_BINARY = 'binary'\nPREDICTION_MODE_PROBABILITY = 'probability'\n\nstopwords = nltk.corpus.stopwords.words(\"english\")\n\nother_exclusions = [\"#ff\", \"ff\", \"rt\"]\nstopwords.extend(other_exclusions)\n\nstemmer = PorterStemmer()\nsentiment_analyzer = VS()\n\n# This is to fix the error: \n# `RecursionError: maximum recursion depth exceeded in comparison`\n# https://github.com/nltk/nltk/issues/1971\nsys.setrecursionlimit(100000)\n\n#===========================#\n# Functions #\n#===========================#\n\ndef get_name_of_train_file():\n \"\"\"\n Returns the name of the train dataset.\n :return: string\n \"\"\"\n return str(str(os.path.basename(args['--model-path'])).split(\n args['--classifier-name'])[1]).split('.model')[0]\n\ndef get_name_of_test_file():\n \"\"\"\n Returns the name of the test dataset.\n :return: string\n \"\"\"\n return str(os.path.basename(args['--test-path'])).split('dataset')[0]\n\ndef write_analytical_results(classifier_name, texts, y_preds):\n \"\"\"\n Creates an analytical report to be used later for the inter-agreement \n calculation of the classifiers.\n :param classifier_name: name of the classifier\n :param texts: test file\n :param y_preds: predictions file\n \"\"\"\n # Get the name of column\n column_name = classifier_name + \\\n get_name_of_train_file() + \\\n get_name_of_test_file()\n \n # Get the name of the export file\n file_name = classifier_name + '/' + \\\n classifier_name + '_' + \\\n get_name_of_train_file() + '_' + \\\n get_name_of_test_file() + \\\n 'analytical_report.csv'\n\n # Convert pd.Series into pd.Dataframe\n texts_df = pd.DataFrame(texts)\n \n # Remove the first item since it was removed from the testing dataset\n texts_df = texts_df.drop(texts_df.index[[0]])\n\n # Convert numpy.darray into pd.Dataframe\n predictions_df = pd.DataFrame(pd.Series(y_preds, name=column_name))\n \n # Fix the index discrepancy\n predictions_df.index += 1\n\n # Concat the dataframes\n final_df = pd.concat([texts_df, predictions_df], axis=1)\n\n # Write dataframe to .csv\n final_df.to_csv(file_name, encoding='utf-8', index=False) \n \n\n print('======> Analytical report saved to file: ' + str(file_name))\n\ndef preprocess(text_string):\n space_pattern = '\\s+'\n giant_url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|'\n '[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')\n mention_regex = '@[\\w\\-]+'\n parsed_text = re.sub(space_pattern, ' ', text_string)\n parsed_text = re.sub(giant_url_regex, '', parsed_text)\n parsed_text = re.sub(mention_regex, '', parsed_text)\n return parsed_text\n\ndef tokenize(tweet):\n tweet = \" \".join(re.split(\"[^a-zA-Z]*\", tweet.lower())).strip()\n tokens = [stemmer.stem(t) for t in tweet.split()]\n return tokens\n\ndef basic_tokenize(tweet):\n tweet = \" \".join(re.split(\"[^a-zA-Z.,!?]*\", tweet.lower())).strip()\n return tweet.split()\n\ndef count_twitter_objs(text_string):\n space_pattern = '\\s+'\n giant_url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|'\n '[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')\n mention_regex = '@[\\w\\-]+'\n hashtag_regex = '#[\\w\\-]+'\n parsed_text = re.sub(space_pattern, ' ', text_string)\n parsed_text = re.sub(giant_url_regex, 'URLHERE', parsed_text)\n parsed_text = re.sub(mention_regex, 'MENTIONHERE', parsed_text)\n parsed_text = re.sub(hashtag_regex, 'HASHTAGHERE', parsed_text)\n return (\n parsed_text.count('URLHERE'), \n parsed_text.count('MENTIONHERE'), \n parsed_text.count('HASHTAGHERE'))\n\ndef other_features(tweet):\n\n sentiment = sentiment_analyzer.polarity_scores(tweet)\n\n words = preprocess(tweet) # Get text only\n\n syllables = textstat.syllable_count(words)\n num_chars = sum(len(w) for w in words)\n num_chars_total = len(tweet)\n num_terms = len(tweet.split())\n num_words = len(words.split())\n avg_syl = round(float((syllables + 0.001)) / float(num_words + 0.001), 4)\n num_unique_terms = len(set(words.split()))\n\n # Modified FK grade, where avg words per sentence is just num words/1\n FKRA = round(float(0.39 * float(num_words) / 1.0) + \\\n float(11.8 * avg_syl) - 15.59, 1)\n \n # Modified FRE score, where sentence fixed to 1\n FRE = round(206.835 - 1.015 * (float(num_words) / 1.0) - \\\n (84.6 * float(avg_syl)), 2)\n\n twitter_objs = count_twitter_objs(tweet)\n retweet = 0\n if \"rt\" in words:\n retweet = 1\n \n features = [\n FKRA, FRE, syllables, avg_syl, num_chars, num_chars_total, num_terms, \n num_words, num_unique_terms, sentiment['neg'], sentiment['pos'], \n sentiment['neu'], sentiment['compound'], twitter_objs[2], \n twitter_objs[1], twitter_objs[0], retweet]\n \n # features = pandas.DataFrame(features)\n \n return features\n\ndef get_feature_array(tweets):\n feats = []\n for t in tweets:\n feats.append(other_features(t))\n return np.array(feats)\n\ndef train(args):\n \"\"\"\n Trains the classifier.\n\n :param args: Parsing arguments.\n \"\"\"\n\n # Read the train dataset\n train_df = pd.read_csv(args['--train-path'], sep='\\t')\n\n # Check if the trained model exists\n if not os.path.isfile(args['--model-path']):\n\n # Get the texts\n texts = train_df.text\n\n vectorizer = TfidfVectorizer(\n tokenizer=tokenize,\n preprocessor=preprocess,\n ngram_range=(1, 3),\n stop_words=stopwords,\n use_idf=True,\n smooth_idf=False,\n norm=None,\n decode_error='replace',\n max_features=10000,\n min_df=5,\n max_df=0.75\n )\n\n pos_vectorizer = TfidfVectorizer(\n tokenizer=None,\n lowercase=False,\n preprocessor=None,\n ngram_range=(1, 3),\n stop_words=None,\n use_idf=False,\n smooth_idf=False,\n norm=None,\n decode_error='replace',\n max_features=5000,\n min_df=5,\n max_df=0.75,\n )\n\n # Construct tfidf matrix and get relevant scores\n tfidf = vectorizer.fit_transform(texts).toarray()\n\n vocab = {v: i for i, v in enumerate(vectorizer.get_feature_names())}\n idf_vals = vectorizer.idf_\n\n # keys are indices; values are IDF scores\n idf_dict = {i: idf_vals[i] for i in vocab.values()} \n\n # Get POS tags for tweets and save as a string\n tweet_tags = []\n for t in texts:\n tokens = basic_tokenize(preprocess(t))\n tags = nltk.pos_tag(tokens)\n tag_list = [x[1] for x in tags]\n tag_str = \" \".join(tag_list)\n tweet_tags.append(tag_str)\n\n # Construct POS TF matrix and get vocab dict\n pos = pos_vectorizer.fit_transform(pd.Series(tweet_tags)).toarray()\n\n pos_vocab = \\\n {v: i for i, v in enumerate(pos_vectorizer.get_feature_names())}\n\n # print(\"Generating features...\")\n feats = get_feature_array(texts)\n\n # Now join them all up\n M = np.concatenate([tfidf, pos, feats], axis=1)\n\n # Finally get a list of variable names\n variables = [''] * len(vocab)\n for k, v in vocab.items():\n variables[v] = k\n\n pos_variables = [''] * len(pos_vocab)\n for k, v in pos_vocab.items():\n pos_variables[v] = k\n\n # Save the vectorizers\n joblib.dump(\n vectorizer, \n str(args['--model-path'])[:-6] + '_vectorizer.bin', compress=9)\n \n joblib.dump(\n pos_vectorizer, \n str(args['--model-path'])[:-6] + '_posvectorizer.bin', compress=9)\n\n X = pd.DataFrame(M)\n\n # Get the labels\n y = train_df['label'].astype(int)\n\n # Get the training labels (test_size: 1 item)\n X_train, _, y_train, _ = train_test_split(\n X, y, test_size=1, random_state=42, shuffle=False)\n\n # Call Machine-Learning-Chan to do his thing.\n pipe = Pipeline([\n ('select', SelectFromModel(LogisticRegression(\n class_weight='balanced', penalty=\"l2\", C=0.01))),\n ('model', LogisticRegression(\n class_weight='balanced', penalty='l2'))])\n \n param_grid = [{}] # Optionally add parameters here\n grid_search = GridSearchCV(\n pipe,\n param_grid,\n cv=StratifiedKFold(\n n_splits=5, \n random_state=42).split(X_train, y_train),\n verbose=2)\n\n # Fit the model\n model = grid_search.fit(X_train, y_train)\n\n # Save the model.\n # Notice how only the best estimator can be saved.\n # This is because: https://stackoverflow.com/a/50705399/873309\n joblib.dump(model.best_estimator_, args['--model-path'], compress=9)\n\n print('======> Model saved.')\n\n else:\n print('======> Trained model already exists.')\n\ndef test(args):\n \"\"\"\n Tests the classifier.\n\n :param args: Parsing arguments.\n \"\"\"\n\n # Read the train dataset\n test_df = pd.read_csv(args['--test-path'])\n\n # Get the texts\n texts = test_df.text\n\n # Load the saved model\n loaded_model = joblib.load(args['--model-path'])\n \n loaded_vectorizer = joblib.load(\n str(args['--model-path'])[:-6] + '_vectorizer.bin')\n \n loaded_posvectorizer = joblib.load(\n str(args['--model-path'])[:-6] + '_posvectorizer.bin')\n\n # Construct tfidf matrix and get relevant scores\n tfidf = loaded_vectorizer.transform(texts).toarray()\n\n # Get POS tags for tweets and save as a string\n tweet_tags = []\n for t in texts:\n tokens = basic_tokenize(preprocess(t))\n tags = nltk.pos_tag(tokens)\n tag_list = [x[1] for x in tags]\n tag_str = \" \".join(tag_list)\n tweet_tags.append(tag_str)\n\n # Construct POS TF matrix and get vocab dict\n pos = loaded_posvectorizer.transform(pd.Series(tweet_tags)).toarray()\n\n # print(\"Generating features...\")\n feats = get_feature_array(texts)\n\n # Now join them all up\n M = np.concatenate([tfidf, pos, feats], axis=1)\n\n X = pd.DataFrame(M)\n\n # Predict\n prediction_mode = args['--prediction-mode']\n \n if prediction_mode == PREDICTION_MODE_BINARY:\n y_preds = loaded_model.predict(X)\n \n elif prediction_mode == PREDICTION_MODE_PROBABILITY:\n y_preds = loaded_model.predict_proba(X)[:,1]\n\n # Concat the results and save the file\n results_df = pd.concat([test_df, pd.DataFrame(y_preds)], axis=1)\n results_df.columns = ['id', 'text', 'label']\n results_df.to_csv(args['--export-results-path'], index=False)\n\n#===========================#\n# Main #\n#===========================#\n\nif __name__ == \"__main__\":\n\n args = docopt(__doc__)\n\n # print('\\nArguments: ')\n # print(args)\n\n if args['--test-path']:\n test(args)\n else:\n train(args)\n\n#===========================#\n# End of Script #\n#===========================#","sub_path":"classifiers/davidson/davidson_classifier.py","file_name":"davidson_classifier.py","file_ext":"py","file_size_in_byte":12570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"445960384","text":"from numpy import *\nfrom PIL import Image\nfrom noise import pnoise3\nimport socket\nimport time\nimport threading\nimport websockets\nimport asyncio\n\nfrom neopixel import *\n\nimport argparse\nimport signal\nimport sys\ndef signal_handler(signal, frame):\n colorWipe(strip, Color(0,0,0))\n sys.exit(0)\ndef opt_parse():\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', action='store_true', help='clear the display on exit')\n args = parser.parse_args()\n if args.c:\n signal.signal(signal.SIGINT, signal_handler)\n\n# LED strip configuration:\nLED_COUNT = 200 # Number of LED pixels.\nLED_PIN = 18 # GPIO pin connected to the pixels (18 uses PWM!).\n#LED_PIN = 10 # GPIO pin connected to the pixels (10 uses SPI /dev/spidev0.0).\nLED_FREQ_HZ = 800000 # LED signal frequency in hertz (usually 800khz)\nLED_DMA = 10 # DMA channel to use for generating signal (try 10)\nLED_BRIGHTNESS = 255 # Set to 0 for darkest and 255 for brightest\nLED_INVERT = False # True to invert the signal (when using NPN transistor level shift)\nLED_CHANNEL = 0 # set to '1' for GPIOs 13, 19, 41, 45 or 53\nLED_STRIP = ws.WS2811_STRIP_GRB # Strip type and colour ordering\n\nh = 5 # height of pixel matrix\nw = int(200/h) # width of pixel matrix\nmag = 5 # magnification/scale of perlin field\noctaves = 2\ntiming = 0.005\nmin_bright = 0\nmax_bright = 255\nx_drift = 1000\ny_drift = 200\nhost = '10.0.0.41'\n#host = '127.0.0.1'\nport = 5555\n\nred_bright, blue_bright, green_bright = [x for x in [max_bright]*3]\niCommand = []\n\ns = socket.socket()\ns.bind((host, port))\ns.listen(1)\n\ndef listener():\n while True:\n c, addr = s.accept()\n print(\"Connection from; \"+str(addr))\n data = c.recv(1024).decode('utf-8')\n iCommand.append(data)\n print(data)\n\n if not data:\n #print(\"Data applied\")\n break\n c.close()\n\ndef interp(val, smin=0.0, smax=100.0, tmin=0.0, tmax=1.0):\n return((((abs(val)-smin)*(tmax-tmin))/(smax-smin))+tmin)\n\ndef reset_strip():\n for i in range(LED_COUNT):\n strip.setPixelColor(i, Color(0,0,0))\n\ndef build_matrix(count, iComm):\n global y_drift, red_bright, blue_bright, green_bright\n global iCommand # set this to clear the iCommand list after it has been used\n if len(iComm) > 0:\n if iComm[0] == 'r':\n green_bright = 0\n red_bright= 255;\n blue_bright = 0\n elif iComm[0] == 'g':\n red_bright = 0\n green_bright = 255\n blue_bright= 0\n elif iComm[0] == 'b':\n red_bright = 0\n blue_bright = 255\n green_bright = 0\n else:\n try:\n y_drift = int(iComm[0])\n except:\n print(\"did not recognize command\")\n iCommand = []\n span = w*h\n img_rgb_matrix = [[]]*span\n for i in range(h):\n for j in range(w):\n led_index = (w*h)-1 - int(i*w+j)\n y_dir, x_dir = i*mag+1+(count*y_drift), j*mag+1+(count*x_drift)\n blueColor = int(interp(pnoise3(\n float(y_dir)/span,\n float(x_dir)/span,\n float(count),\n octaves=octaves),\n 0, 1.0, min_bright, blue_bright))\n\n redColor = int(interp(pnoise3(\n float(y_dir+100)/span,\n float(x_dir+100)/span,\n float(count), octaves=octaves),\n 0, 1.0, min_bright, red_bright))\n\n greenColor = int(interp(pnoise3(\n float(y_dir+200)/span,\n float(x_dir+200)/span,\n float(count), octaves=octaves),\n 0, 1.0, min_bright, green_bright))\n\n strip.setPixelColor(led_index,\n Color(redColor, blueColor, greenColor))\n strip.show()\n\ndef display_img(strip):\n count = 0\n t = threading.Thread(target=listener)\n t.start()\n\n while 1:\n\n # #print(\"STARTING\")\n #\tdata = c.recv(1024).decode('utf-8')\n #\tif not data:\n #\t\tbreak\n #\t#print(\"From connected user: \" + data)\n #\tdata = data.upper()\n #\t#print(\"Sending: \"+data)\n #\tc.send(data.encode('utf-8'))\n build_matrix(count, iCommand)\n count += timing\n #reset_strip()\n c.close()\n\n# Main program logic follows:\nif __name__ == '__main__':\n # Process arguments\n opt_parse()\n # Create NeoPixel object with appropriate configuration.\n strip = Adafruit_NeoPixel(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL, LED_STRIP)\n # Intialize the library (must be called once before other functions).\n\n\n strip.begin()\n display_img(strip)\n\n","sub_path":"Perlin/perlin_port_2d.py","file_name":"perlin_port_2d.py","file_ext":"py","file_size_in_byte":4898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"466430941","text":"# Surprisingly there are only three numbers that can\n# be written as the sum of fourth powers of their digits:\n#\n# 1634 = 1^4 + 6^4 + 3^4 + 4^4\n# 8208 = 8^4 + 2^4 + 0^4 + 8^4\n# 9474 = 9^4 + 4^4 + 7^4 + 4^4\n# As 1 = 14 is not a sum it is not included.\n#\n# The sum of these numbers is 1634 + 8208 + 9474 = 19316.\n#\n# Find the sum of all the numbers that can be written as\n# the sum of fifth powers of their digits.\n\nfrom time import time\nimport sys\nsys.path.append(\"../Library\")\nfrom peresult import peresult\n\ndef solve():\n result = 0\n digit_powers = [x ** 5 for x in range(10)]\n for x in range(10, 6 * (9 ** 5)):\n trimmed = x\n power_digit_sum = 0\n while trimmed > 0:\n power_digit_sum += digit_powers[trimmed % 10]\n trimmed //= 10\n if power_digit_sum == x:\n result += x\n return result\n\nif __name__ == \"__main__\":\n start = time()\n peresult(30, solve(), time() - start)\n","sub_path":"Problems 1-100/p030_DigitFifthPowers.py","file_name":"p030_DigitFifthPowers.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359853698","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\nos.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers \\\n import Conv2D, MaxPool2D, Dense, Activation, Dropout, Flatten, BatchNormalization, ReLU, Reshape, Softmax\nfrom tensorflow.keras.optimizers import Adam, SGD\nfrom optimizer import Eve, RAdam\nfrom image_loader import load_veg, _load_img\nimport pickle\nfrom argparse import ArgumentParser\n# from drawer import Drawer\nfrom pprint import pprint\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n\nEPS = 1e-8\nN_CLASS = 7\n\ndef create_model():\n model = Sequential([\n Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(100,100,3)),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n MaxPool2D(pool_size=2, strides=2, padding='same'),\n Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n MaxPool2D(pool_size=2, strides=2, padding='same'),\n Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n MaxPool2D(pool_size=2, strides=2, padding='same'),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n MaxPool2D(pool_size=2, strides=2, padding='same'),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n MaxPool2D(pool_size=2, strides=2, padding='same'),\n Reshape(target_shape=(4 * 4 * 512,)),\n Dense(units=4096),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Dense(units=4096),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Dense(units=N_CLASS),\n BatchNormalization(epsilon=EPS),\n ReLU(),\n Dropout(0.5),\n Softmax()\n ])\n\n # model.summary()\n\n return model\n\ndef data_augmentation(img):\n im = tf.image.random_flip_left_right(img) # horizontal flip\n im = tf.image.random_flip_up_down(im) # vertical flip\n im = tf.pad(im, tf.constant([[2, 2], [2, 2], [0, 0]]), \"REFLECT\") # pad 2 (outsize:104x104)\n im = tf.image.random_crop(im, size=[100, 100, 3]) # random crop (outsize:100x100)\n return im\n\ndef _train(batch_size, epochs, init_epoch, save_file, load_file, optimizer, lr, experiment, use_tpu, use_callbacks, verbose):\n if use_tpu:\n print(\"Setting up TPU ...\")\n tpu_grpc_url = \"grpc://\" + os.environ[\"COLAB_TPU_ADDR\"]\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)\n tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)\n tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)\n strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)\n print(\"Done!\")\n\n (x_train, y_train), (x_val, y_val), (x_test, y_test) = load_veg(flatten=False)\n\n # cut data\n if experiment:\n x_train, y_train = x_train[:600], y_train[:600]\n x_val, y_val = x_val[:100], y_val[:100]\n x_test, y_test = x_test[:100], y_test[:100]\n\n train_num = len(x_train)\n test_num = len(x_test)\n\n trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)\n valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))\n valset = valset.shuffle(buffer_size=1024).batch(test_num)\n testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n testset = testset.batch(test_num)\n\n callbacks = []\n if use_callbacks:\n checkpoint_path = \"./checkpoint/cp-{epoch:04d}-{val_loss:.4f}-{val_accuracy:.4f}.h5\"\n callbacks.append(tf.keras.callbacks.ModelCheckpoint(checkpoint_path, verbose=1, save_best_only=True, save_weights_only=True, save_freq='epoch'))\n callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=80))\n\n ops = {'SGD':SGD, 'Adam':Adam, 'Eve':Eve, 'RAdam':RAdam}\n op = ops[optimizer](lr=lr, epsilon=EPS)\n\n # TPU\n if use_tpu:\n with strategy.scope():\n model = create_model()\n\n model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\n if load_file is not None: model.load_weights(load_file)\n\n history = model.fit(\n trainset, epochs=epochs, initial_epoch=init_epoch,\n validation_data=valset, callbacks=callbacks, verbose=verbose,\n steps_per_epoch=train_num // batch_size\n )\n res_loss, res_acc = model.evaluate(testset)\n # CPU\n else:\n model = create_model()\n\n model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\n if load_file is not None: model.load_weights(load_file)\n\n history = model.fit(\n trainset, epochs=epochs, initial_epoch=init_epoch,\n validation_data=valset, callbacks=callbacks, verbose=verbose,\n steps_per_epoch=train_num // batch_size\n )\n\n res_loss, res_acc = model.evaluate(testset)\n\n with open(save_file, 'wb') as f:\n pickle.dump(history.history, f)\n print(f\"Saving history to \\'{save_file}\\'. (epochs: {len(history.epoch)})\")\n\n return (res_loss, res_acc)\n\ndef _evaluate(batch_size, load_file, optimizer, lr, experiment, use_tpu):\n if use_tpu:\n print(\"Setting up TPU ...\")\n tpu_grpc_url = \"grpc://\" + os.environ[\"COLAB_TPU_ADDR\"]\n tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)\n tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)\n tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)\n strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)\n print(\"Done!\")\n\n (_, _), (_, _), (x_test, y_test) = load_veg(flatten=False)\n\n # cut data\n if experiment:\n x_test, y_test = x_test[:100], y_test[:100]\n\n test_num = len(x_test)\n testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n testset = testset.batch(test_num)\n\n # ops = {'SGD':SGD, 'Adam':Adam, 'Eve':Eve, 'RAdam':RAdam}\n # op = ops[optimizer](lr=lr, epsilon=EPS)\n\n # TPU\n if use_tpu:\n with strategy.scope():\n model = create_model()\n # model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n model.load_weights(load_file)\n\n model.evaluate(testset)\n # CPU\n else:\n model = create_model()\n # model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n model.load_weights(load_file)\n\n model.evaluate(testset)\n\ndef _expect(img, load_file, optimizer, lr, verbose=True):\n # クラス (7個)\n category = ['bell pepper', 'broccoli', 'carrot', 'eggplant', 'green onion', 'onion', 'tomato']\n # expect\n x = _load_img(img, flatten=False)\n model = create_model()\n # ops = {'SGD':SGD, 'Adam':Adam, 'Eve':Eve, 'RAdam':RAdam}\n # op = ops[optimizer](lr=lr, epsilon=EPS)\n # model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n model.load_weights(load_file)\n\n y = model.predict(x)\n y = y.flatten()\n\n # create dictionary\n dic = dict(zip(category, y))\n # print result\n if verbose:\n for key, value in dic.items():\n print(f\"{key}: {value:.2%}\")\n\n return dic\n\ndef plot_cmx(y_true, y_pred, labels):\n cmx_data = confusion_matrix(y_true, y_pred)\n df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n fig = plt.figure(figsize=(10,7))\n fig.subplots_adjust(left=0.18, right=1.00, bottom=0.25, top=0.92)\n sns.heatmap(df_cmx, annot=True, fmt='.0f', linecolor='white', linewidths=1)\n plt.xlabel(\"Predict\")\n plt.ylabel(\"True\")\n\n plt.show()\n\ndef _report(load_file, optimizer, lr):\n (_, _), (_, _), (x_test, y_test) = load_veg(flatten=False)\n # ラベル (7個)\n labels = [\n 'Green pepper', 'Broccoli', 'Carrot', 'Eggplant',\n 'Green onion', 'Onion', 'Tomato'\n ]\n\n # model = tf.keras.models.load_model('mycnn2.h5', compile=False)\n model.summary()\n # ops = {'SGD':SGD, 'Adam':Adam, 'Eve':Eve, 'RAdam':RAdam}\n # op = ops[optimizer](lr=lr, epsilon=EPS)\n # model.compile(optimizer=op, loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n model.load_weights(load_file)\n\n y_pred = model.predict_classes(x_test, batch_size=128)\n pprint(y_pred)\n print(classification_report(y_test, y_pred, target_names=labels))\n # plot_cmx(y_test, y_pred, labels)\n # tf.keras.models.save_model(model, 'mycnn2.h5', include_optimizer=False)\n\nif __name__ == '__main__':\n argparser = ArgumentParser()\n\n argparser.add_argument('-b', '--batch', type=int, default=128, help='The size of batch.')\n argparser.add_argument('-e', '--epochs', type=int, default=100, help='The number of epochs.')\n argparser.add_argument('-i', '--initepoch', type=int, default=0, help='The epoch at which to start training. (0-indexed)')\n argparser.add_argument('-l', '--load', type=str, default=None, help='The path of weights file to load.')\n argparser.add_argument('-s', '--save', type=str, default='./history/mycnn_history.pkl', help='The path of history file to save.')\n argparser.add_argument('-o', '--optimizer', type=str, default='Adam', choices=['SGD', 'Adam', 'Eve', 'RAdam'], \n help='The optimizer. Choices: SGD, Adam, Eve, RAdam')\n argparser.add_argument('-r', '--lr', type=float, default='0.001', help='The learning rate.')\n argparser.add_argument('-x', '--experiment', action='store_true', help='Whether to run system experimentally.')\n argparser.add_argument('-t', '--tpu', action='store_true', help='Whether to run system with TPU. (CPU/GPU by default)')\n argparser.add_argument('-m', '--mode', type=str, default='train', choices=['train', 'expect', 'report', 'eval', 'search'],\n help='Whether to train or expect.')\n argparser.add_argument('-g', '--img', type=str, default=None, help='The path of img to expect. You should specify the path when expectation mode.')\n argparser.add_argument('-n', '--lr_range', type=float, nargs='*', default=[0.0001, 0.01], help='The range of learning rate for searching')\n argparser.add_argument('-c', '--use_callbacks', action='store_true', help='Whether to use callbacks. Default by \\'True\\'')\n argparser.add_argument('-v', '--verbose', action='store_true', help='Whether to verbose. ')\n args = argparser.parse_args()\n\n if args.mode == 'train':\n print(f\"---------- lr = {args.lr} ----------\")\n v = 1 if args.verbose else 0\n _train(\n args.batch, args.epochs, args.initepoch, args.save, args.load,\n args.optimizer, args.lr, args.experiment, args.tpu, args.use_callbacks, v\n )\n elif args.mode == 'eval':\n if args.load is None:\n print(\"Error: you should specify the path of image and weights file.\")\n else:\n _evaluate(args.batch, args.load, args.optimizer, args.lr, args.experiment, args.tpu)\n elif args.mode == 'expect':\n if args.img is None or args.load is None:\n print(\"Error: you should specify the path of image and weights file.\")\n else:\n result = _expect(args.img, args.load, args.optimizer, args.lr)\n elif args.mode == 'report':\n if args.load is None:\n print(\"Error: you should specify the path of image and weights file.\")\n else:\n _report(args.load, args.optimizer, args.lr)\n elif args.mode == 'search':\n name, ext = os.path.splitext(args.save)\n lr_range = np.arange(*args.lr_range, dtype=float).tolist()\n for lr in lr_range:\n print(f\"---------- lr = {lr} ----------\")\n v = 1 if args.verbose else 2\n loss, acc = _train(\n args.batch, args.epochs, args.initepoch, f\"{name}_{lr:.4f}{ext}\", args.load, \n args.optimizer, lr, args.experiment, args.tpu, args.use_callbacks, v\n )","sub_path":"mycnn2.py","file_name":"mycnn2.py","file_ext":"py","file_size_in_byte":12550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"47984157","text":"from back_test.model.base_option_set import BaseOptionSet\nfrom data_access.get_data import get_50option_mktdata\nimport back_test.model.constant as c\nfrom PricingLibrary.EngineQuantlib import QlBlackFormula,QlBinomial\nfrom PricingLibrary.BlackFormular import BlackFormula\nfrom PricingLibrary.BinomialModel import BinomialTree\nfrom Utilities.PlotUtil import PlotUtil\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport datetime\nimport math\n\ndef fun_htb_rate(df_series,rf):\n # r = math.log(df_series[c.Util.AMT_APPLICABLE_STRIKE]/\n # (df_series[c.Util.AMT_UNDERLYING_CLOSE]+df_series[c.Util.AMT_PUT_QUOTE]\n # -df_series[c.Util.AMT_CALL_QUOTE]),math.e)/df_series[c.Util.AMT_TTM]\n r = -math.log((df_series[c.Util.AMT_CALL_QUOTE]-df_series[c.Util.AMT_PUT_QUOTE]\n +df_series[c.Util.AMT_APPLICABLE_STRIKE]*math.exp(-rf*df_series[c.Util.AMT_TTM]))\n /df_series[c.Util.AMT_UNDERLYING_CLOSE])/df_series[c.Util.AMT_TTM]\n return r\n\ndef fun_iv(df_series:pd.DataFrame, option_type:c.OptionType,rf:float=0.03):\n K = df_series[c.Util.AMT_APPLICABLE_STRIKE]\n S = df_series[c.Util.AMT_UNDERLYING_CLOSE]\n dt_eval = df_series[c.Util.DT_DATE]\n dt_maturity = df_series[c.Util.DT_MATURITY]\n if option_type == c.OptionType.CALL:\n black_call = QlBlackFormula(dt_eval,dt_maturity,c.OptionType.CALL,S,K,rf=rf)\n C = df_series[c.Util.AMT_CALL_QUOTE]\n iv = black_call.estimate_vol(C)\n else:\n black_put = QlBlackFormula(dt_eval,dt_maturity,c.OptionType.PUT,S,K,rf=rf)\n P = df_series[c.Util.AMT_PUT_QUOTE]\n iv = black_put.estimate_vol(P)\n return iv\n\ndef fun_pcp_adjusted_iv(df_series:pd.DataFrame, option_type:c.OptionType,rf:float,htb_r:float=None):\n if htb_r is None:\n htb_r = df_series[c.Util.AMT_HTB_RATE]\n ttm = df_series[c.Util.AMT_TTM]\n K = df_series[c.Util.AMT_APPLICABLE_STRIKE]\n S = df_series[c.Util.AMT_UNDERLYING_CLOSE]*math.exp(-htb_r*ttm)\n dt_eval = df_series[c.Util.DT_DATE]\n dt_maturity = df_series[c.Util.DT_MATURITY]\n if option_type == c.OptionType.CALL:\n C = df_series[c.Util.AMT_CALL_QUOTE]\n black_call = QlBlackFormula(dt_eval,dt_maturity,c.OptionType.CALL,S,K,rf=rf)\n # black_call = BlackFormula(dt_eval,dt_maturity,c.OptionType.CALL,S,K,C,rf=rf)\n # black_call = QlBinomial(dt_eval,dt_maturity,c.OptionType.CALL,c.OptionExerciseType.EUROPEAN,S,K,0.2,rf=rf)\n # iv = black_call.ImpliedVolApproximation()\n iv = black_call.estimate_vol(C)\n else:\n P = df_series[c.Util.AMT_PUT_QUOTE]\n black_put = QlBlackFormula(dt_eval,dt_maturity,c.OptionType.PUT,S,K,rf=rf)\n # black_put = BlackFormula(dt_eval,dt_maturity,c.OptionType.PUT,S,K,P,rf=rf)\n # black_put = QlBinomial(dt_eval,dt_maturity,c.OptionType.PUT,c.OptionExerciseType.EUROPEAN,S,K,0.2,rf=rf)\n # iv = black_put.ImpliedVolApproximation()\n iv = black_put.estimate_vol(P)\n return iv\n\n\n\nstart_date = datetime.date(2018, 8, 20)\nend_date = datetime.date(2018, 9, 8)\nrf = 0.03\ndf_metrics = get_50option_mktdata(start_date, end_date)\npu = PlotUtil()\n\noptionset = BaseOptionSet(df_metrics)\noptionset.init()\nnbr_maturity = 1\nmdt1 = optionset.get_maturities_list()[nbr_maturity]\n\nhtb_r = optionset.get_htb_rate(nbr_maturity)\n# TODO: OTM OPTION INPLIED VOL CURVE\nfor option in optionset.get_dict_options_by_maturities()[mdt1]:\n iv = option.get_implied_vol_adjusted_by_htbr(htb_r)\n delta = option.get_delta(iv)\n print(optionset.eval_date,option.id_instrument(),option.underlying_close(),iv,delta)\n# optionset.next()\n# for option in optionset.get_dict_options_by_maturities()[mdt1]:\n# iv = option.get_implied_vol_adjusted_by_htbr(htb_r)\n# delta = option.get_delta(iv)\n# print(optionset.eval_date,option.id_instrument(),option.underlying_close(),iv,delta)\nt_qupte = optionset.get_T_quotes(nbr_maturity)\n\nt_qupte[c.Util.AMT_HTB_RATE] = t_qupte.apply(lambda x: fun_htb_rate(x,rf),axis=1)\n\nhtb_r_vw = (t_qupte.loc[:,c.Util.AMT_HTB_RATE]*t_qupte.loc[:,c.Util.AMT_TRADING_VOLUME]).sum()/\\\n t_qupte.loc[:,c.Util.AMT_TRADING_VOLUME].sum()\n# htb_r_vw = t_qupte.loc[:,c.Util.AMT_HTB_RATE].mean()\nmin_k_series = t_qupte.loc[t_qupte[c.Util.AMT_APPLICABLE_STRIKE].idxmin()]\nhtb_r_mp = fun_htb_rate(min_k_series,rf)\nt_qupte.loc[:,'diff'] = abs(t_qupte.loc[:,c.Util.AMT_APPLICABLE_STRIKE]-t_qupte.loc[:,c.Util.AMT_UNDERLYING_CLOSE])\natm_series = t_qupte.loc[t_qupte['diff'].idxmin()]\nhtb_r_atm = fun_htb_rate(atm_series,rf)\n# htb_r_vw = optionset.get_implied_rf_vwpcr(nbr_maturity)\n\nt_qupte['amt_iv_adj_call'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.CALL,rf,htb_r=0.388),axis=1)\nt_qupte['amt_iv_adj_put'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.PUT,rf,htb_r=0.388),axis=1)\nt_qupte['amt_iv_adj_call_mk'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.CALL,rf,htb_r=htb_r_mp),axis=1)\nt_qupte['amt_iv_adj_put_mk'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.PUT,rf,htb_r=htb_r_mp),axis=1)\nt_qupte['amt_iv_adj_call_vw'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.CALL,rf,htb_r=htb_r_vw),axis=1)\nt_qupte['amt_iv_adj_put_vw'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.PUT,rf,htb_r=htb_r_vw),axis=1)\nt_qupte['amt_iv_adj_call_pk'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.CALL,rf),axis=1)\nt_qupte['amt_iv_adj_put_pk'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.PUT,rf),axis=1)\nt_qupte['amt_iv_adj_call_atm'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.CALL,rf,htb_r=htb_r_atm),axis=1)\nt_qupte['amt_iv_adj_put_atm'] = t_qupte.apply(lambda x: fun_pcp_adjusted_iv(x,c.OptionType.PUT,rf,htb_r=htb_r_atm),axis=1)\nt_qupte['amt_iv_call'] = t_qupte.apply(lambda x: fun_iv(x,c.OptionType.CALL),axis=1)\nt_qupte['amt_iv_put'] = t_qupte.apply(lambda x: fun_iv(x,c.OptionType.PUT),axis=1)\n# t_qupte['amt_iv_put_adjusted_rf'] = t_qupte.apply(lambda x: fun_calculate_iv(x,c.OptionType.PUT),axis=1)\n\nprint(t_qupte)\n\n\nprint('htb_r_vw : ',htb_r_vw)\nprint('htb_r_mp : ',htb_r_mp)\nprint('htb_r_atm : ',htb_r_atm)\n\n\ndf_otm_iv = optionset.get_otm_implied_vol_curve(nbr_maturity)\n\nKs = df_otm_iv[c.Util.AMT_APPLICABLE_STRIKE]\notm_vols = df_otm_iv[c.Util.PCT_IV_OTM_BY_HTBR]\nprint(df_otm_iv)\npu.plot_line_chart(Ks,[otm_vols],['otm_ivs'])\n\nk = list(t_qupte['amt_strike'])\niv_call = list(t_qupte['amt_iv_call'])\niv_put = list(t_qupte['amt_iv_put'])\niv_adj_call = list(t_qupte['amt_iv_adj_call'])\niv_adj_put = list(t_qupte['amt_iv_adj_put'])\niv_adj_call_vw = list(t_qupte['amt_iv_adj_call_vw'])\niv_adj_put_vw = list(t_qupte['amt_iv_adj_put_vw'])\niv_adj_call_pk = list(t_qupte['amt_iv_adj_call_pk'])\niv_adj_put_pk = list(t_qupte['amt_iv_adj_put_pk'])\niv_adj_call_mk = list(t_qupte['amt_iv_adj_call_mk'])\niv_adj_put_mk = list(t_qupte['amt_iv_adj_put_mk'])\niv_adj_call_atm = list(t_qupte['amt_iv_adj_call_atm'])\niv_adj_put_atm = list(t_qupte['amt_iv_adj_put_atm'])\nimplied_ivs = list(t_qupte[c.Util.AMT_HTB_RATE])\n# plt.figure()\npu.plot_line_chart(k,[iv_call,iv_put],['iv_call','iv_put'])\npu.plot_line_chart(k,[iv_adj_call_atm,iv_adj_put_atm],['iv_adj_call_atm','iv_adj_put_atm'])\npu.plot_line_chart(k,[iv_adj_call_vw,iv_adj_put_vw],['iv_adj_call_vw','iv_adj_put_vw'])\npu.plot_line_chart(k,[implied_ivs],['implied_ivs'])\n\nplt.show()","sub_path":"OptionStrategyLib/OptionStrategy/put_call_parity/put_call_parity-analysis-v1.py","file_name":"put_call_parity-analysis-v1.py","file_ext":"py","file_size_in_byte":7364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"374314381","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.utils import all_estimators\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nimport warnings\n\nimport pdb\n\ndef extract_title(df):\n\n Title_Dictionary = {\n \"Capt\": 0, \"Col\": 0, \"Major\": 0, \"Dr\": 0, \"Rev\": 0,\n \"Jonkheer\": 1, \"Don\": 1, \"Sir\" : 1, \"the Countess\": 1, \"Lady\" : 1,\n \"Mme\": 2, \"Ms\": 2, \"Mrs\" : 2, \n \"Mlle\": 3, \"Miss\" : 3,\n \"Mr\" : 4,\n \"Master\" : 5, \"\": 5,\n }\n return df['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip()).map(Title_Dictionary)\n\n\ndef complement_age(df):\n\n # 年齢の欠損値を補完\n a1 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 0)].fillna(51.0)\n a2 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 1)].fillna(40.0)\n a3 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 2)].fillna(36.0)\n a4 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 3)].fillna(36.0)\n a5 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 4)].fillna(40.0)\n a6 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'male') & (df['Title'] == 5)].fillna(4.0)\n\n a7 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 0)].fillna(46.5)\n a8 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 1)].fillna(36.0)\n a9 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 2)].fillna(34.0)\n a10 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 3)].fillna(34.0)\n a11 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 4)].fillna(31.0)\n a12 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'male') & (df['Title'] == 5)].fillna(1.0)\n\n a13 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 0)].fillna(22.0)\n a14 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 1)].fillna(22.0)\n a15 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 2)].fillna(22.0)\n a16 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 3)].fillna(22.0)\n a17 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 4)].fillna(26.0)\n a18 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'male') & (df['Title'] == 5)].fillna(4.0)\n\n a19 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 0)].fillna(49.0)\n a20 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 1)].fillna(40.5)\n a21 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 2)].fillna(40.0)\n a22 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 3)].fillna(30.0)\n a23 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 4)].fillna(35.0)\n a24 = df['Age'][(df['Pclass'] == 1) & (df['Sex'] == 'female') & (df['Title'] == 5)].fillna(35.0)\n\n a25 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 0)].fillna(24.0)\n a26 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 1)].fillna(24.0)\n a27 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 2)].fillna(31.5)\n a28 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 3)].fillna(24.0)\n a29 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 4)].fillna(24.0)\n a30 = df['Age'][(df['Pclass'] == 2) & (df['Sex'] == 'female') & (df['Title'] == 5)].fillna(24.0)\n\n a31 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 0)].fillna(18.0)\n a32 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 1)].fillna(18.0)\n a33 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 2)].fillna(31.0)\n a34 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 3)].fillna(18.0)\n a35 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 4)].fillna(18.0)\n a36 = df['Age'][(df['Pclass'] == 3) & (df['Sex'] == 'female') & (df['Title'] == 5)].fillna(18.0)\n\n return np.sum(pd.DataFrame([a1, a2, a3, a4, a5, a6, a7, a8, a9, a10,\n a11, a12, a13, a14, a15, a16, a17, a18, a19, a20,\n a21, a22, a23, a24, a25, a26, a27, a28, a29, a30,\n a31, a32, a33, a34, a35, a36]).fillna(0))\n\n\ndef build_ticket_df(df):\n\n ticket = df['Ticket'].str.split(' ', expand=True)\n\n ticket1 = ticket[0].to_list()\n ticket2 = ticket[1].to_list()\n ticket3 = ticket[2].to_list()\n\n ticket_num = []\n \n for t1, t2, t3 in zip(ticket1, ticket2, ticket3):\n try:\n ticket_num.append(int(t1))\n except:\n try:\n ticket_num.append(int(t2))\n except:\n try:\n ticket_num.append(int(t3))\n except:\n ticket_num.append(0)\n\n return pd.DataFrame(ticket_num)\n\n\ndef clean_cabin(df):\n \n cabin = df['Cabin'].to_list()\n for i, c in enumerate(cabin):\n if 'A' in str(c):\n cabin[i] = float(1.0)\n elif 'B' in str(c):\n cabin[i] = float(2.0)\n elif 'C' in str(c):\n cabin[i] = float(3.0)\n elif 'D' in str(c):\n cabin[i] = float(4.0)\n elif 'E' in str(c):\n cabin[i] = float(5.0)\n elif 'F' in str(c):\n cabin[i] = float(6.0)\n elif 'G' in str(c):\n cabin[i] = float(7.0)\n elif 'T' in str(c):\n cabin[i] = float(8.0)\n else:\n cabin[i] = float(0.0)\n\n return pd.DataFrame(cabin)\n\n\ndef process_df(df):\n\n # 名前から肩書きを抜き出す\n df['Title'] = extract_title(df)\n\n # 年齢の欠損値を補完する\n df['Age'] = complement_age(df)\n \n # 性別を数値に変換する\n df['Sex'] = df['Sex'].apply(lambda x: 1 if x == 'male' else 0)\n \n # 旅客運賃の欠損値を補完する\n df['Fare'] = df['Fare'].fillna(df['Fare'].median())\n\n # 乗船港の欠損値を補完する\n df['Embarked'] = df['Embarked'].fillna('S')\n df['Embarked'] = df['Embarked'].map( {'S':0, 'C':1, 'Q':2}).astype(int)\n \n # チケット番号を整理する\n df['Ticket'] = build_ticket_df(df)\n\n # 客室番号を整理する\n df['Cabin'] = clean_cabin(df)\n \n return df\n\n\nif __name__ == '__main__':\n\n # 訓練データを読み込み\n df = process_df(pd.read_csv('train.csv'))\n \n # 不要なデータを破棄\n Survived = df['Survived']\n df = df.drop(['PassengerId', 'Survived', 'Name'], axis=1)\n \n # 0〜1の範囲で正規化\n df = (df - df.min()) / (df.max() - df.min())\n \n # 入出力データを生成\n X = df\n Y = Survived\n \n # クロスバリデーション用のオブジェクトをインスタンス化する\n kfold_cv = KFold(n_splits=5, shuffle=False)\n warnings.filterwarnings('ignore')\n\n # classifier のアルゴリズムをすべて取得する\n all_Algorithms = all_estimators(type_filter=\"classifier\")\n warnings.filterwarnings('ignore')\n \n max_clf = None\n max_score = -1\n \n # 各分類アルゴリズムをクロスバリデーションで評価する\n for (name, algorithm) in all_Algorithms:\n try:\n if (name == \"LinearSVC\"):\n clf = algorithm(max_iter = 10000)\n else:\n clf = algorithm()\n \n if hasattr(clf, \"score\"):\n scores = cross_val_score(clf, X, Y, cv=kfold_cv)\n print(name, \"の正解率：\")\n print(scores)\n if max_score < np.mean(scores):\n max_clf = clf\n max_score = np.mean(scores)\n except Exception as e:\n pass\n \n print(max_clf, max_score)\n \n # 平均正解率が最高だったモデルをトレーニング\n max_clf = max_clf.fit(X, Y)\n \n # テストデータを読み込み\n df_test = process_df(pd.read_csv('test.csv'))\n passsengerid = df_test['PassengerId']\n \n # 不要なデータを破棄\n df_test = df_test.drop(['PassengerId', 'Name'], axis=1)\n \n # 0〜1の範囲で正規化\n X_test = (df_test - df_test.min()) / (df_test.max() - df_test.min())\n X_test = X_test.fillna(0)\n \n # 結果を出力\n pred = max_clf.predict(X_test)\n result = [int(i) for i in pred]\n \n submission = pd.DataFrame({'PassengerId':passsengerid, 'Survived':result})\n submission.to_csv('submission.csv', index=False)\n \n","sub_path":"titanic_revised5.py","file_name":"titanic_revised5.py","file_ext":"py","file_size_in_byte":8777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299707138","text":"import socket, threading\n\n\ndef recv_data(server_client_socket):\n while True:\n # 5.收发消息\n recv_data = server_client_socket.recv(1024)\n if recv_data:\n print(\"接受客户端的数据:\", recv_data.decode(\"gbk\"))\n server_client_socket.send(\"你的请求已收到,正在处理中...\".encode(\"gbk\"))\n # 终止对链接客户服务\n else:\n print(\"结束对客户服务\")\n server_client_socket.close()\n break\n\nif __name__ == '__main__':\n # 1.创建socket\n tcp_server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n # 立刻收回端口\n tcp_server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, True)\n\n # \"\",表示绑定本机任一ip地址,6789端口号\n # 2.绑定端口\n tcp_server_socket.bind((\"\", 6789))\n\n # 3.设置响铃模式,监听模式\n # 128:等待服务端接收的最大连接数\n # tcp_sever_socket主动套接字,变成被动,也就是不能等待被接收用户的连接\n tcp_server_socket.listen(128)\n\n while True:\n # 4.等待客户端的链接,来一个客户找一个客服服务这个客户,10086客服\n server_client_socket, ip_port = tcp_server_socket.accept()\n print(server_client_socket)\n\n # 开辟线程\n recv_thread = threading.Thread(target=recv_data, args=(server_client_socket,))\n recv_thread.setDaemon(True)\n recv_thread.start()\n\n\n # 6.关闭socket(可选)\n # 好比机构关门,不接受新连接,已建立链接的客户还要服务\n tcp_server_socket.close()\n\n\n","sub_path":"6day/01.tcp_sever.py","file_name":"01.tcp_sever.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"601028931","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\nimport urllib\nimport smtplib\nimport urllib.request\nfrom email.mime.text import MIMEText\nimport json\n\n\n#定义关键字，可以通过查看返回数据找到所有监控的关键字，可以一个或者多个\nkey_word=['''bookState\":\"0\"''']\n#定义监控页面的url，这里具体为请求域名的具体查询链接，可以根据需要去network里面获取\nurl=\"http://checksolr.xinnet.com/domain/domainBookSearch?callbackparam=jQuery17208028104623956083_1606397646236&domainName=¬DomainName=&deleteDate=&location=contains&domainLengthStart=1&domainLengthEnd=4&domainKindType=LET&domainXSType=&domainSuffix=.com&pageSize=30&pageNo=1&lengthSortDesc=false&_=1606398368116\"\n#快速链接而已，邮件内容一部分，方便快速打卡新网域名列表\nnew_url = \"http://www.xinnet.com/domain/domain_book_search.jsp?f=nva\"\n\n\n#获取网页数据，并且转换为json格式，loads为dict格式，对数据进行解析读取\n\nreq = urllib.request.urlopen(url)\nhtml = req.read()\n# 处理返回数据为utf8\ncode_data = str(html, encoding='utf-8')\n#优化返回数据结构，掐头去尾保留为可解析的json数据\ncode_data1 = \"{\"+(code_data[91:-3])+\"}\"\n# print(code_data1)\n#json数据解析为dict数据\njson_data = json.loads(code_data1)\n# print(type(json_data))\n\n#定义一个空字符串，没读取到一个域名，拼接一个域名内容并换行，解析后为dict字典数据，可以根据键名去依次获取\ndomain_str = ''\nfor i in range(len(json_data[\"list\"])):\n bookstate = json_data[\"list\"][i][\"bookState\"]\n domainname = json_data[\"list\"][i][\"domainName\"]\n if bookstate == \"0\":\n domain_str=domain_str + \"未预定\" +\" \"+domainname+\"\\n\"\n else:\n domain_str = domain_str + \"已预定\" +\" \"+ domainname+\"\\n\"\nreq.close()\n\n\n#定义发送邮件发送方及收件方,注意密码要使用邮箱授权密码才能发送邮件\nmy_pass = '此处为授权密码'\nmail_from ='402151718@qq.com'\nmail_to ='402151718@qq.com'\n\n#定义邮件主题和内容及发送形式\nsubject='有最新的未预定.com域名'\ncontent = \"主人,com域名更新,请火速查看,网址\\n\"+new_url+\"\\n\"+\"详情请看 \\n\"+ domain_str #定义发送内容\nmsg = MIMEText(content, 'plain', 'utf-8') #重构发送信息\nmsg['From'] = mail_from\nmsg['To'] = mail_to\nmsg['Subject'] = subject\n\n\n\n#定义邮件发送函数，随时调用\ndef mail():\n try:\n server = smtplib.SMTP_SSL('smtp.qq.com', smtplib.SMTP_SSL_PORT)\n server.login(mail_from,my_pass)\n server.sendmail(mail_from,mail_to, msg.as_string())\n print('发送邮件成功')\n except Exception as e:\n print('发送邮件异常')\n print(e)\n finally:\n server.quit()\n\n\n#循环查找列表内关键字，可设置多个关键字符\nfor des_date in key_word:\n if des_date in code_data:\n mail()\n else:\n print('暂时无内容更新')\n","sub_path":"xinnet_domain_monitor.py","file_name":"xinnet_domain_monitor.py","file_ext":"py","file_size_in_byte":2927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"343908450","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# © Adapted for Keybase.io and style by Brandon Kalinowski\n# © Original Code by Thelonius Kort - MIT License\n\nimport re\nimport string\nfrom ansible.module_utils.basic import *\nfrom ansible.module_utils.urls import fetch_url\n\nDOCUMENTATION = '''\n---\nmodule: gpg\nversion_added: 2.0\nshort_description: Manages import and removal of GPG-keys\ndescription:\n - Imports, refreshes, and deletes GnuPG keys. Just specify the keybase\n username and either the key email or key fingerprint.\n You can also import keys from files.\noptions:\n keybase_user:\n description:\n - The Username to fetch on Keybase. The module will download\n https://keybase.io//pgp_keys.asc automatically when specified.\n key_id:\n description:\n - The id of the key to be fetched and imported.\n Only applicable to public keys. Either key_file or key_id is required.\n required: false\n default: null\n key_file:\n description:\n - Filename of key to be imported. Must be on remote machine, not local.\n Either key_file or key_id is required.\n Can also be used with delete, module will extract the fingerprint from the provided key file and\n delete the matching key from the key-chain.\n required: false\n default: null\n key_type:\n description:\n - What type of key to import. Only applicable to key_file\n required: true\n choices: [ \"private\", \"public\" ]\n default: \"public\"\n bin_path:\n description:\n - \"Location of GPG binary\"\n require: false\n default: /usr/bin/gpg\n state:\n description:\n - Whether to import (C(present), C(latest)), or remove (C(absent)) a key.\n required: false\n choices: [ \"present\", \"latest\", \"absent\" ]\n default: \"present\"\nnotes: []\nrequirements: [ gpg ]\nauthor: Brandon Kalinowski\n'''\n\nEXAMPLES = '''\n- name: Import GPG key from keybase\n gpg:\n keybase_user: brandonkal\n state: present\n key_id: F33344CEF855F4FE4C2C55820E9D2E07D3D89BDD\n # Key ID can be fingerprint as above or email address\n\n- name: Import Public GPG Key from file\n gpg:\n key_file: publickey.asc\n key_id: you@email.com\n\n- name: Remove GPG Key\n gpg:\n keybase_user: gpgtools\n key_id: team@gpgtools.com\n state: absent\n'''\n\n\nclass SafeDict(dict):\n def __missing__(self, key):\n return '{' + key + '}'\n\n\n# http://stackoverflow.com/a/33621609/659298\nclass SafeFormatter(string.Formatter):\n def __init__(self, default='{{{0}}}'):\n self.default = default\n\n def get_value(self, key, args, kwds):\n if isinstance(key, str):\n return kwds.get(key, self.default.format(key))\n string.Formatter.get_value(key, args, kwds)\n\n\nclass GpgImport(object):\n\n def __init__(self, module):\n self.m = module\n self.debuglist = []\n self._setup_creds()\n self._execute_task()\n\n def _debug(self, msg):\n # named 'debuglist' to avoid 'self.debug()' attempting to work.\n self.debuglist.append(msg)\n\n def trust_all(self):\n self._debug('Checking Trust')\n res = self._execute_command('check-trust')\n self._debug('check trust: %s' % (str(res['stdout'])))\n gpg_output = res['stdout']\n pattern = re.findall('fpr:::::::::([0-9A-F]+):', gpg_output)\n # Create list with all keys marked as trusted:\n trusted = \":6:\\n\".join(pattern) + \":6:\"\n res = self._execute_command('import-trust', data=trusted)\n self._debug('import trust: %s' % (str(res['stdout'])))\n\n def get_keybase(self):\n url = 'https://keybase.io/' + self.m.params[\"keybase_user\"] + '/pgp_keys.asc'\n rsp, info = fetch_url(self.m, url=url, timeout=10, method='GET')\n\n # Check for errors first\n # Exceptions in fetch_url may result in a status -1, ensure error in all cases.\n if info['status'] == -1:\n self.m.fail_json(msg=info['msg'], url=url)\n\n elif info['status'] != 200:\n self.m.fail_json(\n msg=\"Request failed\",\n status_code=info['status'],\n response=info['msg'], url=url\n )\n else:\n # Required for python3\n remote_key = to_native(rsp.read())\n return remote_key\n\n def _execute_task(self):\n key_present = False\n\n if self.keybase_user:\n if self.key_id:\n res = self._execute_command('check')\n self._debug('keybase check: %s' % (str(res)))\n key_present = res['rc'] == 0\n self.changed = False\n else:\n self.m.fail_json(msg='key_id is required when keybase_user is defined!')\n\n if self.key_file:\n filekey = self._get_key_from_file()\n if self.key_type == 'public' and filekey:\n # rerun the original setup with this key in the commands\n self._setup_creds(filekey)\n res = self._execute_command('check-public')\n self._debug('checkpublic: %s' % (str(res)))\n key_present = res['rc'] == 0\n\n elif self.key_type == 'private' and filekey:\n # rerun the original setup with this key in the commands\n self._setup_creds(filekey)\n res = self._execute_command('check-private')\n self._debug('checkprivate: %s' % (str(res)))\n key_present = res['rc'] == 0\n\n if key_present and self.state == 'absent':\n res = self._execute_command('delete')\n self.changed = res['rc'] == 0\n elif key_present and self.state == 'latest':\n res = self._execute_command('keybase', data=self.get_keybase())\n self.changed = re.search('gpg:\\s+unchanged: 1\\n', res['stderr']) is None\n elif not key_present and self.state in ('present', 'latest'):\n if self.key_type == 'private' and self.key_file:\n self._debug('importing private key file')\n res = self._execute_command('import-key')\n elif self.keybase_user:\n self._debug('importing Keybase public keys for ' + self.keybase_user)\n res = self._execute_command('keybase', data=self.get_keybase())\n elif self.key_type == 'public':\n self._debug('importing public key file')\n res = self._execute_command('import-key')\n self.changed = res['rc'] == 0\n else:\n self.changed = False\n res = {'rc': 0}\n\n if res['rc'] != 0:\n self._debug(res)\n self.m.fail_json(msg=self.log_dic, debug=self.debuglist)\n\n # Check if a change has occurred and mark all keys as trusted\n if self.changed and self.state != 'absent':\n self.trust_all()\n\n def _setup_creds(self, key_override=None):\n for k, v in self.m.params.items():\n setattr(self, k, v)\n if key_override:\n self.key_id = key_override\n\n self.commands = {\n 'check': '{bin_path} {check_mode} --list-keys {key_id}',\n 'delete': '{bin_path} {check_mode} --batch --yes --delete-secret-and-public-keys {key_id}',\n 'check-private': '{bin_path} {check_mode} --list-secret-keys {key_id}',\n 'check-public': '{bin_path} {check_mode} --list-public-keys {key_id}',\n 'import-key': '{bin_path} {check_mode} --batch --fast-import {key_file}',\n 'keybase': '{bin_path} {check_mode} --batch --fast-import',\n 'check-trust': '{bin_path} {check_mode} --list-keys --fingerprint --with-colons',\n 'import-trust': '{bin_path} {check_mode} --fast-ownertrust',\n }\n command_data = {\n 'check_mode': '--dry-run' if self.m.check_mode else '',\n 'bin_path': self.m.get_bin_path(self.bin_path, True),\n 'key_id': self.key_id,\n 'key_file': self.key_file\n }\n # sort of a brilliant way of late-binding/double-formatting given here:\n # http://stackoverflow.com/a/17215533/659298\n for c, l in self.commands.items():\n sf = SafeFormatter()\n self.commands[c] = sf.format(l, **command_data)\n self._debug('set up commands: %s' % (str(self.commands)))\n\n def _execute_command(self, cmd, data=''):\n self._debug('command: %s' % (str(self.commands[cmd])))\n if data:\n raw_res = self.m.run_command(self.commands[cmd], data=data)\n else:\n raw_res = self.m.run_command(self.commands[cmd])\n return self._legiblify(cmd, raw_res)\n\n def _legiblify(self, sec, res):\n \"\"\"turn tuple to dict and preserve it for debugging\"\"\"\n if not hasattr(self, 'log_dic'):\n self.log_dic = {}\n rdic = dict([k, res[i]] for i, k in enumerate(('rc', 'stdout', 'stderr')))\n return rdic\n\n def _get_key_from_file(self):\n keycmd = '%s --with-colons --with-fingerprint %s'\n bp = self.m.get_bin_path(self.bin_path, True)\n print(bp, self.key_file)\n keycmd_expanded = keycmd % (bp, self.key_file)\n self.changed = False\n raw_res = self.m.run_command(keycmd_expanded)\n keyinfo = raw_res[1]\n self._debug('keyinfo: %s' % (str(keyinfo)))\n keysearch = re.search(r'fpr:{9}([0-9A-F]{40}):', keyinfo, re.MULTILINE)\n\n if keysearch and keysearch.group(1):\n self._debug('keysearch groups: %s' % (str(keysearch.groups())))\n return keysearch.group(1)\n return None\n\n\ndef main():\n module = AnsibleModule(\n argument_spec=dict(\n keybase_user=dict(type='str'),\n key_id=dict(required=False, type='str'),\n key_type=dict(default='public', choices=['private', 'public']),\n key_file=dict(required=False, type='str'),\n bin_path=dict(default='/usr/bin/gpg', type='str'),\n state=dict(default='present', choices=['latest', 'absent', 'present']),\n ),\n supports_check_mode=True,\n required_one_of=[['keybase_user', 'key_file']],\n )\n\n gkm = GpgImport(module)\n\n result = {\n 'log_dic': gkm.log_dic,\n 'changed': gkm.changed,\n 'debug': gkm.debuglist,\n }\n\n module.exit_json(**result)\n\n\nmain()\n","sub_path":"gpg.py","file_name":"gpg.py","file_ext":"py","file_size_in_byte":10291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"465919925","text":"# -*- coding: utf-8 -*-\n# Copyright (c), 2011, the txYoga authors. See the LICENSE file for details.\n\"\"\"\nTest updating elements in collections.\n\"\"\"\nfrom twisted.trial.unittest import TestCase\nfrom twisted.web import http, http_headers\n\nfrom txyoga.serializers import json\nfrom txyoga.test import collections\n\n\nclass ElementUpdatingTest(collections.UpdatableCollectionMixin, TestCase):\n \"\"\"\n Test the updating of elements.\n \"\"\"\n uselessUpdateBody = {\"color\": \"green\"}\n usefulUpdateBody = {\"maximumOccupancy\": 200}\n\n\n def setUp(self):\n collections.UpdatableCollectionMixin.setUp(self)\n self.addElements()\n self.headers = http_headers.Headers()\n self.body = self.uselessUpdateBody\n\n\n def _test_updateElement(self, expectedStatusCode=http.OK):\n \"\"\"\n Tries to change the color of a bikeshed.\n \"\"\"\n name = self.elementArgs[0][0]\n self.getElement(name)\n expectedContent = self.responseContent\n\n encodedBody = json.dumps(self.body)\n self.updateElement(name, encodedBody, self.headers)\n\n if expectedStatusCode is http.OK:\n # A successful PUT does not have a response body\n self.assertEqual(self.request.code, expectedStatusCode)\n self._checkContentType(None)\n expectedContent[\"color\"] = self.body[\"color\"]\n else:\n # A failed PUT has a response body\n self._checkContentType(\"application/json\")\n self._decodeResponse()\n self._checkBadRequest(expectedStatusCode)\n\n self.getElement(name)\n self.assertEqual(self.request.code, http.OK)\n self._checkContentType(\"application/json\")\n self.assertEqual(self.responseContent, expectedContent)\n\n\n def test_updateElement(self):\n \"\"\"\n Test that updating an element works.\n \"\"\"\n self.headers.setRawHeaders(\"Content-Type\", [\"application/json\"])\n self._test_updateElement()\n\n\n def test_updateElement_missingContentType(self):\n \"\"\"\n Test that trying to update an element when not specifying the content\n type fails.\n \"\"\"\n self._test_updateElement(http.UNSUPPORTED_MEDIA_TYPE)\n\n\n def test_updateElement_badContentType(self):\n \"\"\"\n Test that trying to update an element when specifying a bad content\n type fails.\n \"\"\"\n self.headers.setRawHeaders(\"Content-Type\", [\"ZALGO/ZALGO\"])\n self._test_updateElement(http.UNSUPPORTED_MEDIA_TYPE)\n\n\n def test_updateElement_nonUpdatableAttribute(self):\n \"\"\"\n Tests that updating an attribute which is not allowed to be updated\n responds that that operation is forbidden.\n\n Try to make the bikeshed twice as large, which won't work because that\n would be a useful change.\n \"\"\"\n self.headers.setRawHeaders(\"Content-Type\", [\"application/json\"])\n self.body = self.usefulUpdateBody\n self._test_updateElement(http.FORBIDDEN)\n\n\n def test_updateElement_partiallyUpdatableAttributes(self):\n \"\"\"\n Tests that updates are atomic; when part of an update is not allowed,\n the entire update does not happen.\n\n Try to make the bikeshed twice as large and change its color. Both\n will fail, since the useful operation blocks the entire change.\n \"\"\"\n self.headers.setRawHeaders(\"Content-Type\", [\"application/json\"])\n self.body = dict(self.usefulUpdateBody)\n self.body.update(self.uselessUpdateBody)\n self._test_updateElement(http.FORBIDDEN)\n","sub_path":"txyoga/test/test_updating.py","file_name":"test_updating.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"167841544","text":"#!/usr/bin/env python3\n\nencoded_hex_str = \"1b37373331363f78151b7f2b783431333d78397828372d363c78373e783a393b3736\"\n\nbytes_1 = bytes.fromhex(encoded_hex_str)\nprint(\"bytes_1:{}\".format(bytes_1))\n\n\nfor x in range(256):\n bytes_xored = bytes([a ^ x for a in bytes_1]) \n bytes_xored_str = bytes_xored.hex()\n print(\"key={}\".format(x))\n print(\"bytes_xored_str:{}\".format(bytes_xored_str))\n","sub_path":"basics/single-byte-xor/single-byte-xor_2.py","file_name":"single-byte-xor_2.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"13898739","text":"# -*- coding: utf-8 -*-\r\n'''\r\n Author : Huseyin BIYIK \r\n Year : 2016\r\n License : GPL\r\n\r\n This program is free software: you can redistribute it and/or modify\r\n it under the terms of the GNU General Public License as published by\r\n the Free Software Foundation, either version 3 of the License, or\r\n (at your option) any later version.\r\n\r\n This program is distributed in the hope that it will be useful,\r\n but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n GNU General Public License for more details.\r\n\r\n You should have received a copy of the GNU General Public License\r\n along with this program. If not, see .\r\n'''\r\nimport re\r\n\r\nimport vods\r\nimport htmlement\r\n\r\n\r\nclass dmaxtr(vods.showextension):\r\n title = u\"DMAX TR\"\r\n domain = \"https://www.dmax.com.tr\"\r\n uselinkplayers = False\r\n useaddonplayers = False\r\n\r\n def iterelems(self, xpath, url=None, page=None, tree=None, cat=None):\r\n if not tree:\r\n if not page:\r\n page = self.download(url)\r\n tree = htmlement.fromstring(page)\r\n for elem in tree.iterfind(xpath):\r\n if cat:\r\n catid = elem.get(\"data-serie-category-id\")\r\n if not catid == cat:\r\n continue\r\n img = elem.find(\".//img\")\r\n if img is not None:\r\n img = img.get(\"src\")\r\n else:\r\n img = \"DefaultFolder.png\"\r\n title = elem.find(\".//h3\")\r\n if title is None:\r\n continue\r\n else:\r\n title = title.text\r\n info = {}\r\n art = {\"icon\": img, \"thumb\": img, \"poster\": img}\r\n if xpath.endswith(\"/a\"):\r\n link = elem.get(\"href\")\r\n else:\r\n link = elem.find(\".//a\").get(\"href\")\r\n yield title, link, info, art\r\n\r\n def getcategories(self):\r\n tree = htmlement.fromstring(self.download(self.domain + \"/programlar\"))\r\n for cat in tree.iterfind(\".//ul[@class='category-list category-type']/li\"):\r\n catid = cat.get(\"data-category-id\")\r\n if catid == \"0\":\r\n continue\r\n self.additem(cat.text, catid)\r\n\r\n def getshows(self, cat, keyw=None):\r\n if cat or filter:\r\n xpath = \".//li[@class='content_pool_item_add content_pool_item']\"\r\n url = self.domain + \"/programlar\"\r\n for args in self.iterelems(xpath, url, None, None, cat):\r\n if keyw and not keyw.lower() in args[0].lower():\r\n continue\r\n self.additem(*args)\r\n\r\n def searchshows(self, keyw):\r\n self.getshows(None, keyw)\r\n\r\n def getepisodes(self, show=None, sea=None):\r\n if not show and not sea:\r\n url = self.domain + \"/kesfet\"\r\n xpath = \".//div[@class='promoted-content-item-box content_pool as_container']/a\"\r\n else:\r\n url = self.domain + show + \"/bolumler\"\r\n xpath = \".//div[@class='promoted-content-item content_pool_item_add content_pool_item']\"\r\n for args in self.iterelems(xpath, url):\r\n self.additem(*args)\r\n\r\n def geturls(self, url):\r\n page = self.download(self.domain + url, referer=self.domain)\r\n video = re.search(\"window\\.location = '(.+)'\", page)\r\n if video:\r\n yield video.group(1)\r\n","sub_path":"service.vods.dmax/lib/dmax.py","file_name":"dmax.py","file_ext":"py","file_size_in_byte":3518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"635184548","text":"# Author: Eric Kow\n# License: CeCILL-B (French BSD3-like)\n\n\"\"\"\nPut subcommand help text here\n\"\"\"\n\nfrom __future__ import print_function\nimport copy\nimport sys\n\nfrom educe.annotation import Span\nimport educe.stac\n\nfrom ..annotate import show_diff\nfrom ..args import\\\n add_usual_input_args, add_usual_output_args,\\\n get_output_dir, announce_output_dir\nfrom ..doc import compute_renames, move_portion\nfrom ..output import save_document\n\n\ndef is_target(args):\n \"\"\"\n Corpus filter to pick out the part we want to move to\n \"\"\"\n def is_match(k):\n \"is a target entry\"\n return k.doc == args.doc and k.subdoc == args.target\n return is_match\n\n\ndef is_requested(args):\n \"\"\"\n Corpus filter to pick out the part we want to move from\n \"\"\"\n def is_match(k):\n \"is a source entry\"\n return k.doc == args.doc and k.subdoc == args.subdoc\n return is_match\n\n\ndef read_source_corpus(args):\n \"\"\"\n Read the part of the corpus that we want to move from\n \"\"\"\n reader = educe.stac.Reader(args.corpus)\n src_files = reader.filter(reader.files(), is_requested(args))\n return reader.slurp(src_files)\n\n\ndef read_target_corpus(args):\n \"\"\"\n Read the part of the corpus that we want to move to\n \"\"\"\n reader = educe.stac.Reader(args.corpus)\n tgt_files = reader.filter(reader.files(), is_target(args))\n return reader.slurp(tgt_files)\n\n# ---------------------------------------------------------------------\n# command and options\n# ---------------------------------------------------------------------\n\nNAME = 'move'\n\n\ndef config_argparser(parser):\n \"\"\"\n Subcommand flags.\n\n You should create and pass in the subparser to which the flags\n are to be added.\n \"\"\"\n add_usual_input_args(parser, doc_subdoc_required=True,\n help_suffix='to move from')\n add_usual_output_args(parser)\n parser.add_argument('start', metavar='INT', type=int,\n help='Text span start')\n parser.add_argument('end', metavar='INT', type=int,\n help='Text span end')\n parser.add_argument('target', metavar='SUBDOC')\n parser.set_defaults(func=main)\n\n\ndef main(args):\n \"\"\"\n Subcommand main.\n\n You shouldn't need to call this yourself if you're using\n `config_argparser`\n \"\"\"\n output_dir = get_output_dir(args)\n if args.start != 0:\n sys.exit(\"Sorry, only know how to deal with start=0 at the moment\")\n\n src_corpus = read_source_corpus(args)\n tgt_corpus = read_target_corpus(args)\n\n portion = Span(args.start, args.end)\n\n renames = compute_renames(tgt_corpus, src_corpus)\n for src_k in src_corpus:\n tgt_k = copy.copy(src_k)\n tgt_k.subdoc = args.target\n if tgt_k not in tgt_corpus:\n sys.exit(\"Uh-oh! we don't have %s in the corpus\" % tgt_k)\n else:\n src_doc = src_corpus[src_k]\n tgt_doc = tgt_corpus[tgt_k]\n new_src_doc, new_tgt_doc =\\\n move_portion(renames, src_doc, tgt_doc, portion)\n diffs = [\"======= TO %s ========\" % tgt_k,\n show_diff(tgt_doc, new_tgt_doc),\n \"^------ FROM %s\" % src_k,\n show_diff(src_doc, new_src_doc),\n \"\"]\n print(\"\\n\".join(diffs), file=sys.stderr)\n save_document(output_dir, src_k, new_src_doc)\n save_document(output_dir, tgt_k, new_tgt_doc)\n\n announce_output_dir(output_dir)\n","sub_path":"educe/stac/util/cmd/move.py","file_name":"move.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"796558","text":"from random import randint\r\nnumbers = []\r\nuser_numbers = []\r\ni = 1\r\nj = 0\r\nstrike = 0\r\nball = 0\r\ncount = 0\r\n\r\n# 숫자 뽑음 \r\nwhile len(numbers) < 3:\r\n new_number = randint(0, 9)\r\n\r\n while new_number in numbers:\r\n new_number = randint(0, 9)\r\n numbers.append(new_number)\r\nprint(numbers)\r\nprint(\"0과 9 사이의 서로 다른 세 숫자를 랜덤한 순서로 뽑았습니다. \\n\")\r\n\r\n\r\nwhile strike != 3:\r\n print(\"세 수를 하나씩 차례대로 입력하세요\")\r\n while len(user_numbers) < 3:\r\n num = int(input(\"%d번째 수를 입력하세요.\" % i))\r\n if num in user_numbers:\r\n print(\"중복되는 수 입니다.\")\r\n elif num > 9:\r\n print(\"범위를 벗어난 수 입니다.\")\r\n else:\r\n user_numbers.append(num)\r\n i += 1\r\n\r\n if numbers[j] == user_numbers[j]:\r\n strike += 1 \r\n elif user_numbers[j] in numbers:\r\n ball += 1\r\n j += 1\r\n print(\"%dS %dB \\n\" % (strike, ball))\r\n count += 1\r\n if strike != 3: \r\n user_numbers = []\r\n i = 1\r\n j = 0\r\n strike = 0\r\n ball = 0\r\n \r\n else:\r\n print(\"축하합니다. %d번만에 세 숫자의 값과 위치를 모두 맞추셨습니다.\" % count)\r\n\r\n\r\n","sub_path":"code_it/num_baseball.py","file_name":"num_baseball.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"607500335","text":"import argparse\nimport threading\nimport socket\nimport sys\nimport re\n\nimport taskqueue\n\n\nclass ClientThread(threading.Thread):\n def __init__(self, conn, addr, server):\n super().__init__(daemon=True)\n self.conn = conn\n self.addr = addr\n self.server = server\n self.data = b''\n self.commands = [\n (re.compile(r'ADD (?P\\S+) (?P\\d+) (?P.+)', re.DOTALL), self.server.add_task),\n (re.compile(r'GET (?P\\S+)'), self.server.get_task),\n (re.compile(r'ACK (?P\\S+) (?P\\S+)'), self.server.ack_task),\n (re.compile(r'IN (?P\\S+) (?P\\S+)'), self.server.check_task)\n ]\n\n def run(self):\n while True:\n try:\n self.data = self.data + self.conn.recv(1024)\n except OSError:\n break\n\n if self.data.endswith(b'\\n'):\n try:\n self.exec_command(self.data)\n except OSError:\n break\n\n def exec_command(self, data : bytes):\n data_str = data.decode('utf-8').strip()\n res = b''\n for command in self.commands:\n m = command[0].fullmatch(data_str)\n if m is not None:\n try:\n res = command[1](*tuple(m.groupdict().values()))\n except ValueError:\n res = b'INVALID COMMAND ARGUMENTS'\n break\n else:\n res = b'INVALID COMMAND'\n self.conn.send(res.strip() + b'\\n')\n self.data = b''\n\n\nclass TaskQueueServer(threading.Thread):\n def __init__(self, port, ip, path, timeout):\n super().__init__(daemon=True)\n # socket moment)\n try:\n self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.sock.bind((ip, port))\n self.sock.listen(10)\n except OSError:\n print('FAILED TO CREATE SOCKET')\n sys.exit()\n\n self.clients = []\n self.queues = {}\n\n def run(self):\n while True:\n conn, addr = self.sock.accept()\n new_client = ClientThread(conn, addr, self)\n self.clients.append(new_client)\n new_client.start()\n\n def add_task(self, q_name, length, t_data):\n length = int(length)\n t_data = t_data.encode('utf-8')\n if len(t_data) != length:\n raise ValueError\n if q_name not in self.queues:\n self.queues[q_name] = taskqueue.TaskQueue(q_name)\n return self.queues[q_name].add_task(length, t_data)\n\n def get_task(self, q_name):\n if q_name not in self.queues:\n return b'NONE'\n else:\n return self.queues[q_name].get_task()\n \n def ack_task(self, q_name, id):\n if len(id) > 128:\n raise ValueError\n if q_name not in self.queues:\n return b'QUEUE NOT EXISTS'\n else:\n return self.queues[q_name].ack_task(id)\n\n def check_task(self, q_name, id):\n if q_name not in self.queues:\n return b'QUEUE NOT EXISTS'\n else:\n return self.queues[q_name].check_task(id)\n\n def dump(self):\n pass\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='This is a simple task queue server with custom protocol')\n parser.add_argument(\n '-p',\n action=\"store\",\n dest=\"port\",\n type=int,\n default=5556,\n help='Server port')\n parser.add_argument(\n '-i',\n action=\"store\",\n dest=\"ip\",\n type=str,\n default='0.0.0.0',\n help='Server ip address')\n parser.add_argument(\n '-c',\n action=\"store\",\n dest=\"path\",\n type=str,\n default='./',\n help='Server checkpoints dir')\n parser.add_argument(\n '-t',\n action=\"store\",\n dest=\"timeout\",\n type=int,\n default=300,\n help='Task maximum GET timeout in seconds')\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = parse_args()\n server = TaskQueueServer(**args.__dict__)\n server.run()\n sys.exit()","sub_path":"homeworks/task_queue/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"461280085","text":"import torch\nimport torch.nn as nn\nfrom torch import Tensor\n\nfrom data import load_data, create_encoders, encode_with\n\nfrom ex1 import accuracy\n\n#############################################\n# DATASET\n#############################################\n\n# Load the datasets\nraw_train = load_data(\"train.csv\")\nraw_dev = load_data(\"dev.csv\")\n\n# Create the encoders, encode the datasets\nchar_enc, lang_enc = create_encoders(raw_train)\nenc_train = encode_with(raw_train, char_enc, lang_enc)\nenc_dev = encode_with(raw_dev, char_enc, lang_enc)\n\n# Report the size of the datasets\nprint(f'# train = {len(enc_train)}')\nprint(f'# dev = {len(enc_dev)}')\n\n#############################################\n# TRAINING LOSS\n#############################################\n\n# You solved that one already, see for instance:\n# https://github.com/kawu/hhu-dl-solutions-2020/blob/main/4/ex2.py\ndef loss(pred: Tensor, target: Tensor) -> Tensor:\n \"\"\"Calculate the cross-entropy loss between the predicted and\n the target tensors, in case where the target is a scalar value\n (tensor of dimension 0).\n \"\"\"\n # Preliminary checks (optional)\n assert pred.dim() == 1 # vector\n assert target.dim() == 0 # scalar\n assert 0 <= target.item() < pred.shape[0]\n nn_loss = nn.CrossEntropyLoss()\n return nn_loss(pred.view(1, -1), target.view(1))\n\n#############################################\n# MODEL\n#############################################\n\nclass CBOW(nn.Module):\n '''Continuous bag of words\n \n Type: Tensor[N x D] -> Tensor[D], where\n * N: sentence length\n * D: embedding size\n\n CBOW replaces the input matrix tensor (where each row represents the\n embedding of an input object) by a single vector, which is a sum of all\n the input vectors.\n '''\n def forward(self, m: torch.Tensor) -> torch.Tensor:\n return torch.sum(m, dim=0)\n\n# TODO: Improve the model specified below in order to improve\n# its language prediction accuracy\n\n# The baseline model:\n# 1. Embed all characters in the given input person name as vectors\n# 2. Use CBOW to compute the vector representation of the input name\n# 3. Score the person name representations with a linear transformation\n# See also https://github.com/kawu/hhu-dl-solutions-2020/blob/main/4/ex1_v1.py\nmodel = nn.Sequential(\n nn.Embedding(char_enc.size()+1, 10, padding_idx=char_enc.size()),\n CBOW(),\n nn.Linear(10, lang_enc.size()),\n)\n\n#############################################\n# TRAINING\n#############################################\n\n# TODO: Implement the training procedure here. You should train the model\n# on the (encoded) training set. Report the accuracy on the (encoded) dev\n# set during training to see whether the performance increases.\n\n#############################################\n# EVALUATION\n#############################################\n\n# Put the model in the evaluation mode\nmodel.eval()\n\ndef predict(model, name: str) -> str:\n \"\"\"Language prediction with the trained model.\"\"\"\n x = torch.tensor([char_enc.encode(char) for char in name])\n pred_y = torch.argmax(model(x), dim=0)\n return lang_enc.decode(pred_y.item()) # type: ignore\n\n# First show the results for a selection of person names\nprint('# PREDICTION FOR SELECTED NAMES')\nfor name, gold_lang in raw_dev[:50]:\n pred_lang = predict(model, name)\n print(f'{name} =>\\t{pred_lang}\\t(gold: {gold_lang})')\n\n# NOTE: Do not change the code below!\nprint('# FINAL EVALUATION')\ndev_acc = accuracy(model, enc_dev)\nif dev_acc > 0.8:\n print(f'PERFECT: acc(dev) = {dev_acc} (> 0.8)')\nelif dev_acc > 0.75:\n print(f'VERY GOOD: acc(dev) = {dev_acc} (> 0.75)')\nelif dev_acc > 0.7:\n print(f'GOOD: acc(dev) = {dev_acc} (> 0.7)')\nelif dev_acc > 0.6:\n print(f'SUFFICIENT: acc(dev) = {dev_acc} (> 0.6)')\nelse:\n print(f'POOR: acc(dev) = {dev_acc} (<= 0.6)')\n","sub_path":"homeworks/7/ex2.py","file_name":"ex2.py","file_ext":"py","file_size_in_byte":3829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"448243893","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 31 13:52:32 2019\n\n@author: nmazzi\n\"\"\"\n\nimport gurobipy as gb\nimport numpy as np\n#from functions.heatpumps import poly\n\n#%%\nclass obj(object):\n '''\n A small class which can have attributes set\n '''\n pass\n\nclass singleZoneOptimizer:\n \n# C_m = x[0]\n# H_tr_em = x[1]\n# H_tr_is = x[2]\n# H_tr_ms = x[3]\n# H_tr_w = x[4] \n# H_ve = x[5]\n# k_conv = x[6] \n \n \n def __init__(self, optimal_parameters, settings, build_data, hvac_data):\n\n self.data = obj()\n self.vardata = obj()\n self.variables = obj()\n self.constraints = obj()\n self.output = obj()\n self.tseries = obj()\n\n \n # Load sets\n self.data.T = range(settings['nhours_horizon']*settings['hourly_steps']); T = self.data.T\n \n # Load output\n self.output.u = np.zeros(len(T))\n \n # Load parameters\n \n self.data.tau = settings['timestep']; tau = self.data.tau\n\n# self.calset.x_nom = [self.nompars.C_m, self.nompars.H_tr_em, self.nompars.H_tr_is, \n# self.nompars.H_tr_ms, self.nompars.H_tr_w, self.nompars.H_ve, \n# self.auxdata.k_conv_nom, self.auxdata.fgc, self.auxdata.phi_0]\n \n self.data.Cm = optimal_parameters[0]/tau; Cm = self.data.Cm\n self.data.H_trem = optimal_parameters[1]; H_trem = self.data.H_trem\n self.data.H_tris = optimal_parameters[2]; H_tris = self.data.H_tris\n self.data.H_trms = optimal_parameters[3]; H_trms = self.data.H_trms\n self.data.H_trw = optimal_parameters[4]; H_trw = self.data.H_trw\n self.data.H_ve = optimal_parameters[5]; H_ve = self.data.H_ve \n self.data.k_conv = optimal_parameters[6]; #k_conv = self.data.k_conv\n self.data.fgc = optimal_parameters[7]; #fgc = self.data.fgc\n self.data.phi_0 = optimal_parameters[8]; \n \n self.data.Am = build_data['A_m'] # Am = self.data.Am \n self.data.A_floor = build_data['A_floor'] # A_floor = self.data.A_floor\n self.data.A_aw = build_data['A_walls']\n self.data.k0_s = self.data.fgc*build_data['k0_s'] # 0.6\n self.data.k1_s = self.data.fgc*build_data['k1_s'] # 0.024 \n self.data.k2_s = self.data.fgc*build_data['k2_s'] # 0.190\n \n# scrivere i parametri in modo che sia COP = f(T_ext) \n self.data.season = hvac_data['season']\n self.data.Q_max_h = hvac_data['Q_hp_nom_heat'] \n self.data.coeff_qmax_h = hvac_data['params_hp_heat_qmax'] \n self.data.coeff_fcop_h = hvac_data['params_hp_heat_fcop']\n# self.data.Q_max_c = hvac_data['Q_hp_nom_cool'] \n# self.data.coeff_qmax_c = hvac_data['params_hp_cool_qmax'] \n# self.data.coeff_fcop_c = hvac_data['params_hp_cool_fcop']\n self.data.V_hs = hvac_data['V_hs'] # m3\n self.data.UA_hs = hvac_data['UA_hs'] # W/K \n self.data.theta_hs_min = hvac_data['theta_hs_min'] # °C\n self.data.theta_hs_max = hvac_data['theta_hs_max'] # °C\n self.data.k_fc_c = hvac_data['k_fancoil_c'] # W/K\n self.data.k_fc_h = hvac_data['k_fancoil_h'] # W/K \n self.data.r_min = hvac_data['r_min'] # -\n self.data.r_min_start = hvac_data['r_min_start'] # -\n self.data.r_min_fc = hvac_data['r_min_fc'] # \n self.data.Qmax_fc_heat = hvac_data['Q_fc_max_heat']\n self.data.Qmax_fc_cool = hvac_data['Q_fc_max_cool']\n \n self.data.rho = 970 # kg/m3\n self.data.cpw = 4183 # J/(kg K)\n self.data.M_hs = self.data.rho*self.data.V_hs*self.data.cpw # J/K\n self.data.C_hs = self.data.M_hs/tau \n UA_hs = self.data.UA_hs # W/K\n C_hs = self.data.C_hs # W/K\n \n self.data.pbuy = settings['pbuy']/1000*(tau/3600)\n self.data.psell = settings['psell']/1000*(tau/3600)\n self.data.pgamma = settings['pgamma']\n self.data.ugamma = settings['ugamma']\n self.data.blocked_hours = settings['blocked_hours']\n self.data.blocked_steps = int(self.data.blocked_hours*3600/tau)\n \n self.data.time_limit = settings['time_limit']\n \n self.vardata.phi = {}\n self.vardata.phi_sol = {}\n self.vardata.theta_e = {}\n self.vardata.theta_su = {}\n self.vardata.Q_hp_max = {}\n self.vardata.Q_hc_max = {}\n self.vardata.f_cop = {}\n\n self.model = gb.Model()\n self.model.Params.OutputFlag = False\n\n # Load variables\n self.variables.w_buy = {}; w_buy = self.variables.w_buy\n self.variables.w_sell = {}; w_sell = self.variables.w_sell\n self.variables.u_hp = {}; u_hp = self.variables.u_hp\n self.variables.w_hp = {}; w_hp = self.variables.w_hp\n self.variables.phi_hp = {}; phi_hp = self.variables.phi_hp\n self.variables.Q_hp_mod = {}; Q_hp_mod = self.variables.Q_hp_mod\n self.variables.Q_hc_mod = {}; Q_hc_mod = self.variables.Q_hc_mod\n self.variables.theta_i = {}; theta_i = self.variables.theta_i \n self.variables.theta_s = {}; theta_s = self.variables.theta_s \n self.variables.theta_m = {}; theta_m = self.variables.theta_m \n self.variables.theta_hs = {}; theta_hs = self.variables.theta_hs \n self.variables.u_hc = {}; u_hc = self.variables.u_hc\n self.variables.phi_hc = {}; phi_hc = self.variables.phi_hc \n self.variables.delta_up = {}; delta_up = self.variables.delta_up\n self.variables.delta_dw = {}; delta_dw = self.variables.delta_dw \n self.variables.x_su = {}; x_su = self.variables.x_su \n\n for t in T:\n self.variables.w_buy[t] = self.model.addVar(lb = 0)\n self.variables.w_sell[t] = self.model.addVar(lb = 0)\n self.variables.u_hp[t] = self.model.addVar(lb = 0 , ub = 1, vtype = gb.GRB.BINARY)\n self.variables.w_hp[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.phi_hp[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.Q_hp_mod[t] = self.model.addVar(lb = 0)\n self.variables.Q_hc_mod[t] = self.model.addVar(lb = 0)\n self.variables.theta_i[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.theta_s[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.theta_m[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.theta_hs[t] = self.model.addVar(lb = self.data.theta_hs_min , ub = self.data.theta_hs_max)\n self.variables.u_hc[t] = self.model.addVar(lb = 0 , ub = 1, vtype = gb.GRB.BINARY)\n self.variables.phi_hc[t] = self.model.addVar(lb = -gb.GRB.INFINITY)\n self.variables.delta_up[t] = self.model.addVar(lb = 0)\n self.variables.delta_dw[t] = self.model.addVar(lb = 0) \n self.variables.x_su[t] = self.model.addVar(lb = 0 , ub = 1, vtype = gb.GRB.BINARY)\n\n self.model.update()\n\n # Objective\n self.model.setObjective(0, gb.GRB.MINIMIZE)\n\n # Constraints\n self.constraints.building_3a = {}\n self.constraints.building_3b = {}\n self.constraints.building_3c = {}\n self.constraints.storage_4 = {}\n self.constraints.storage_4b = {}\n self.constraints.heatpump_5a = {}\n self.constraints.heatpump_5b = {}\n self.constraints.heatpump_5c = {}\n self.constraints.heatpump_5d = {}\n self.constraints.heatpump_5e = {}\n self.constraints.heatpump_5f = {}\n self.constraints.heatpump_5g = {}\n self.constraints.heatpump_5h = {}\n self.constraints.elbalance_6 = {}\n self.constraints.comfort_7a = {}\n self.constraints.comfort_7b = {}\n self.constraints.fancoil_8a = {}\n self.constraints.fancoil_8b = {}\n self.constraints.fancoil_8c = {}\n\n\n for t in T:\n # Building energy balance\n self.constraints.building_3a[t] = self.model.addConstr(-(H_ve+H_tris)*theta_i[t]+H_tris*theta_s[t]+self.data.k_conv*phi_hc[t], gb.GRB.EQUAL , 0)\n self.constraints.building_3b[t] = self.model.addConstr(H_tris*theta_i[t]-(H_tris+H_trw+H_trms)*theta_s[t]+H_trms*theta_m[t]+(1-self.data.k_conv)*phi_hc[t], gb.GRB.EQUAL , 0)\n if (t==min(T)):\n self.constraints.building_3c[t] = self.model.addConstr(H_trms*theta_s[t]-(H_trms+H_trem+Cm)*theta_m[t], gb.GRB.EQUAL, 0)\n else:\n self.constraints.building_3c[t] = self.model.addConstr(H_trms*theta_s[t]-(H_trms+H_trem+Cm)*theta_m[t]+Cm*theta_m[t-1], gb.GRB.EQUAL, 0)\n # Thermal storage energy balance\n if (t==min(T)):\n self.constraints.storage_4[t] = self.model.addConstr((C_hs+UA_hs)*theta_hs[t]-UA_hs*theta_i[t]-phi_hp[t]+phi_hc[t], gb.GRB.EQUAL, 0)\n \n else:\n self.constraints.storage_4[t] = self.model.addConstr((C_hs+UA_hs)*theta_hs[t]-UA_hs*theta_i[t]-phi_hp[t]+phi_hc[t]-C_hs*theta_hs[t-1], gb.GRB.EQUAL, 0)\n \n # Heat pump capacity and COP\n self.constraints.heatpump_5a[t] = self.model.addConstr(phi_hp[t] - u_hp[t] - Q_hp_mod[t], gb.GRB.EQUAL, 0) # incomplete: updated at line 228\n self.constraints.heatpump_5b[t] = self.model.addConstr(Q_hp_mod[t] - u_hp[t], gb.GRB.LESS_EQUAL, 0)\n self.constraints.heatpump_5c[t] = self.model.addConstr(w_hp[t] + phi_hp[t], gb.GRB.EQUAL, 0) # incomplete: updated at line 229\n # Definition of start-up (d,e,f)\n if t==min(T):\n self.constraints.heatpump_5d[t] = self.model.addConstr(u_hp[t] - x_su[t], gb.GRB.LESS_EQUAL, 0)\n self.constraints.heatpump_5f[t] = self.model.addConstr(x_su[t] , gb.GRB.LESS_EQUAL, 0)\n else:\n self.constraints.heatpump_5d[t] = self.model.addConstr(u_hp[t] - u_hp[t-1] - x_su[t], gb.GRB.LESS_EQUAL, 0)\n self.constraints.heatpump_5f[t] = self.model.addConstr(x_su[t] + u_hp[t-1], gb.GRB.LESS_EQUAL, 0)\n self.constraints.heatpump_5e[t] = self.model.addConstr(x_su[t] - u_hp[t], gb.GRB.LESS_EQUAL, 0)\n \n # Mimimum power at start-up\n self.constraints.heatpump_5g[t] = self.model.addConstr(x_su[t] - phi_hp[t], gb.GRB.LESS_EQUAL, 0) #incomplete: updated at line XXX \n # Mimimum duration of heat pump operation (2 steps)\n if t < max(T):\n self.constraints.heatpump_5h[t] = self.model.addConstr(x_su[t] - u_hp[t+1], gb.GRB.LESS_EQUAL, 0)\n # Electrical energy balance\n self.constraints.elbalance_6[t] = self.model.addConstr(w_hp[t] + w_sell[t] - w_buy[t], gb.GRB.EQUAL, 0)\n # Thermal comfort\n self.constraints.comfort_7a[t] = self.model.addConstr(theta_i[t] - delta_up[t], gb.GRB.LESS_EQUAL, 1)\n self.constraints.comfort_7b[t] = self.model.addConstr(theta_i[t] + delta_dw[t], gb.GRB.GREATER_EQUAL, 1)\n # Fan coils\n if self.data.season == 'h':\n self.constraints.fancoil_8a[t] = self.model.addConstr(phi_hc[t] - u_hc[t]*self.data.r_min_fc*self.data.Qmax_fc_heat - Q_hc_mod[t], gb.GRB.EQUAL, 0) \n self.constraints.fancoil_8b[t] = self.model.addConstr(Q_hc_mod[t] -u_hc[t]*(1-self.data.r_min_fc)*self.data.Qmax_fc_heat, gb.GRB.LESS_EQUAL, 0)\n self.constraints.fancoil_8c[t] = self.model.addConstr(phi_hc[t] - self.data.k_fc_h*theta_hs[t] + self.data.k_fc_h*theta_i[t], gb.GRB.LESS_EQUAL, 0)\n else:\n self.constraints.fancoil_8a[t] = self.model.addConstr(phi_hc[t] - u_hc[t]*self.data.r_min_fc*self.data.Qmax_fc_cool - Q_hc_mod[t], gb.GRB.EQUAL, 0)\n self.constraints.fancoil_8b[t] = self.model.addConstr(-Q_hc_mod[t] -u_hc[t]*(1-self.data.r_min_fc)*self.data.Qmax_fc_heat, gb.GRB.LESS_EQUAL, 0)\n self.constraints.fancoil_8c[t] = self.model.addConstr(-phi_hc[t] - self.data.k_fc_c*theta_i[t] + self.data.k_fc_c*theta_hs[t], gb.GRB.LESS_EQUAL, 0)\n \n if self.data.season == 'h':\n self.constraints.storage_4b[0] = self.model.addConstr(theta_hs[0] - theta_hs[max(T)], gb.GRB.LESS_EQUAL, 0)\n else:\n self.constraints.storage_4b[0] = self.model.addConstr(theta_hs[max(T)] - theta_hs[0], gb.GRB.LESS_EQUAL, 0)\n \n \n# self.model.optimize() \n# timeLimit = 120\n try:\n oldSolutionLimit = self.model.Params.SolutionLimit\n self.model.Params.SolutionLimit = 1\n self.model.optimize()\n self.model.Params.TimeLimit = self.data.time_limit - self.model.getAttr(gb.GRB.Attr.Runtime)\n self.model.Params.SolutionLimit = oldSolutionLimit - self.model.Params.SolutionLimit\n self.model.optimize()\n except (AttributeError, Exception) as e:\n print('Caught: ' + e.message)\n \n # Load tseries\n self.tseries.phi_hc = np.zeros(len(self.data.T))\n self.tseries.phi_sol = np.zeros(len(self.data.T))\n self.tseries.w_hp = np.zeros(len(self.data.T))\n self.tseries.phi_hp = np.zeros(len(self.data.T))\n self.tseries.theta_i = np.zeros(len(self.data.T))\n self.tseries.theta_hs = np.zeros(len(self.data.T))\n self.tseries.u_hp = np.zeros(len(self.data.T))\n self.tseries.x_su = np.zeros(len(self.data.T))\n self.tseries.u_hc = np.zeros(len(self.data.T))\n \n \n def gains2nodes(self, phi_int, I_opa, I_gla, T_e, T_sky):\n A_m = self.data.Am\n H_tr_w = self.data.H_trw\n k0 = self.data.k0_s\n k1 = self.data.k1_s\n k2 = self.data.k2_s\n A_t = 4.5*self.data.A_floor\n S_aw = self.data.A_aw\n phi_sol = np.zeros(len(self.data.T))\n for t in self.data.T: \n self.vardata.phi_sol[t] = k0*I_gla[t] + k1/S_aw*I_opa[t] + k2*(-3.) #(T_e[t] - T_sky[t]) # simplified formula\n # distribute solar and internal heat gains to temperature nodes\n self.vardata.phi[t,0] = 0.5*phi_int[t]\n self.vardata.phi[t,1] = (1 - A_m/A_t - H_tr_w/(9.1*A_t))*(0.5*phi_int[t] + phi_sol[t])\n self.vardata.phi[t,2] = A_m/A_t*(0.5*phi_int[t] + phi_sol[t])\n \n def heatPump(self, t1, t2):\n # Maximum heat flow rate\n c0 = self.data.coeff_qmax_h[0]\n c = self.data.coeff_qmax_h[1]\n if len(c) == 9:\n x = [t1, t2, t1**2, t2**2, t1*t2, t1**3, t2**3, (t1**2)*t2, t1*(t2**2)]\n elif len(c) == 5:\n x = [t1, t2, t1**2, t2**2, t1*t2]\n elif len(c) == 2:\n x = [t1, t2]\n else:\n print('Error: number of coefficient is not coherent with length of polynomials')\n # Maximum heat flow rate\n qmax = c0 + np.sum(np.multiply(x,c))\n Q_hp_max = qmax[0]*1000 # converted kW (logs) --> W (optimization)\n \n # f_cop (to be updated with correlation)\n k0 = self.data.coeff_fcop_h[0]\n k = self.data.coeff_fcop_h[1]\n fcop = k0 + np.sum(np.multiply([t1, t2],k))\n f_cop = fcop[0] \n return Q_hp_max, f_cop\n\n def update(self, fdata, cstate, *argv):\n \n # Read input data\n self.vardata.theta_e = fdata['Te']\n self.vardata.theta_su = fdata['Te'] # to be updated with formula of heat recovery\n self.vardata.theta_sky = fdata['Te'] # to be updated with T_sky\n self.vardata.I_opa = fdata['I_tot_opa']\n self.vardata.I_gla = fdata['I_tot_gla']\n self.vardata.phi_int = self.data.phi_0*np.ones([len(fdata.index),]) #0*fdata['I_tot_gla'] \n self.vardata.theta_max = fdata['theta_max']\n self.vardata.theta_min = fdata['theta_min']\n self.vardata.w_pv = fdata['W_pv']\n self.vardata.phi_hc_old = fdata['phi_hc_old']\n self.vardata.phi_hp_old = fdata['phi_hp_old']\n \n self.vardata.theta_m0 = cstate['Tm_0']; theta_m0 = self.vardata.theta_m0\n self.vardata.theta_hs0 = cstate['Ths_0']; theta_hs0 = self.vardata.theta_hs0\n self.vardata.theta_hp_out = cstate['Thp_out_avg']\n self.vardata.u_hp0 = cstate['u_hp0']; u_hp0 = self.vardata.u_hp0\n \n season = argv[0]\n if season == 'h':\n for t in self.data.T:\n # calculate heat pump performance (max heat flow rate and COP)\n self.vardata.Q_hp_max[t], self.vardata.f_cop[t] = self.heatPump(self.vardata.theta_e[t], self.vardata.theta_hp_out) \n # update coefficients to linear constraints \n self.model.chgCoeff(self.constraints.heatpump_5a[t], self.variables.u_hp[t], -self.data.r_min*self.vardata.Q_hp_max[t])\n self.model.chgCoeff(self.constraints.heatpump_5c[t], self.variables.phi_hp[t], -self.vardata.f_cop[t]) \n# self.model.chgCoeff(self.constraints.fancoil_8a[t], self.variables.u_hc[t], -self.data.r_min_fc*self.data.Qmax_fc_heat) \n elif season == 'c':\n for t in self.data.T:\n # calculate heat pump performance (max heat flow rate and COP)\n self.vardata.Q_hp_max[t], self.vardata.f_cop[t] = self.heatPump(self.vardata.theta_e[t], self.vardata.theta_hp_out[t]) \n self.vardata.Q_hc_max[t] = -self.data.Qmax_fc_cool\n # update coefficients to linear constraints\n self.model.chgCoeff(self.constraints.heatpump_5a[t], self.variables.u_hp[t], self.data.r_min*self.vardata.Q_hp_max[t])\n self.model.chgCoeff(self.constraints.heatpump_5a[t], self.variables.Q_hp_mod[t], 1)\n self.model.chgCoeff(self.constraints.heatpump_5c[t], self.variables.phi_hp[t], self.vardata.f_cop[t])\n \n # Objective\n self.model.setObjective(gb.quicksum(self.data.pbuy*self.variables.w_buy[t] - self.data.psell*self.variables.w_sell[t] + \n self.data.pgamma*(self.variables.delta_up[t] + self.variables.delta_dw[t]) for t in self.data.T) + \n gb.quicksum(self.data.ugamma*(self.variables.phi_hc[t] - self.vardata.phi_hc_old[t])**2 + \n self.data.ugamma*(self.variables.phi_hp[t] - self.vardata.phi_hp_old[t])**2 for t in range(self.data.blocked_steps)), \n gb.GRB.MINIMIZE)\n \n # Distribute heat gains to nodes\n self.gains2nodes(self.vardata.phi_int, self.vardata.I_opa, self.vardata.I_gla, self.vardata.theta_e, self.vardata.theta_sky)\n \n # Right hand side (rhs) terms of the constraints (equations) \n for t in self.data.T:\n # Building energy balance (rhs)\n self.constraints.building_3a[t].rhs = -self.data.H_ve*self.vardata.theta_e[t]-self.vardata.phi[t,0]\n self.constraints.building_3b[t].rhs = -self.vardata.phi[t,1]-self.data.H_trw*self.vardata.theta_e[t]\n if (t==min(self.data.T)):\n self.constraints.building_3c[t].rhs = -self.vardata.phi[t,2]-self.data.H_trem*self.vardata.theta_e[t]-self.data.Cm*theta_m0\n else:\n self.constraints.building_3c[t].rhs = -self.vardata.phi[t,2]-self.data.H_trem*self.vardata.theta_e[t]\n # Thermal storage energy balance (rhs) \n if (t==min(self.data.T)):\n self.constraints.storage_4[t].rhs = self.data.C_hs*theta_hs0 \n else:\n self.constraints.storage_4[t].rhs = 0 \n # Heat pump (rhs)\n self.model.chgCoeff(self.constraints.heatpump_5b[t], self.variables.u_hp[t], -(1-self.data.r_min)*self.vardata.Q_hp_max[t])\n if (t==min(self.data.T)):\n self.constraints.heatpump_5d[t].rhs = u_hp0\n self.constraints.heatpump_5f[t].rhs = 1 - u_hp0\n else:\n self.constraints.heatpump_5d[t].rhs = 0\n self.constraints.heatpump_5f[t].rhs = 1\n \n self.model.chgCoeff(self.constraints.heatpump_5g[t], self.variables.x_su[t], self.data.r_min_start*self.vardata.Q_hp_max[t])\n # Electrical energy balance (rhs)\n self.constraints.elbalance_6[t].rhs = self.vardata.w_pv[t]\n # Thermal comfort (rhs)\n self.constraints.comfort_7a[t].rhs = self.vardata.theta_max[t]\n self.constraints.comfort_7b[t].rhs = self.vardata.theta_min[t]\n \n\n self.model.optimize()\n \n# # Load updated variables in tseries object (for controller check)\n for t in self.data.T:\n self.tseries.phi_hc[t] = self.variables.phi_hc[t].x\n self.tseries.phi_hp[t] = self.variables.phi_hp[t].x\n self.tseries.w_hp[t] = self.variables.w_hp[t].x\n self.tseries.theta_i[t] = self.variables.theta_i[t].x\n self.tseries.theta_hs[t] = self.variables.theta_hs[t].x\n self.tseries.u_hp[t] = self.variables.u_hp[t].x\n self.tseries.x_su[t] = self.variables.x_su[t].x\n self.tseries.u_hc[t] = self.variables.u_hc[t].x\n self.tseries.phi_sol[t] = self.vardata.phi_sol[t]\n\n\n\n#%%\nclass cthermostat:\n def __init__(self,delta_theta):\n\n self.fixdata = obj()\n self.vardata = obj()\n self.output = obj()\n self.tseries = obj()\n\n # Load fixdata (heat pump parameters)\n self.fixdata.delta_theta = delta_theta \n \n # Initialize vardata (time-dependent variables)\n self.vardata.state = {} \n self.vardata.theta_i = {} \n self.vardata.theta_set = {}\n \n # Initialize output variables\n self.output.onoff = {};\n \n # Initialize time-series\n self.tseries.onoff = np.zeros(0)\n \n\n def update(self, state, theta_i, theta_set, season):\n\n # Update vardata (time-dependent variables)\n self.vardata.state = state; state = self.vardata.state \n self.vardata.theta_i = theta_i; theta_i = self.vardata.theta_i\n self.vardata.theta_set = theta_set; theta_set = self.vardata.theta_set\n \n self.vardata.theta_min = theta_set - self.fixdata.delta_theta ; theta_min = self.vardata.theta_min\n self.vardata.theta_max = theta_set + self.fixdata.delta_theta ; theta_max = self.vardata.theta_max\n \n if season == 'h':\n if (theta_i < theta_min):\n onoff = 1\n elif(theta_i > theta_max):\n onoff = 0\n else:\n if (state==1):\n onoff = 1\n else:\n onoff = 0\n elif season == 'c':\n if (theta_i < theta_min):\n onoff = 0\n elif(theta_i > theta_max):\n onoff = 1\n else:\n if (state==1):\n onoff = 1\n else:\n onoff = 0 \n\n self.output.onoff = onoff \n \n # Store values in time-series objects\n# self.tseries.time = np.append(self.tseries.time ,1+len(self.tseries.time))\n self.tseries.onoff = np.append(self.tseries.onoff ,self.output.onoff) ","sub_path":"modules/old/optimizationClasses6b.py","file_name":"optimizationClasses6b.py","file_ext":"py","file_size_in_byte":24096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"310122509","text":"#!/usr/bin/python\n\nimport subprocess\nimport json\n\n#Setting this to true will alert you when there is a communication problem while posting plugin data to server\nHEARTBEAT = \"true\"\n\n#if any impacting changes to this plugin kindly increment the plugin version here.\nPLUGIN_VERSION = \"1\"\n\nif __name__ == '__main__':\n cmd = 'mailq | grep -c \"^[A-F0-9]\"'\n data = {}\n p = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)\n (output, err) = p.communicate()\n p_status = p.wait()\n \n data['mailq_count'] = int(output) \n data['heartbeat_required'] = HEARTBEAT\n data['plugin_version'] = PLUGIN_VERSION\n print(json.dumps(data, indent=2, sort_keys=False))\n \n","sub_path":"postfix_mailq_count/postfix_mailq_count.py","file_name":"postfix_mailq_count.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"2430097","text":"from datetime import datetime\nfrom typing import Callable, List, Optional\n\nimport click\n\nimport qubekit\nfrom qubekit.molecules import Ligand\nfrom qubekit.utils.exceptions import WorkFlowExecutionError\nfrom qubekit.utils.file_handling import folder_setup\nfrom qubekit.workflow import WorkFlow, WorkFlowResult\n\nstages = click.Choice(\n [\n \"parametrisation\",\n \"optimisation\",\n \"hessian\",\n \"charges\",\n \"virtual_sites\",\n \"non_bonded\",\n \"bonded_parameters\",\n \"torsion_scanner\",\n \"torsion_optimisation\",\n ],\n case_sensitive=True,\n)\n\n\ndef runtime_options(function: Callable) -> Callable:\n \"\"\"Wrap a CLI function with common runtime option settings.\"\"\"\n function = click.option(\n \"-nc\",\n \"--cores\",\n type=click.INT,\n help=\"The total number of cores the workflow can use.\",\n )(function)\n function = click.option(\n \"-mem\",\n \"--memory\",\n type=click.INT,\n help=\"The total amount of memory available in GB.\",\n )(function)\n function = click.option(\n \"-e\",\n \"--end\",\n help=\"The name of the last stage to be run in the workflow, can be used to finish a workflow early.\",\n type=stages,\n )(function)\n function = click.option(\n \"-s\",\n \"--skip-stages\",\n multiple=True,\n help=\"The names of any stages that should be skipped in the workflow.\",\n type=stages,\n )(function)\n function = click.option(\n \"-c\",\n \"--config\",\n type=click.Path(exists=True, dir_okay=False, resolve_path=True, readable=True),\n help=\"The name of the config file which contains the workflow.\",\n )(function)\n return function\n\n\ndef prep_config(\n config_file: Optional[str] = None,\n results: Optional[WorkFlowResult] = None,\n cores: Optional[int] = None,\n memory: Optional[int] = None,\n) -> WorkFlow:\n \"\"\"A helper function to load the config file for the CLI and update common options.\n Note:\n If not config file path is given then we load the default workflow.\n \"\"\"\n # load the local config\n if config_file is not None:\n workflow = WorkFlow.parse_file(config_file)\n # load config from results\n elif results is not None:\n workflow = WorkFlow.from_results(results=results)\n else:\n # use the basic workflow if no config given\n workflow = WorkFlow()\n\n # update the workflow\n workflow.local_resources.cores = cores or workflow.local_resources.cores\n workflow.local_resources.memory = memory or workflow.local_resources.memory\n return workflow\n\n\n@click.group()\n@click.version_option(version=qubekit.__version__, prog_name=\"QUBEKit\")\ndef cli():\n pass\n\n\n@cli.group()\ndef config():\n \"\"\"Make and validate workflows.\"\"\"\n pass\n\n\n@config.command()\n@click.argument(\"filename\", type=click.STRING)\ndef create(filename: str) -> None:\n \"\"\"Create a new config file using the standard workflow and write to file.\"\"\"\n w = WorkFlow()\n w.to_file(filename=filename)\n\n\n@config.command()\n@click.argument(\"filename\", type=click.Path(exists=True))\ndef validate(filename: str) -> None:\n \"\"\"Validate a config file and make sure there are no missing dependencies.\"\"\"\n w = WorkFlow.parse_file(path=filename)\n # validate the full workflow\n workflow = w.get_running_order()\n w.validate_workflow(workflow=workflow)\n\n\n@cli.command()\n@click.option(\n \"-i\",\n \"--input-file\",\n help=\"The name of the input file containing a molecule to be parameterised.\",\n type=click.Path(exists=True, dir_okay=False, resolve_path=True, readable=True),\n)\n@click.option(\n \"-sm\",\n \"--smiles\",\n help=\"The smiles string of the molecule to be parameterised.\",\n type=click.STRING,\n)\n@click.option(\n \"-m\",\n \"--multiplicity\",\n type=click.INT,\n help=\"The multiplicity of the molecule used in QM calculations.\",\n default=1,\n)\n@click.option(\n \"-n\",\n \"--name\",\n type=click.STRING,\n help=\"The name of the molecule, used for fileIO and folder setup.\",\n)\n@runtime_options\ndef run(\n input_file: Optional[str] = None,\n smiles: Optional[str] = None,\n name: Optional[str] = None,\n multiplicity: int = 1,\n end: Optional[str] = None,\n skip_stages: Optional[List[str]] = None,\n config: Optional[str] = None,\n cores: Optional[int] = None,\n memory: Optional[int] = None,\n):\n \"\"\"Run the QUBEKit parametrisation workflow on an input molecule.\"\"\"\n # make sure we have an input or smiles not both\n if input_file is not None and smiles is not None:\n raise RuntimeError(\n \"Please supply either the name of the input file or a smiles string not both.\"\n )\n # load the molecule\n if input_file is not None:\n molecule = Ligand.from_file(file_name=input_file, multiplicity=multiplicity)\n else:\n if name is None:\n raise RuntimeError(\n \"Please also pass a name for the molecule when starting from smiles.\"\n )\n molecule = Ligand.from_smiles(\n smiles_string=smiles, name=name, multiplicity=multiplicity\n )\n\n # load workflow\n workflow = prep_config(config_file=config, memory=memory, cores=cores)\n\n # move into the working folder and run\n with folder_setup(f\"QUBEKit_{molecule.name}_{datetime.now().strftime('%Y_%m_%d')}\"):\n # write the starting molecule\n molecule.to_file(file_name=f\"{molecule.name}.pdb\")\n workflow.new_workflow(molecule=molecule, skip_stages=skip_stages, end=end)\n\n\n@cli.command()\n@click.argument(\"start\", type=stages)\n@click.option(\n \"-r\",\n \"--results\",\n type=click.Path(exists=True, dir_okay=False, readable=True, resolve_path=True),\n help=\"The results file that the workflow should be restarted from.\",\n default=\"workflow_result.json\",\n)\n@runtime_options\ndef restart(\n start: str,\n results: str,\n skip_stages: Optional[List[str]] = None,\n end: Optional[str] = None,\n config: Optional[str] = None,\n cores: Optional[int] = None,\n memory: Optional[int] = None,\n) -> None:\n \"\"\"Restart a QUBEKit parametrisation job from the given stage.\n Must be started from within an old workflow folder.\n \"\"\"\n # try and load the results file\n results = WorkFlowResult.parse_file(results)\n\n if config is None:\n # if we have no new config load from results\n workflow = prep_config(results=results, cores=cores, memory=memory)\n else:\n # load the new config file\n workflow = prep_config(config_file=config, cores=cores, memory=memory)\n\n # now run the workflow\n workflow.restart_workflow(\n start=start, result=results, skip_stages=skip_stages, end=end\n )\n\n\n@cli.group()\ndef bulk():\n \"\"\"Create or run bulk workflows on a set of molecules.\"\"\"\n pass\n\n\n@bulk.command()\n@click.argument(\n \"bulk_file\",\n type=click.Path(exists=True, dir_okay=False, readable=True, resolve_path=True),\n)\n@click.option(\n \"-restart\",\n \"--restart\",\n type=stages,\n help=\"The stage the workflow should be restarted from.\",\n)\n@runtime_options\ndef run(\n bulk_file: str,\n skip_stages: Optional[List[str]] = None,\n end: Optional[str] = None,\n restart: Optional[str] = None,\n config: Optional[str] = None,\n cores: Optional[int] = None,\n memory: Optional[int] = None,\n) -> None:\n \"\"\"Run the QUBEKit parametrisation workflow on a collection of molecules in serial.\n\n Loop over the molecules in order of the CSV file.\n \"\"\"\n import glob\n import os\n\n from qubekit.utils.helpers import mol_data_from_csv\n\n home = os.getcwd()\n # load all inputs\n bulk_data = mol_data_from_csv(bulk_file)\n\n # start main molecule loop\n for name, mol_data in bulk_data.items():\n print(f\"Analysing: {name}\")\n try:\n if restart is not None or mol_data[\"restart\"] is not None:\n # we are trying to restart a run, find the folder\n # should only be one\n fname = name.split(\".\")[0]\n folder = glob.glob(f\"QUBEKit_{fname}_*\")[0]\n with folder_setup(folder):\n results = WorkFlowResult.parse_file(\"workflow_result.json\")\n if config is None:\n # if we have no new config load from results\n workflow = prep_config(\n results=results, cores=cores, memory=memory\n )\n else:\n # load the new config file\n workflow = prep_config(\n config_file=config, cores=cores, memory=memory\n )\n\n workflow.restart_workflow(\n start=restart or mol_data[\"restart\"],\n skip_stages=skip_stages,\n end=end or mol_data[\"end\"],\n result=results,\n )\n\n else:\n if mol_data[\"smiles\"] is not None:\n molecule = Ligand.from_smiles(\n smiles_string=mol_data[\"smiles\"], name=name\n )\n else:\n molecule = Ligand.from_file(file_name=name)\n\n # load the CLI config or the csv config, else default\n workflow = prep_config(\n config_file=config or mol_data[\"config_file\"],\n memory=memory,\n cores=cores,\n )\n # move into the working folder and run\n with folder_setup(\n f\"QUBEKit_{molecule.name}_{datetime.now().strftime('%Y_%m_%d')}\"\n ):\n # write the starting molecule\n molecule.to_file(file_name=f\"{molecule.name}.pdb\")\n workflow.new_workflow(\n molecule=molecule,\n skip_stages=skip_stages,\n end=end or mol_data[\"end\"],\n )\n except WorkFlowExecutionError:\n os.chdir(home)\n print(\n f\"An error was encountered while running {name} see folder for more info.\"\n )\n continue\n\n\n@bulk.command()\n@click.argument(\"filename\", type=click.STRING)\ndef create(filename: str) -> None:\n \"\"\"Generate a bulk run CSV file from all molecule files in the current directory.\"\"\"\n from qubekit.utils.helpers import generate_bulk_csv\n\n if filename.split(\".\")[-1].lower() != \"csv\":\n filename = f\"{filename}.csv\"\n\n generate_bulk_csv(csv_name=filename)\n\n\n@cli.command()\ndef progress():\n \"\"\"Generate a report of the parametrisation workflow progress for a set of QUBEKit job folders.\"\"\"\n import os\n\n from qubekit.utils.constants import COLOURS\n from qubekit.workflow import Status\n\n results = {}\n for root, _, files in os.walk(\".\", topdown=True):\n for file in files:\n if \"workflow_result.json\" in file and \"backups\" not in root:\n result = WorkFlowResult.parse_file(\n os.path.abspath(os.path.join(root, file))\n )\n results[result.input_molecule.name] = result.status()\n\n if not results:\n print(\n \"No QUBEKit directories with log files found. Perhaps you need to move to the parent directory.\"\n )\n else:\n # Sort alphabetically\n results = dict(sorted(results.items(), key=lambda item: item[0]))\n\n print(\"Displaying progress of all analyses in current directory.\")\n print(f\"Progress key: {COLOURS.green}\\u2713{COLOURS.end} = Done;\", end=\" \")\n print(f\"{COLOURS.blue}S{COLOURS.end} = Skipped;\", end=\" \")\n print(f\"{COLOURS.red}E{COLOURS.end} = Error;\", end=\" \")\n print(f\"{COLOURS.orange}R{COLOURS.end} = Running;\", end=\" \")\n print(f\"{COLOURS.purple}~{COLOURS.end} = Queued\")\n\n header_string = \"{:15}\" + \"{:>10}\" * 9\n print(\n header_string.format(\n \"Name\",\n \"Param\",\n \"Opt\",\n \"Hessian\",\n \"Bonded\",\n \"Charges\",\n \"VSites\",\n \"Non-Bond\",\n \"Tor Scan\",\n \"Tor Opt\",\n )\n )\n\n for name, result in results.items():\n print(f\"{name[:13]:15}\", end=\" \")\n for s in result.values():\n if s == Status.Done:\n stat = \"\\u2713\"\n print(f\"{COLOURS.green}{stat:>9}{COLOURS.end}\", end=\" \")\n elif s == Status.Error:\n stat = \"E\"\n print(f\"{COLOURS.red}{stat:>9}{COLOURS.end}\", end=\" \")\n elif s == Status.Skip:\n stat = \"S\"\n print(f\"{COLOURS.blue}{stat:>9}{COLOURS.end}\", end=\" \")\n elif s == Status.Running:\n stat = \"R\"\n print(f\"{COLOURS.orange}{stat:>9}{COLOURS.end}\", end=\" \")\n elif s == Status.Waiting:\n stat = \"~\"\n print(f\"{COLOURS.purple}{stat:>9}{COLOURS.end}\", end=\" \")\n\n print(\"\")\n\n\nif __name__ == \"__main__\":\n cli()\n","sub_path":"qubekit/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":13231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"470046891","text":"from storage.utils import get_request_title_description\nfrom storage.utils import get_comments_count\nfrom storage.utils import get_test_number, get_pull_request_total, get_reviewer, get_comment_date_max_min\nfrom storage.utils import get_pull_request_user, get_comment_user_and_date, get_pull_request_user_for_test\nfrom collections import Counter\nfrom common.utils import delete_stop_words,sort_priority\nfrom common.algorithms import TermFrequency\nfrom common.algorithms import cosine_similarity\nfrom collections import defaultdict\nimport datetime\nimport os\nimport numpy\nimport json\nimport time\nimport networkx as nx\nfrom common.log import LogAdapter\nfrom multiprocessing import Pool\n\nfrom storage.sql import ONE_MONTH,HALF_YEAR,THREE_MONTH, ONE_YEAR\n\nfrom common.settings import PROJECT_NAME\nLOG_NAME = 'CommentNetwork'\nLOG = LogAdapter().set_log(LOG_NAME)\n\nEMPIRICAL_VALUE = 1.0\nDECAY_FACTOR = 0.6\n\nparent_dir = os.path.abspath(os.pardir)\nsave_path = os.path.join(parent_dir, 'result', 'IRz')\nprint(save_path)\nif not os.path.exists(save_path):\n os.mkdir(save_path)\n\n\nclass CosineSimilarity(object):\n def __init__(self,project_name):\n self.project_name = project_name\n self.comments = get_comments_count(self.project_name)\n\n @staticmethod\n def cos_score(new_word, past_word):\n return cosine_similarity(new_word, past_word)\n\n def score(self, new_word, past_word, number):\n # return self.cos_score(new_word, past_word) * number\n return self.cos_score(new_word, past_word)\n\n\nclass CommentNetwork(object):\n def __init__(self, project_name,train_time):\n self.project_name = project_name\n self.comment_info = get_comment_user_and_date(self.project_name)\n self.default_corpus = self.corpus_prepare(train_time)\n self.max_date, self.min_date = get_comment_date_max_min(self.project_name)\n self.nx = nx.DiGraph()\n\n def corpus_prepare(self,train_time):\n LOG.info('Start to corpus prepare for Comment Network')\n corpus_dict = defaultdict(dict)\n try:\n request_info = get_pull_request_user(self.project_name,train_time)\n for request_user, value in request_info.items():\n for request_number in value:\n comment_user_info = self.comment_info.get(request_number, None)\n\n # delete pull request user own comment\n comment_user_info.pop(request_user, None) if comment_user_info else None\n # if pull request not comment continue\n if not comment_user_info:\n continue\n # TODO have fixed\n # at here,the pull request number is unique, so can easy update.\n corpus_dict[request_user].update({request_number: comment_user_info})\n except Exception as e:\n LOG.error('Failed in corpus prepare,cause {}'.format(e), exc_info=True)\n raise e\n\n return corpus_dict\n\n def calculate_egde_weight(self, comment_date):\n emprircal_value = EMPIRICAL_VALUE\n decay_factor = DECAY_FACTOR\n base_line = datetime.datetime.strptime(self.min_date, '%Y-%m-%d')\n deadline = datetime.datetime.strptime(self.max_date, '%Y-%m-%d')\n\n weight_list = []\n try:\n for item in comment_date:\n for index, date in enumerate(item):\n # factor formula is\n factor = pow(decay_factor, index)\n timestamp = datetime.datetime.strptime(date, '%Y-%m-%d')\n t = (timestamp - base_line) / (deadline - base_line)\n # timestamp = datetime.datetime.strftime()\n weight = factor * t\n weight_list.append(weight)\n sum_weight = numpy.sum(weight_list) * emprircal_value\n except Exception as e:\n LOG.error('Error in Calculate_edge_weight ,Cause {}, Comment_date is {}'.format(e, comment_date), exc_info=True)\n sum_weight = 0\n return sum_weight\n\n def update_graph(self, reuquest_user, comment_info):\n comment_dict = defaultdict(list)\n node = reuquest_user\n # add node\n try:\n if not self.nx.has_node(node):\n self.nx.add_node(reuquest_user)\n\n for number, comment_user_info in comment_info.items():\n for comment_user, comment_date in comment_user_info.items():\n comment_dict[comment_user].append(comment_date)\n\n for edge, comment_date in comment_dict.items():\n # comment date structure is like [[2013-02-04 17:34:02],[2014-03-07 23:55:04,2014-12-04 23:50:02]]\n weight = self.calculate_egde_weight(comment_date)\n # add edge and weight\n self.nx.add_edge(node, edge, weight=weight)\n print(reuquest_user, edge, weight)\n except Exception as e:\n LOG.error('Error update graph, Cause {}, request_user is {},comment_info is {}'.format(e, reuquest_user, comment_info), exc_info=True)\n\n def init_graph(self):\n graph_data = self.default_corpus\n # user is node.\n print('start init ------')\n for request_user, comment_info in graph_data.items():\n self.update_graph(request_user, comment_info)\n print('end init -----')\n\n def corpus_test(self):\n # self.init_graph()\n test_info = get_pull_request_user_for_test(self.project_name)\n return test_info, self.comment_info\n\n def get_graph_edge(self, request):\n edge_list = list()\n origin = self.nx.edges()\n try:\n for item in origin:\n request_user = item[0]\n comment_user = item[1]\n if request_user == request:\n weight = self.nx.get_edge_data(request_user,comment_user).get('weight', 0)\n weight_list = [request_user,comment_user,weight]\n edge_list.append(weight_list)\n sort_edge_list = sorted(edge_list, key=lambda x: x[2], reverse=True)\n if not edge_list:\n print('{} no comment network'.format(request))\n except Exception as e:\n LOG.error('Error in get_graph_edge, Cause {}, request is {}'.format(e, request),exc_info=True)\n sort_edge_list = None\n return sort_edge_list\n\n\nclass Process(object):\n def __init__(self, project_name):\n self.project_name = project_name\n self.cs = CosineSimilarity(self.project_name)\n self.tf = TermFrequency()\n\n def get_corpus(self,train_time):\n request_infos = get_request_title_description(self.project_name,train_time)\n\n key_list = list()\n word_list = list()\n for request_info in request_infos:\n number = list(request_info.keys())[0]\n data = list(request_info.values())[0]\n valid_word = delete_stop_words(data)\n word = Counter(valid_word)\n\n key_list.append(number)\n word_list.append(word)\n\n return key_list, word_list\n\n def get_corpus_result(self,train_time):\n key_list, count_list = self.get_corpus(train_time)\n start = time.time()\n\n score_dict = {}\n for key, count in zip(key_list, count_list):\n\n word_score = {word: self.tf.tf_idf(word, count, count_list) for word in count}\n sorted_score = dict(sorted(word_score.items(), key=lambda x: x[1], reverse=True))\n\n sorted_list = list(sorted_score.keys())\n score_dict.update({key: sorted_list})\n end = time.time()\n print('spend time is {}'.format(end - start))\n return score_dict\n\n def cos_score(self, new, past, reviewer, k):\n \"\"\"\n in this function will get pull request similarity.\n :param new:\n :param past:\n :param k: the number of pull request\n :return:\n \"\"\"\n review_dict = {}\n\n for number, past_word in past.items():\n # print('the number is',number)\n lib_tech_past = list(past_word)\n past_reviewer = reviewer.get(number)\n if not lib_tech_past or not new or not past_reviewer:\n continue\n\n score = self.cs.score(new, past_word, number)\n\n for rpv in past_reviewer:\n if rpv not in review_dict:\n review_dict.setdefault(rpv, score)\n\n else:\n past_score = review_dict.get(rpv)\n review_dict.update({rpv: past_score + score})\n\n candidate_list_sort = sorted(review_dict.items(), key=lambda item: item[1], reverse=True)\n # strip zero score\n strip_zero_value = list(filter(lambda i: i[1] > 0, candidate_list_sort))\n print(strip_zero_value)\n if len(strip_zero_value) > 0:\n split_list = candidate_list_sort[0:k]\n # split_list = candidate_list_sort\n result = [i for i in split_list]\n else:\n result = []\n\n return result\n\n def cos_test_info(self,train_time):\n # get word vector\n info_dict = self.get_corpus_result(train_time)\n # get predict interval\n reviewer = get_reviewer(self.project_name)\n print(reviewer)\n\n # test_number = get_test_number(self.project_name)\n # for i in test_number:\n # new_word = info_dict.get(i)\n # past_dict = {k: v for k, v in info_dict.items() if (k < i)}\n # score_list = self.cos_score(new_word, past_dict, reviewer, 5)\n # print(score_list)\n\n return info_dict, reviewer\n\n\ndef graph_func(comment_info_all, number, pull_request_user, cn):\n edge_info = cn.get_graph_edge(pull_request_user)\n\n comment_info = comment_info_all.get(number, None)\n comment_info.pop(pull_request_user, None) if comment_info else None\n corpus_comment_info = cn.default_corpus.get(pull_request_user)\n\n if not comment_info:\n LOG.info('pull request {} valid comment info is empty'.format(number))\n return\n if not corpus_comment_info:\n LOG.info('corpus no request user:{} comment info'.format(pull_request_user))\n return\n\n cn.update_graph(pull_request_user, corpus_comment_info)\n cn.default_corpus[pull_request_user][number] = comment_info\n\n return edge_info\n\n\ndef cos_func(info_dict, number, reviewer, proc):\n new_word = info_dict.get(number)\n past_dict = {k: v for k, v in info_dict.items() if (k < number)}\n score_list = proc.cos_score(new_word, past_dict, reviewer, 5)\n return score_list\n\n\ndef write_to_file(file_name, info):\n file_path = os.path.join(save_path, file_name)\n with open('{}.txt'.format(file_path), 'a') as f:\n f.write(json.dumps(info))\n f.write('\\n')\n\n\ndef compare_result(predict, review, count_dict):\n result = set(predict) & set(review)\n count_dict['all'] += 1\n if result:\n count_dict['right'] += 1\n else:\n count_dict['wrong'] += 1\n\n return count_dict\n\n\ndef save_rate(project,name, count_dict):\n dict_value = list(count_dict.values())\n try:\n rate = dict_value[0] / (dict_value[0] + dict_value[1])\n except ZeroDivisionError:\n rate = 0\n info = {'project:{} rate:{:.2%}'.format(project, rate): count_dict}\n file_name = '{}'.format(name)\n write_to_file(file_name, info)\n\n\ndef main(project,train_time):\n start_time = time.time()\n proc = Process(project)\n file_name = project.split('/')[1] + train_time.replace('-', '_')\n print('Start Prepare Corpus')\n info_dict, reviewer = proc.cos_test_info(train_time)\n cn = CommentNetwork(project,train_time)\n test_info, comment_info_all = cn.corpus_test()\n\n print('start test-----')\n count_dict1 = {'right': 0, 'wrong': 0, 'all':0}\n count_dict3 = {'right': 0, 'wrong': 0, 'all':0}\n count_dict5 = {'right': 0, 'wrong': 0, 'all':0}\n\n for number, pull_request_user in test_info.items():\n try:\n # comment_info = comment_info_all.get(number, None)\n # if not comment_info:\n # write_to_file(file_name, {number: 'this pull request no comment info'})\n # continue\n\n # comment_info_key = list(comment_info.keys())\n # print(comment_info_key)\n score_list = cos_func(info_dict, number, reviewer, proc)\n print('----',number, pull_request_user, score_list)\n\n # if not score_list:\n # continue\n\n # print('========== graph test ========')\n # edge_info = graph_func(comment_info_all, number, pull_request_user, cn)\n # if edge_info:\n # network_user = [i[2] for i in edge_info]\n # commented_user = sort_priority(score_list, network_user)\n # else:\n # commented_user = score_list\n commented_user = score_list\n write_to_file(file_name, {number: commented_user})\n # commented_user_1, commented_user_3, commented_user_5 = [commented_user[0]],commented_user[0:2],commented_user\n #\n # count_dict1 = compare_result(commented_user_1, comment_info_key,count_dict1)\n # count_dict3 = compare_result(commented_user_3, comment_info_key,count_dict3)\n # count_dict5 = compare_result(commented_user_5, comment_info_key,count_dict5)\n except Exception as e:\n LOG.error('Error in {}:{}, Cause {}'.format(number, pull_request_user, e), exc_info=True)\n # save_rate(project, file_name, count_dict1)\n # save_rate(project, file_name, count_dict3)\n # save_rate(project, file_name, count_dict5)\n end_time = time.time()\n spend_time = end_time - start_time\n write_to_file(file_name,f'spend time is {spend_time}')\n print('end test----')\n\n\nif __name__ == '__main__':\n\n project_name_list = PROJECT_NAME\n pool = Pool()\n for project in project_name_list:\n print(project)\n train_time = [\n ONE_YEAR,\n # HALF_YEAR,\n # THREE_MONTH,\n # ONE_MONTH\n ]\n for i in train_time:\n # pool.apply_async(main, (project, i))\n main(project,i)\n\n # pool.close()\n # pool.join()\n\n#\n# TIME = [ONE_YEAR,HALF_YEAR,THREE_MONTH,ONE_MONTH]\n# for i in TIME:\n# main(project_name,i)\n\n\n\n","sub_path":"core/IR.py","file_name":"IR.py","file_ext":"py","file_size_in_byte":14331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"578067483","text":"\nimport base64,urllib.request\nimport hashlib\nimport hmac\nimport uuid,time\n\ntimestamp=time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime())\nD = {\n 'Action':'DescribeBinlogFiles',\n 'DBInstanceId':'rm-wz9azm783hlq621n9',\n 'StartTime':'2018-07-11T15:00:00Z',\n 'EndTime':timestamp,\n 'Format':'JSON',\n 'Version':'2014-08-15',\n 'AccessKeyId':'LTAIRrEMWqcD9kYX',\n 'SignatureVersion':'1.0',\n 'SignatureMethod':'HMAC-SHA1',\n 'SignatureNonce': str(uuid.uuid1()),\n 'TimeStamp': timestamp,\n\n}\n\nsortedD = sorted(D.items(),key=lambda x: x[0])\n\nurl = 'https://rds.aliyuncs.com'\ndef percentEncode(encodeStr):\n encodeStr = str(encodeStr)\n res = urllib.request.quote(encodeStr)\n res = res.replace('+', '%20')\n res = res.replace('*', '%2A')\n res = res.replace('%7E', '~')\n return str(res)\n\n\npercentEncode(sortedD)\n\ncanstring = ''\nfor k,v in sortedD:\n canstring += '&' + percentEncode(k) + '=' + percentEncode(v)\n\nstiingToSign = 'GET&%2F&' + percentEncode(canstring[1:])\n\naccess_id = 'LTAIRrEMWqcD9kYX'\naccess_key_secret = 'kMO3w6Ek1DnSd8wioYdSUOMfUdwYWJ'\n\n\nbs = access_key_secret + '&'\nbs = bytes(bs,encoding='utf8')\nstiingToSign = bytes(stiingToSign,encoding='utf8')\nh = hmac.new(bs,stiingToSign,hashlib.sha1)\nstiingToSign = base64.b64encode(h.digest()).strip()\n\nD['Signature'] = stiingToSign\n\nurl = url + \"/?\" + urllib.parse.urlencode(D)\nprint(url)","sub_path":"aliyun/py_rds.py","file_name":"py_rds.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"509001911","text":"import os\nimport tempfile\nimport unittest\n\nfrom video_manager import VideoManager\n\n\nclass VideoManagerTest(unittest.TestCase):\n def test_can_extract_audio(self):\n manager = VideoManager(\n os.path.join(os.path.dirname(__file__), \"input\", \"test1.mp4\"),\n )\n _, audio = tempfile.mkstemp(\".wav\")\n\n extracted_audio = manager.extract_audio(audio)\n\n self.assertEqual(audio, extracted_audio)\n self.assertTrue(os.path.exists(extracted_audio))\n\n os.remove(audio)\n\n def test_can_extract_thumbnail(self):\n manager = VideoManager(\n os.path.join(os.path.dirname(__file__), \"input\", \"test2.mp4\"),\n )\n _, thumbnail = tempfile.mkstemp(\".jpg\")\n\n extracted_thumbnail = manager.extract_thumbnail(thumbnail)\n\n self.assertEqual(thumbnail, extracted_thumbnail)\n self.assertTrue(os.path.exists(extracted_thumbnail))\n\n os.remove(thumbnail)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"tape/video_manager_test.py","file_name":"video_manager_test.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"130946312","text":"\"\"\"\nTitle: Find substring\n\nProblem:\n You are given a string, s, and a list of words, words, that are all of the\n same length. Find all starting indices of substring(s) in s that is a\n concatenation of each word in words exactly once and without any\n intervening characters.\n\nExecution: python find_substring.py\n\"\"\"\nfrom collections import Counter\nfrom typing import List\nimport unittest\n\n\ndef find_substring(s: str, words: List[str]) -> List[int]:\n if not words or not s:\n return []\n\n wordlen, nwords = len(words[0]), len(words)\n indices = []\n\n # iterative over `wordlen` times\n for i in range(wordlen):\n counter = Counter(words)\n # start index of sliding window left and right pointer\n l = r = i\n while r + wordlen <= len(s):\n # remove the substring from the counter\n counter[s[r:r+wordlen]] -= 1\n r += wordlen\n # move left pointer one word offset to the right once the window size exceeds\n if r - l > wordlen * nwords:\n counter[s[l:l+wordlen]] += 1\n l += wordlen\n # if the window size is what we want and we found that all values in counter are 0\n # we can make sure that the substring in this window is one of the answer\n # we are looking for\n if r - l == wordlen * nwords and all(count == 0 for count in counter.values()):\n indices.append(l)\n\n return indices\n\nclass TestFindSubstring(unittest.TestCase):\n \"\"\"Unit tests for find_substring.\"\"\"\n\n def test_1(self):\n self.assertEqual(find_substring(\"barfoothefoobarman\", [\"foo\", \"bar\"]), [0, 9])\n\n def test_2(self):\n self.assertEqual(find_substring(\"wordgoodgoodgoodbestword\", [\"word\", \"good\", \"best\", \"word\"]), [])\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"quickstart_guides/arrays_strings/python/find_substring.py","file_name":"find_substring.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"303409335","text":"# coding:utf-8\n\n# import numpy as np\n# import pandas as pd\nimport socket\nimport time\nimport pymysql\n\nfrom ascii_string import AISAnalysis\n\n\nclass AisMes(object):\n def __init__(self):\n self.ascii_string = AISAnalysis()\n self.conn = pymysql.connect(host='192.168.1.63', port=3306, user='root', passwd='traffic170910@0!7!@#3@1',\n db='dbtraffic')\n self.cur = self.conn.cursor()\n\n def ais_org_analysis(self, ais_org_text):\n \"\"\"\n ais原始报文解析\n :param ais_org_text: ais原始报文，类型：string\n :return: 解析后的ais数据，col0:mmsi, col1:create_time, col2:longitude, col3:latitude\n \"\"\"\n msg_body = ais_org_text.split(',')[-2]\n if (len(msg_body) != 0) & (ais_org_text[0] == '!'):\n if msg_body[0] == '1':\n print(ais_org_text)\n msg_ascii = self.ascii_string.str_to_ascii(msg_body)\n\n mmsi = int(self.ascii_string.python_substring(msg_ascii, 8, 30), 2)\n longitude = int(self.ascii_string.python_substring(msg_ascii, 62, 27), 2) / 600000.\n latitude = int(self.ascii_string.python_substring(msg_ascii, 90, 26), 2) / 600000.\n # time_stamp = int(self.ascii_string.python_substring(msg_ascii, 138, 5), 2)\n return [mmsi, longitude, latitude]\n\n def ais_bm_analysis(self, ais_bm_text):\n \"\"\"\n ais博懋数据解析\n :param ais_bm_text: 博懋ais报文，类型：string\n :return: 解析后的ais数据，col0:mmsi, col1:create_time, col2:longitude, col3:latitude\n \"\"\"\n ais_bm_list = ais_bm_text.split('@')\n if ais_bm_list[0] == '0':\n # 解析动态数据\n mmsi = int(ais_bm_list[2])\n longitude = float(ais_bm_list[7])\n latitude = float(ais_bm_list[8])\n nav_status = int(ais_bm_list[3])\n rot = float(ais_bm_list[4])\n sog = float(ais_bm_list[5])\n pos_acc = float(ais_bm_list[6])\n cog = float(ais_bm_list[9])\n true_head = float(ais_bm_list[10])\n if(int(longitude) >= 121) & (int(longitude) <= 123) &\\\n (int(latitude) >= 30) & (int(latitude) <= 32):\n # print(mmsi, longitude, latitude)\n # input(\"-----------------------\")\n return [mmsi, longitude, latitude, nav_status, rot, sog, pos_acc, cog, true_head]\n elif ais_bm_list[0] == '1':\n # 解析静态数据\n mmsi = int(ais_bm_list[2])\n ship_type = ais_bm_list[3]\n imo = ais_bm_list[4]\n callsign = ais_bm_list[5]\n ship_length = float(ais_bm_list[6])\n ship_width = float(ais_bm_list[7])\n pos_type = ais_bm_list[8]\n eta = ais_bm_list[9]\n draught = float(ais_bm_list[10])\n destination = ais_bm_list[12]\n return [mmsi, ship_type, imo, callsign, ship_length, ship_width, pos_type, eta, draught, destination]\n\n def ais_dynamic_mysql(self, ais_msg, create_time, data_source):\n \"\"\"\n 将接受到的ais原始报文入库\n :return:\n \"\"\"\n insert_sql = \"\"\"\n INSERT INTO ais_dynamic(mmsi, create_time, longitude, latitude, data_source, nav_status, rot, sog, \n pos_acc, cog, true_head) VALUE ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')\n \"\"\" % (ais_msg[0], create_time, ais_msg[1], ais_msg[2], data_source, ais_msg[3], ais_msg[4],\n ais_msg[5], ais_msg[6], ais_msg[7], ais_msg[8])\n # print(insert_sql)\n self.cur.execute(insert_sql)\n self.conn.commit()\n\n def ais_static_mysql(self, ais_msg, create_time):\n \"\"\"\n 将接受到的ais静态数据入库\n :param ais_msg:\n :param create_time:\n :return:\n \"\"\"\n insert_sql = \"\"\"\n INSERT INTO ship_static_data_eway(mmsi, shiptype, IMO, callsign, ship_length, ship_width,\n pos_type, eta, draught, destination, ship_english_name) \n VALUE('%d', '%s', '%s', '%s', '%f', '%f', '%s', '%s', '%f', '%s', NULL)\n \"\"\" % (int(ais_msg[0]), ais_msg[1], ais_msg[2], ais_msg[3], float(ais_msg[4]),\n float(ais_msg[5]), ais_msg[6], ais_msg[7], float(ais_msg[8]), ais_msg[9])\n self.cur.execute(insert_sql)\n self.conn.commit()\n\n def get_ais_org_ys(self):\n \"\"\"\n 获取ais数据原始报文，数据源：洋山\n :return: 返回ais原始报文列表，类型：list\n \"\"\"\n address = ('192.168.1.66', 16197)\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.bind(address)\n\n while True:\n try:\n data, addr = s.recvfrom(2048)\n create_time = time.strftime('%Y-%m-%d %H:%M:%S')\n if not data:\n print(\"client has exist\")\n break\n ais_analysised = self.ais_org_analysis(data.decode())\n if ais_analysised:\n self.ais_dynamic_mysql(ais_analysised, create_time, 0)\n except Exception as e:\n print(e)\n s.close()\n\n def get_ais_org_bm(self):\n \"\"\"\n 获取博懋ais动态数据\n :return:\n \"\"\"\n address = ('192.168.1.66', 16198)\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.bind(address)\n\n while True:\n try:\n data, addr = s.recvfrom(2048)\n create_time = time.strftime('%Y-%m-%d %H:%M:%S')\n if not data:\n print(\"client has exist\")\n break\n data_analysised = self.ais_bm_analysis(data.decode())\n if data_analysised:\n if len(data_analysised) == 9:\n self.ais_dynamic_mysql(data_analysised, create_time, 1)\n else:\n self.ais_static_mysql(data_analysised, create_time)\n except Exception as e:\n if 'PRIMARY' in str(e):\n pass\n else:\n print(e)\n\n def ais_message_main(self, ais_org_list):\n \"\"\"\n 解析ais报文主函数\n :return:\n \"\"\"\n ais_list = []\n for tmp_string in ais_org_list:\n try:\n pass\n except Exception as e:\n print(\"==============\")\n print(e)\n print(\"==============\")\n # print(e)\n return ais_list\n\nif __name__ == \"__main__\":\n aisMes = AisMes()\n aisMes.get_ais_org_bm()\n # ais_df = pd.DataFrame(ais_list, columns=['longitude', 'latitude'])\n # ais_df = ais_df[(ais_df['longitude'] <= 180.0) & (ais_df['longitude'] >= -180.0) &\n # (ais_df['latitude'] <= 90.0) & (ais_df['latitude'] >= -90.0)]\n # ais_df.to_csv('/home/qiu/Documents/ys_ais/ysAIS_test.csv', index=None)\n","sub_path":"ais_message/upd_ais.py","file_name":"upd_ais.py","file_ext":"py","file_size_in_byte":7107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"362274531","text":"import sys\nsys.stdin = open('베이비진_input.txt')\n\ndef chk_run(lst):\n for i in range(len(lst)-2):\n if lst.count(lst[i]+1):\n if lst.count(lst[i]+2):\n return True\n return False\n\ndef chk_triplet(lst):\n for i in range(len(lst)-2):\n if lst[i] == lst[i+1] == lst[i+2]:\n return True\n return False\n\nT = int(input())\n\nfor tc in range(1,T+1):\n card = list(map(int, input().split()))\n player1 = []\n player2 = []\n for i in range(0,12,2):\n player1.append(card[i])\n player2.append(card[i+1])\n player1.sort()\n player2.sort()\n if i >= 4:\n if chk_run(player1) or chk_triplet(player1):\n print(\"#{} 1\".format(tc))\n break\n elif chk_run(player2) or chk_triplet(player2):\n print(\"#{} 2\".format(tc))\n break\n else: print(\"#{} {}\".format(tc, 0))","sub_path":"online/베이비진.py","file_name":"베이비진.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"479613098","text":"# -*- coding: utf-8 -*-\n\n__author__ = 'Wang Chao'\n__date__ = '2/25/14'\n\nimport sys\n\nfrom core.character import Char\nfrom core.msgpipe import publish_to_char\nfrom core.activeplayers import ActivePlayers\nfrom core.exception import SanguoException\n\nfrom utils import pack_msg\nfrom utils.api import api_system_broadcast_get, APIFailure\nfrom preset.settings import CHAT_MESSAGE_MAX_LENGTH\nfrom preset import errormsg\n\nfrom protomsg import ChatMessageNotify, BroadcastNotify\n\n\nclass ChatMessagePublish(object):\n __slots__ = ['char_id', 'cache_char']\n def __init__(self, char_id):\n self.char_id = char_id\n self.cache_char = Char(char_id).cacheobj\n\n def check(self, text):\n if len(text) > CHAT_MESSAGE_MAX_LENGTH:\n raise SanguoException(\n errormsg.CHAT_MESSAGE_TOO_LONG,\n self.char_id,\n \"Chat\",\n \"message too long\"\n )\n\n def to_char(self, target_char_id, text, check=True):\n if check:\n self.check(text)\n msg = ChatMessageNotify()\n chat_msg = msg.msgs.add()\n chat_msg.char.id = self.cache_char.id\n chat_msg.char.name = self.cache_char.name\n chat_msg.char.official = self.cache_char.official\n chat_msg.msg = text\n publish_to_char(target_char_id, pack_msg(msg))\n\n\n def to_server(self, text):\n self.check(text)\n ap = ActivePlayers()\n active_list = ap.get_list()\n for cid in active_list:\n self.to_char(cid, text, check=False)\n\n\nclass SystemBroadcast(object):\n def __init__(self, char_id):\n self.char_id = char_id\n\n def _fill_up_msg(self, msg, text, repeated_times):\n m = msg.msgs.add()\n m.text = text\n m.repeated_times = repeated_times\n\n def send_global_broadcast(self):\n try:\n data = api_system_broadcast_get({})\n except APIFailure:\n sys.stderr.write(\"API_SYSTEM_BROADCAST_GET FAILURE\\n\\n\")\n return\n\n msg = BroadcastNotify()\n for item in data['data']:\n self._fill_up_msg(msg, item['content'], item['play_times'])\n\n publish_to_char(self.char_id, pack_msg(msg))\n\n def send_server_broadcast(self, text, repeated_times):\n pass\n\n","sub_path":"sanguo/core/msgpublish.py","file_name":"msgpublish.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"627994806","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 12 13:49:48 2018\n\n@author: snoopyknight\n\"\"\"\n\nfrom bs4 import BeautifulSoup\nimport requests \nimport pandas as pd\nfrom pandas import Series\nimport numpy as np\nfrom multiprocessing import Process\nimport time\n\ndef main():\n start = 37930 \n end = 38943\n \n payload = {\n 'from':'/bbs/Gossiping/index.html',\n 'yes':'yes'\n }\n \n rs = requests.session()\n res = rs.post('https://www.ptt.cc/ask/over18', data = payload) \n# =============================================================================\n# res = rs.get('https://www.ptt.cc/bbs/Gossiping/index.html')\n# soup = BeautifulSoup(res.text,'lxml')\n# data = soup.find_all('div','r-ent')\n# for r_ent in data:\n# title = r_ent.find('div','title').text\n# print(title)\n# =============================================================================\n article =[]\n link = []\n for i in range(start,end):\n url = 'https://www.ptt.cc/bbs/Gossiping/index' + str(i) + '.html'\n res = rs.get(url)\n soup = BeautifulSoup(res.text,'lxml')\n data = soup.find_all('div','r-ent')\n for r_ent in data:\n title = r_ent.find('div','title').text\n if(title.find(u\"柯文哲\") > 0):\n for a in r_ent.find_all('a', href=True): #ignore the articles which doesn't exist \n #print(a['href'])\n article.append(title)\n link.append(a['href'])\n \n \n df = pd.DataFrame({'article':article, 'link':link})\n \n #print(df.head())\n #print(df.article[1])\n \n df['push_content'] = Series(np.random.randn(len(df.article)), index=df.index)\n for i in range(len(df.article)):\n each_page = 'https://www.ptt.cc' + str(link[i])\n print(df.article[i])\n #print(each_page)\n res2 = rs.get(each_page)\n soup = BeautifulSoup(res2.text,'lxml')\n push = soup.find_all('div','push') \n \n tmp_list = []\n for p in push: \n push_content = p.find('span','f3 push-content').text\n tmp_list.append(push_content)\n #print(len(tmp_list[1]))\n df.push_content[i] = tmp_list\n# =============================================================================\n# for i in range(len(df.article)): \n# df.push_content[i] = tmp_list[i]\n# =============================================================================\n \n df.to_csv('title.csv')\n \nif __name__ == '__main__':\n start = time.time() \n p = Process(target = main)\n p.start()\n p.join()\n end = time.time() \n print(\"ececution time : \",end - start)\n","sub_path":"craw.py","file_name":"craw.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"81223773","text":"__author__ = 'zhy'\n\n\nclass Solution(object):\n @staticmethod\n def countAndSay(n):\n \"\"\"\n :type n: int\n :rtype: str\n \"\"\"\n\n def count(k):\n res, slow, prev = '', 0, k[0]\n for inx, value in enumerate(k):\n if value != prev:\n res += str(inx - slow) + str(prev)\n slow, prev = inx, value\n res += str(len(k) - slow) + k[-1]\n return res\n\n if n <= 0:\n return None\n prev, cur = None, 1\n for i in range(1, n):\n prev = cur\n cur = count(str(prev))\n return str(cur)\n\n\ndef test():\n a = [-1, 0, 1, 2, 3, 4, 5, 10, 20]\n for i in a:\n print(Solution.countAndSay(i))\n\n\nif __name__ == '__main__':\n test()\n","sub_path":"Count and Say.py","file_name":"Count and Say.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"95580197","text":"from management.general import get_credits, set_credits\nfrom management.items import item_to_int\nimport config\nimport sqlite3\n\nconn = sqlite3.connect(config.general_database)\nc = conn.cursor()\n\ndef give_item(user_id,item,amount=-1):\n try:\n number = int(item)\n except ValueError:\n number = item_to_int(item)\n if number == None:\n print('Failed to update inventory!')\n return None\n \n if number == 0:\n return set_credits(user_id,amount)\n\n c.execute(\"SELECT * FROM 'inventory' WHERE id=? AND item=?\",(user_id,number))\n\n if c.fetchone() == None:\n c.execute(\"INSERT INTO 'inventory'('id','item','amount') VALUES (?,?,?);\",(user_id,number,amount))\n else:\n c.execute(\"UPDATE 'inventory' SET amount = amount + ? WHERE item=?\",(amount,number))\n c.execute(\"DELETE FROM 'inventory' WHERE amount =0\")\n conn.commit()\n\ndef has_item(user_id,item,return_bool=True):\n try:\n number = int(item)\n except ValueError:\n number = item_to_int(item)\n if number == None:\n print('Failed to update inventory!')\n if return_bool:\n return False\n return 0\n\n if number == 0:\n return get_credits(user_id)\n\n c.execute(\"SELECT * FROM 'inventory' WHERE id=? AND item=?\",(user_id,number))\n\n result = c.fetchone()\n if return_bool:\n if result == None:\n return False\n return True\n if result == None:\n return 0\n return int(result[2])\n","sub_path":"old-management/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"572228467","text":"import mass_nums\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef plt_rfg(rad):\n fig = plt.figure(figsize=plt.figaspect(1))\n ax = fig.add_subplot(111, projection='3d')\n u = np.linspace(0, 2 * np.pi, 100)\n v = np.linspace(0, np.pi, 100)\n x = rad * np.outer(np.cos(u), np.sin(v))\n y = rad * np.outer(np.sin(u), np.sin(v))\n z = rad * np.outer(np.ones_like(u), np.cos(v))\n ax.plot_surface(x, y, z, rstride=4, cstride=4, color='b')\n max_radius = max(rad, rad, rad)\n for axis in 'xyz':\n getattr(ax, 'set_{}lim'.format(axis))((-max_radius, max_radius))\n plt.show()\n","sub_path":"modules/graphing.py","file_name":"graphing.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"203842558","text":"# -*- coding:utf-8 -*-\r\nfrom xml.etree.ElementTree import Element, SubElement\r\nfrom pandas.core.dtypes.missing import isnull\r\nfrom xml.etree import ElementTree\r\nfrom bs4 import BeautifulSoup\r\nfrom xml.dom import minidom\r\nimport requests,codecs\r\n\r\ndef prettify(elem):\r\n \"\"\"Return a pretty-printed XML string for the Element.\r\n \"\"\"\r\n rough_string = ElementTree.tostring(elem, 'utf-8')\r\n reparsed = minidom.parseString(rough_string)\r\n return reparsed.toprettyxml(indent=\" \")\r\n\r\nDate_prefix = u\"最近更新日期:\"\r\ndateUpdated = \"\"\r\nlabels = [s.strip() for s in [\"代號\",\"名稱 \",\"ISIN\",\"上市日\",\" 市場\",\" 產業別\",\"CFI\"]]\r\nURL = \"https://isin.twse.com.tw/isin/C_public.jsp?strMode=2\" #本國上市證券國際證券辨識號碼一覽表\r\nr = requests.get(URL, allow_redirects=True)\r\nif r.status_code != 200:\r\n exit(\"\".join([\"無法存取網頁:\" , URL]))\r\nwebpage = r.text.encode('utf-8')\r\n# webpage = open(\"C_public.html\",\"r\").read()\r\nif isnull(webpage):\r\n exit(\"無法擷取來源\")\r\n\r\ntop = Element('top')\r\n\r\nsoup = BeautifulSoup(webpage,'html.parser') # encode as utf-8 is very important\r\ntags = soup.find(\"font\",class_=\"h1\") # get title\r\nif tags:\r\n title = SubElement(top, 'title')\r\n title.text = tags.text.strip()\r\n \r\ntags = soup.find_all('center',limit=2) # date is contained in one of
    tags \r\nif tags:\r\n for t in tags:\r\n if t.text.startswith(Date_prefix): # date is prefixed with Date_prefix\r\n dateUpdated = t.text.split(\":\")[-1] # date is behind \":\"\r\n datelisted = SubElement(top, 'dateUpdated')\r\n datelisted.text = dateUpdated.strip()\r\n break\r\n\r\ntry:\r\n rows = soup.find('tr')\r\n rows = rows.next_sibling #ignore the 1st row\r\nexcept:\r\n exit(\"Can't catch stock table\")\r\n\r\nnewSerurityType = True\r\nwhile (rows):\r\n newSerurityType = rows.td.has_attr(\"colspan\")\r\n if (newSerurityType):\r\n serurityType = SubElement(top, 'serurityType',id = rows.td.text.strip())\r\n else:\r\n properties = \" \".join([s for s in rows.strings]).split()\r\n security = SubElement(serurityType, 'security')\r\n for i in range(0,len(properties)):\r\n f = SubElement(security, 'property', id = labels[i])\r\n f.text = properties[i]\r\n rows = rows.next_sibling\r\n \r\nwith codecs.open(\"output.xml\",'w','utf-8') as f:\r\n f.write(prettify(top))\r\n f.close()","sub_path":"Finmoon/StockList.py","file_name":"StockList.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"306808322","text":"from src.setting import Opt, Refr\nimport pygame\n\n\ndef draw_rect(surface, rect, color, thickness=0):\n pygame.draw.rect(surface, color, rect, thickness)\ndef draw_circle(surface, pos, radius, color, thickness=0):\n pygame.draw.circle(surface, color, pos, radius, thickness)\ndef draw_token(surface, pos, color):\n draw_circle(surface,pos,Opt.Token.RADIUS,color)\n\nclass Grill:\n def __init__(self):\n pygame.init()\n self.font_name = pygame.font.match_font(Opt.Font.NAME)\n self.screen = pygame.display.set_mode(Opt.Window.SIZE)\n self.screen.fill(Opt.Colors.BACKGROUND)\n self.mount_under()\n self.mount_upper()\n self.screen.blit(self.under_screen, (0,0))\n self.screen.blit(self.upper_screen, (0,0))\n self.tokens = pygame.sprite.Group()\n pygame.display.flip()\n\n def mount_under(self):\n self.under_screen = pygame.Surface(Opt.Window.SIZE)\n self.under_screen.set_colorkey(Opt.Colors.KEY)\n draw_rect(self.under_screen,Opt.Window.RECT, Opt.Colors.EMPTY)\n\n def mount_upper(self):\n self.upper_screen = pygame.Surface(Opt.Window.SIZE)\n self.upper_screen.set_colorkey(Opt.Colors.KEY)\n draw_rect(self.upper_screen,Opt.Window.RECT, Opt.Colors.GRILL)\n for y in range(Opt.Window.NY):\n for x in range(Opt.Window.NX):\n center = (int(x*Opt.Token.SIZE + Opt.Window.MARGIN + Opt.Token.SIZE/2),\n int(y*Opt.Token.SIZE + Opt.Window.MARGIN + Opt.Token.SIZE/2))\n draw_token(self.upper_screen,center,Opt.Colors.KEY)\n\n def get_position(self):\n key = None\n while not key in list(range(7)):\n for event in pygame.event.get():\n if event.type == pygame.KEYDOWN:\n key = event.key - 49\n elif event.type == pygame.QUIT:\n return Refr.QUIT\n return key\n\n def token(self, turn, x, y):\n token = Token(turn,x,y)\n self.tokens.add(token)\n while token.rect.y < token.desty:\n now = pygame.time.get_ticks()\n if now - token.last_move > token.speed:\n token.rect.y += 10\n self.screen.fill(Opt.Colors.BACKGROUND)\n self.screen.blit(self.under_screen,(0,0))\n self.tokens.draw(self.screen)\n self.screen.blit(self.upper_screen,(0,0))\n self.last_move = now\n pygame.display.flip()\n\n\nclass Token(pygame.sprite.Sprite):\n def __init__(self,turn,x,y):\n pygame.sprite.Sprite.__init__(self)\n if turn == Refr.PLAYER: self.color = Opt.Colors.PLAYER\n elif turn == Refr.COMPUTER: self.color = Opt.Colors.COMPUTER\n self.desty = y * Opt.Token.SIZE + Opt.Window.MARGIN\n self.image = pygame.Surface((Opt.Token.SIZE,Opt.Token.SIZE))\n draw_token(self.image,(Opt.Token.SIZE//2,Opt.Token.SIZE//2),self.color)\n self.image.set_colorkey(Opt.Colors.KEY)\n self.rect = self.image.get_rect()\n self.rect.x = int(x * Opt.Token.SIZE + Opt.Window.MARGIN)\n self.rect.y = -Opt.Token.SIZE\n self.speed = Opt.Token.SPEED\n self.last_move = 0\n","sub_path":"src/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"369637974","text":"#coding:utf-8\nimport random\nimport pickle\nimport sys\n\ndef get_entier():\n\tentier = 0\n\tboucle = True\n\twhile boucle:\n\t\ttry:\n\t\t\tentier = int(input(\"entre ton choix (chiffres) -> \"))\n\t\texcept ValueError:\n\t\t\tprint(\"il faut entrer un nombre !\")\n\t\t\tcontinue\n\t\texcept KeyboardInterrupt:\n\t\t\tprint(\"raccourcie clavier !\")\n\t\t\tprint(\"fin du programme..\")\n\t\t\tsys.exit(0)\n\t\telse:\n\t\t\treturn entier\n\t\t\tboucle = False\n\ndef melanger_str(motMystere):\n\tmotMystere.strip()\n\ti = 0\n\tmelange = ''\n\twhile i < len(motMystere):\n\t\tnbre_alea = random.randint(0,len(motMystere) - 1)\n\t\tmelange += motMystere[nbre_alea]\n\t\tmotMystere = motMystere[:nbre_alea] + motMystere[nbre_alea+1:]\n\treturn melange\n\ndef get_motInDico():\n\twith open(\"includes/dico.txt\",\"r\") as dico: #autre façon d'ouvrir des fichier\n\t\tlisteDico = dico.readlines() # C'EST MIEUX :)\n\t\tnbre_ligne = len(listeDico)\n\t\treturn listeDico[random.randint(0,nbre_ligne)].strip()\n\n\n\nif __name__ == '__main__':\n\tget_motInDico()\n","sub_path":"projet/projet 2 - devine mot/v1/includes/fonctions.py","file_name":"fonctions.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"174865557","text":"\nclass Solution:\n def reverse(self, x):\n x_list = list(str(x))\n tem_list = []\n is_minus = False\n\n while x_list:\n element = x_list.pop()\n if element == '-':\n is_minus = True\n continue;\n else:\n tem_list.append(element)\n result = int(''.join(tem_list))\n\n if is_minus:\n result *= -1\n\n result_max = 0xffffffff/2\n if result < (result_max*-1) or result > (result_max-1):\n result = 0\n\n return result\n\n\n# x = 123\n# x_list = list(str(x))\n# print(\"x_list\", x_list)\n# ssss = []\n# while x_list:\n# ssss.append(x_list.pop())\n# print(\"ssss\", ssss)\n#\n# rrr = ''.join(ssss)\n# print('rrr',rrr)\n\nif __name__ == '__main__':\n ss = Solution()\n x = -123\n print(ss.reverse(x))","sub_path":"python/leetcode007.py","file_name":"leetcode007.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"419278472","text":"import httpretty\nimport json\nimport re\n\n\ndef http_mock_hub_syncacl(acl, uri, status=200):\n httpretty.register_uri(\n httpretty.POST, re.compile('{}/api/domains/\\w+/services/syncacl'.format(uri)),\n body=json.dumps(acl),\n status=status,\n content_type='application/json'\n )\n","sub_path":"ulearn/core/tests/mockers.py","file_name":"mockers.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"244325961","text":"import theano.tensor as T\nimport theano\nimport numpy as np\nimport nnb\nimport nnb.cost as cost\nfrom nnb.train import Trainer\n\nclass AdagradTrainer(Trainer):\n reset_history = None\n\n @staticmethod\n def get_options():\n ops = Trainer.get_options()\n ops.add(\n name=\"model\",\n required=True,\n description=\"The model to be trained.\",\n value_type=nnb.Model\n )\n ops.add(\n name=\"L2_reg\",\n value=0.,\n value_type=[float, list],\n description=\"L2 regularization values. It can be a float that \" + \\\n \"will be applied for all parameters or a list of \" + \\\n \"floats, one for each parameter of the model.\"\n )\n ops.add(\n name=\"L1_reg\",\n value=0.,\n value_type=[float, list],\n description=\"L1 regularization values. It can be a float that \" + \\\n \"will be applied for all parameters or a list of \" + \\\n \"floats, one for each parameter of the model.\"\n )\n ops.add(\n name=\"cost_func\",\n value=cost.mean_square_error,\n description=\"Cost function to be applied to the model's output \" + \\\n \"and expected output.\"\n )\n ops.add(\n name=\"hist\",\n value_type=list,\n description=\"Starting adagrad history\"\n )\n ops.add(\n name=\"learning_rate\",\n value=0.1,\n value_type=float,\n description=\"Learning rate used for the training\"\n )\n return ops\n\n def setup(self):\n options = self.options\n model = options.get('model')\n\n inputs, output = model.get_io()\n t = T.TensorType(output.dtype, (False,) * output.ndim)\n expected_output = t('expected_output')\n\n cost_func = options.get('cost_func')\n cost = cost_func(output, expected_output)\n\n params = model.params\n\n L2_reg = options.get('L2_reg')\n\n if isinstance(L2_reg, float):\n L2_reg = [L2_reg for p in params]\n\n L1_reg = options.get('L1_reg')\n\n if isinstance(L1_reg, float):\n L1_reg = [L1_reg for p in params]\n\n for l1, l2, param in zip(L1_reg, L2_reg, params):\n cost += abs(param).sum() * l1\n cost += T.sqr(param).sum() * l2\n\n params_grads = [T.grad(cost=cost, wrt=param) for param in params]\n\n adagrad_hist = options.get('hist')\n if adagrad_hist is None:\n adagrad_hist = [\n np.zeros_like(param.get_value(borrow=True))\n for param in params\n ]\n adagrad_hist = [\n theano.shared(\n hist,\n name=\"adagrad_hist_{0}\".format(param),\n borrow=True\n ) for hist, param in zip(adagrad_hist, params)\n ]\n\n new_hist = [ah + T.sqr(param_g)\n for ah, param_g in zip(adagrad_hist, params_grads)]\n\n new_grad = [grad / (1e-6 + T.sqrt(ah))\n for grad, ah in zip(params_grads, new_hist)]\n\n learning_rate = options.get('learning_rate')\n\n import collections\n updates = collections.OrderedDict()\n for param, ng in zip(params, new_grad):\n updates[param] = param - learning_rate * ng\n\n for hist, nh in zip(adagrad_hist, new_hist):\n updates[hist] = nh\n\n adagrad_reset_update = [(hist, T.zeros_like(hist))\n for hist in adagrad_hist]\n\n self.reset_history = theano.function(\n inputs=[],\n outputs=None,\n updates=adagrad_reset_update\n )\n\n\n all_ = inputs + [expected_output]\n\n self.__train = theano.function(all_, [], updates=updates)\n self.__get_grads = theano.function(all_, params_grads)\n self.__train_with_grads = theano.function(params_grads, [],\n updates=updates)\n\n def train(self, inputs, expected_outputs):\n options = self.options\n model = options.get('model')\n\n grads = [np.zeros_like(param.get_value(borrow=True)) \n for param in model.params]\n for inp, outp in zip(inputs, expected_outputs):\n a = list(inp) + [outp]\n grads_i = self.__get_grads(*a)\n for g, gi in zip(grads, grads_i):\n g += gi\n\n for g in grads:\n g /= len(inputs)\n self.__train_with_grads(*grads)\n","sub_path":"nnb/train/adagrad.py","file_name":"adagrad.py","file_ext":"py","file_size_in_byte":4591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"404347830","text":"# Author: Roi Yehoshua\n# Date: July 2020\nfrom collections import defaultdict\n\nclass TDPrediction():\n \"\"\"Temporal difference prediction for estimating the state value function\"\"\"\n def __init__(self, env, policy, gamma, alpha, n_episodes, max_episode_len=None):\n \"\"\"\n :param env: an instance of gym environment\n :param policy: an object that implements a get_action() method\n :param gamma: the discount factor\n :param alpha: learning rate\n :param n_episodes: number of episodes to sample\n :param max_episode_len: maximum number of steps per episode\n \"\"\"\n self.env = env\n self.policy = policy\n self.gamma = gamma\n self.alpha = alpha\n self.n_episodes = n_episodes\n self.max_episode_len = max_episode_len\n\n self.V = defaultdict(lambda: 0) # the value function\n\n def estimate_value(self):\n \"\"\"Estimate the state value function of the policy\n :return: the value function\n \"\"\"\n for episode in range(self.n_episodes):\n done = False\n step = 0\n\n state = self.env.reset()\n while not done:\n action = self.policy.get_action(state)\n next_state, reward, done, _ = self.env.step(action)\n self.update_v(state, reward, next_state)\n state = next_state\n\n step += 1\n if self.max_episode_len and step > self.max_episode_len:\n break\n\n # Print out which episode we're on\n if (episode + 1) % 1000 == 0:\n print(f'\\rEpisode {episode + 1}/{self.n_episodes}', end='')\n return self.V\n\n def update_v(self, state, reward, next_state):\n \"\"\"Update the V table using the given the current transition\n :param\n \"\"\"\n self.V[state] += self.alpha * (reward + self.gamma * self.V[next_state] - self.V[state])\n","sub_path":"chapter06/td_prediction.py","file_name":"td_prediction.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"494106749","text":"'''\nWritten by Will Drevno, IEOR 180, 2013\n'''\nimport math\n \ndef distance(point_A, point_B):\n lat1, lon1 = point_A\n lat2, lon2 = point_B\n radius = 3963.1676 # miles\n dlat = math.radians(lat2-lat1)\n dlon = math.radians(lon2-lon1)\n a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \\\n * math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n dist = radius * c\n return dist","sub_path":"latlong.py","file_name":"latlong.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"329860665","text":"\"\"\"\nSkills test module.\n\"\"\"\nfrom unittest import skip\n\nfrom django.test import TestCase\nfrom evennia.utils.test_resources import EvenniaTest\nfrom typeclasses.characters import Character\nfrom world.skills import load_skill, ALL_SKILLS\nfrom world.archetypes import PRIMARY_TRAITS\n\n\nclass LoadSkillTestCase(TestCase):\n \"\"\"Test case for the `load_skill` module function.\"\"\"\n def test_load_skill(self):\n print(ALL_SKILLS)\n for name in ALL_SKILLS:\n s = load_skill(name)\n self.assertEqual(s.name.lower(), name)\n self.assertIn(s.base, PRIMARY_TRAITS)\n\n@skip(\"Skip until archtypes are refactored\")\nclass CharSkillsTestCase(EvenniaTest):\n \"\"\"Test case for module functions that operate on characters.\"\"\"\n def setUp(self):\n self.character_typeclass = Character\n super(CharSkillsTestCase, self).setUp()\n archetypes.apply_archetype(self.char1, 'warrior')\n tr = self.char1.traits\n tr.STR.base += 2\n tr.PER.base += 1\n tr.INT.base += 1\n tr.DEX.base += 1\n tr.CHA.base += 1\n tr.VIT.base += 2\n\n def test_apply_skills(self):\n \"\"\"test module function `apply_skills`\"\"\"\n skills.apply_skills(self.char1)\n sk = self.char1.skills\n self.assertEqual(sk.escape.actual, 8)\n self.assertEqual(sk.climb.actual, 8)\n self.assertEqual(sk.jump.actual, 8)\n self.assertEqual(sk.lockpick.actual, 2)\n self.assertEqual(sk.listen.actual, 2)\n self.assertEqual(sk.sense.actual, 2)\n self.assertEqual(sk.appraise.actual, 2)\n self.assertEqual(sk.medicine.actual, 2)\n self.assertEqual(sk.survival.actual, 2)\n self.assertEqual(sk.balance.actual, 5)\n self.assertEqual(sk.sneak.actual, 5)\n self.assertEqual(sk.throwing.actual, 5)\n self.assertEqual(sk.animal.actual, 5)\n self.assertEqual(sk.barter.actual, 5)\n self.assertEqual(sk.leadership.actual, 5)\n\n def test_validate_skills(self):\n \"\"\"test module function `apply_skills`\"\"\"\n skills.apply_skills(self.char1)\n # not\n self.assertFalse(skills.validate_skills(self.char1)[0])\n self.assertIn('Not enough -1',\n skills.validate_skills(self.char1)[1])\n sk = self.char1.skills\n sk.escape.minus += 1\n sk.climb.minus += 1\n sk.jump.minus += 1\n sk.medicine.plus += 1\n self.assertTrue(skills.validate_skills(self.char1)[0])\n sk.appraise.plus += 1\n self.assertFalse(skills.validate_skills(self.char1)[0])\n self.assertIn('Not enough +1',\n skills.validate_skills(self.char1)[1])\n\n def test_finalize_skills(self):\n \"\"\"test module function `finalize_skills`\"\"\"\n skills.apply_skills(self.char1)\n # allocate skills for char1\n sk = self.char1.skills\n sk.escape.minus += 1\n sk.climb.minus += 1\n sk.jump.minus += 1\n sk.medicine.plus += 1\n skills.finalize_skills(sk)\n # confirm the plusses and minuses are applied\n self.assertEqual(sk.escape.actual, 7)\n self.assertEqual(sk.climb.actual, 7)\n self.assertEqual(sk.jump.actual, 7)\n self.assertEqual(sk.medicine.actual, 3)\n # confirm plus/minus counters are deleted\n with self.assertRaises(AttributeError):\n x = sk.escape.plus\n with self.assertRaises(AttributeError):\n x = sk.escape.minus\n","sub_path":"world/test_skills.py","file_name":"test_skills.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"290121335","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.5 (62131)\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-i686/egg/flexirest/meta.py\n# Compiled at: 2009-11-13 08:21:32\nSHORT_NAME = 'flexirest'\nCMDLINE_DESC = 'The medium-featured, flexible reStructuredText utility'\nVERSION = '0.8.2'\nURL = 'http://www.aspektratio.net/flexirest'\nAUTHOR = 'Jacob Oscarson'\nEMAIL = 'jacob@aspektratio.net'\nSHORT_DESC = CMDLINE_DESC\nCMDLINE_USAGE = 'flexirest '","sub_path":"pycfiles/flexirest-0.8.2-py2.5/meta.py","file_name":"meta.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"51470234","text":"r\"\"\"General purpose tensor functionals\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom typing import List, Tuple #, Union\n\n\ndef channel_conv(\n x: torch.Tensor,\n kernel: torch.Tensor,\n padding: int = 0, # Union[int, Tuple[int, ...]]\n) -> torch.Tensor:\n r\"\"\"Returns the channel-wise convolution of \\(x\\) with the kernel `kernel`.\n\n Args:\n x: A tensor, \\((N, C, *)\\).\n kernel: A kernel, \\((C', 1, *)\\).\n padding: The implicit paddings on both sides of the input dimensions.\n\n Example:\n >>> x = torch.arange(25, dtype=torch.float).view(1, 1, 5, 5)\n >>> x\n tensor([[[[ 0., 1., 2., 3., 4.],\n [ 5., 6., 7., 8., 9.],\n [10., 11., 12., 13., 14.],\n [15., 16., 17., 18., 19.],\n [20., 21., 22., 23., 24.]]]])\n >>> kernel = torch.ones((1, 1, 3, 3))\n >>> channel_conv(x, kernel)\n tensor([[[[ 54., 63., 72.],\n [ 99., 108., 117.],\n [144., 153., 162.]]]])\n \"\"\"\n\n return F.conv1d(x, kernel, padding=padding, groups=x.size(1))\n\n\ndef channel_convs(\n x: torch.Tensor,\n kernels: List[torch.Tensor],\n padding: int = 0, # Union[int, Tuple[int, ...]]\n) -> torch.Tensor:\n r\"\"\"Returns the channel-wise convolution of \\(x\\) with\n the series of kernel `kernels`.\n\n Args:\n x: A tensor, \\((N, C, *)\\).\n kernels: A list of kernels, each \\((C', 1, *)\\).\n padding: The implicit paddings on both sides of the input dimensions.\n\n Example:\n >>> x = torch.arange(25, dtype=torch.float).view(1, 1, 5, 5)\n >>> x\n tensor([[[[ 0., 1., 2., 3., 4.],\n [ 5., 6., 7., 8., 9.],\n [10., 11., 12., 13., 14.],\n [15., 16., 17., 18., 19.],\n [20., 21., 22., 23., 24.]]]])\n >>> kernels = [torch.ones((1, 1, 3, 1)), torch.ones((1, 1, 1, 3))]\n >>> channel_convs(x, kernels)\n tensor([[[[ 54., 63., 72.],\n [ 99., 108., 117.],\n [144., 153., 162.]]]])\n \"\"\"\n\n if padding > 0:\n pad = (padding,) * (2 * x.dim() - 4)\n x = F.pad(x, pad=pad)\n\n for k in kernels:\n x = channel_conv(x, k)\n\n return x\n\n\ndef gaussian_kernel(\n size: int,\n sigma: float = 1.\n) -> torch.Tensor:\n r\"\"\"Returns the 1-dimensional Gaussian kernel of size \\(K\\).\n\n $$ G(x) = \\frac{1}{\\sum_{y = 1}^{K} G(y)} \\exp\n \\left(\\frac{(x - \\mu)^2}{2 \\sigma^2}\\right) $$\n\n where \\(x \\in [1; K]\\) is a position in the kernel\n and \\(\\mu = \\frac{1 + K}{2}\\).\n\n Args:\n size: The kernel size \\(K\\).\n sigma: The standard deviation \\(\\sigma\\) of the distribution.\n\n Returns:\n The kernel vector, \\((K,)\\).\n\n Note:\n An \\(N\\)-dimensional Gaussian kernel is separable, meaning that\n applying it is equivalent to applying a series of \\(N\\) 1-dimensional\n Gaussian kernels, which has a lower computational complexity.\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Gaussian_blur\n\n Example:\n >>> gaussian_kernel(5, sigma=1.5)\n tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201])\n \"\"\"\n\n kernel = torch.arange(size, dtype=torch.float)\n kernel -= (size - 1) / 2\n kernel = kernel ** 2 / (2. * sigma ** 2)\n kernel = torch.exp(-kernel)\n kernel /= kernel.sum()\n\n return kernel\n\n\ndef kernel_views(kernel: torch.Tensor, n: int = 2) -> List[torch.Tensor]:\n r\"\"\"Returns the \\(N\\)-dimensional views of the 1-dimensional\n kernel `kernel`.\n\n Args:\n kernel: A kernel, \\((C, 1, K)\\).\n n: The number of dimensions \\(N\\).\n\n Returns:\n The list of views, each \\((C, 1, \\underbrace{1, \\dots, 1}_{i}, K,\n \\underbrace{1, \\dots, 1}_{N - i - 1})\\).\n\n Example:\n >>> kernel = gaussian_kernel(5, sigma=1.5).repeat(3, 1, 1)\n >>> kernel.size()\n torch.Size([3, 1, 5])\n >>> views = kernel_views(kernel, n=2)\n >>> views[0].size(), views[1].size()\n (torch.Size([3, 1, 5, 1]), torch.Size([3, 1, 1, 5]))\n \"\"\"\n\n if n == 1:\n return [kernel]\n elif n == 2:\n return [kernel.unsqueeze(-1), kernel.unsqueeze(-2)]\n\n # elif n > 2:\n c, _, k = kernel.size()\n\n shape: List[int] = [c, 1] + [1] * n\n views = []\n\n for i in range(2, n + 2):\n shape[i] = k\n views.append(kernel.view(shape))\n shape[i] = 1\n\n return views\n\n\ndef haar_kernel(size: int) -> torch.Tensor:\n r\"\"\"Returns the horizontal Haar kernel.\n\n Args:\n size: The kernel (even) size \\(K\\).\n\n Returns:\n The kernel, \\((K, K)\\).\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Haar_wavelet\n\n Example:\n >>> haar_kernel(2)\n tensor([[ 0.5000, -0.5000],\n [ 0.5000, -0.5000]])\n \"\"\"\n\n return torch.outer(\n torch.ones(size) / size,\n torch.tensor([1., -1.]).repeat_interleave(size // 2),\n )\n\n\ndef prewitt_kernel() -> torch.Tensor:\n r\"\"\"Returns the Prewitt kernel.\n\n Returns:\n The kernel, \\((3, 3)\\).\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Prewitt_operator\n\n Example:\n >>> prewitt_kernel()\n tensor([[ 0.3333, 0.0000, -0.3333],\n [ 0.3333, 0.0000, -0.3333],\n [ 0.3333, 0.0000, -0.3333]])\n \"\"\"\n\n return torch.outer(\n torch.tensor([1., 1., 1.]) / 3,\n torch.tensor([1., 0., -1.]),\n )\n\n\ndef sobel_kernel() -> torch.Tensor:\n r\"\"\"Returns the Sobel kernel.\n\n Returns:\n The kernel, \\((3, 3)\\).\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Sobel_operator\n\n Example:\n >>> sobel_kernel()\n tensor([[ 0.2500, 0.0000, -0.2500],\n [ 0.5000, 0.0000, -0.5000],\n [ 0.2500, 0.0000, -0.2500]])\n \"\"\"\n\n return torch.outer(\n torch.tensor([1., 2., 1.]) / 4,\n torch.tensor([1., 0., -1.]),\n )\n\n\ndef scharr_kernel() -> torch.Tensor:\n r\"\"\"Returns the Scharr kernel.\n\n Returns:\n The kernel, \\((3, 3)\\).\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Scharr_operator\n\n Example:\n >>> scharr_kernel()\n tensor([[ 0.1875, 0.0000, -0.1875],\n [ 0.6250, 0.0000, -0.6250],\n [ 0.1875, 0.0000, -0.1875]])\n \"\"\"\n\n return torch.outer(\n torch.tensor([3., 10., 3.]) / 16,\n torch.tensor([1., 0., -1.]),\n )\n\n\ndef gradient_kernel(kernel: torch.Tensor) -> torch.Tensor:\n r\"\"\"Returns `kernel` transformed into a gradient.\n\n Args:\n kernel: A convolution kernel, \\((K, K)\\).\n\n Returns:\n The gradient kernel, \\((2, 1, K, K)\\).\n\n Example:\n >>> g = gradient_kernel(prewitt_kernel())\n >>> g.size()\n torch.Size([2, 1, 3, 3])\n \"\"\"\n\n return torch.stack([kernel, kernel.t()]).unsqueeze(1)\n\n\ndef tensor_norm(\n x: torch.Tensor,\n dim: List[int], # Union[int, Tuple[int, ...]] = ()\n keepdim: bool = False,\n norm: str = 'L2',\n) -> torch.Tensor:\n r\"\"\"Returns the norm of \\(x\\).\n\n $$ L_1(x) = \\left\\| x \\right\\|_1 = \\sum_i \\left| x_i \\right| $$\n\n $$ L_2(x) = \\left\\| x \\right\\|_2 = \\left( \\sum_i x^2_i \\right)^\\frac{1}{2} $$\n\n Args:\n x: A tensor, \\((*,)\\).\n dim: The dimension(s) along which to calculate the norm.\n keepdim: Whether the output tensor has `dim` retained or not.\n norm: Specifies the norm funcion to apply:\n `'L1'` | `'L2'` | `'L2_squared'`.\n\n Wikipedia:\n https://en.wikipedia.org/wiki/Norm_(mathematics)\n\n Example:\n >>> x = torch.arange(9).float().view(3, 3)\n >>> x\n tensor([[0., 1., 2.],\n [3., 4., 5.],\n [6., 7., 8.]])\n >>> tensor_norm(x, dim=0)\n tensor([6.7082, 8.1240, 9.6437])\n \"\"\"\n\n if norm == 'L1':\n x = x.abs()\n else: # norm in ['L2', 'L2_squared']\n x = x ** 2\n\n x = x.sum(dim=dim, keepdim=keepdim)\n\n if norm == 'L2':\n x = x.sqrt()\n\n return x\n\n\ndef normalize_tensor(\n x: torch.Tensor,\n dim: List[int], # Union[int, Tuple[int, ...]] = ()\n norm: str = 'L2',\n epsilon: float = 1e-8,\n) -> torch.Tensor:\n r\"\"\"Returns \\(x\\) normalized.\n\n $$ \\hat{x} = \\frac{x}{\\left\\|x\\right\\|} $$\n\n Args:\n x: A tensor, \\((*,)\\).\n dim: The dimension(s) along which to normalize.\n norm: Specifies the norm funcion to use:\n `'L1'` | `'L2'` | `'L2_squared'`.\n epsilon: A numerical stability term.\n\n Returns:\n The normalized tensor, \\((*,)\\).\n\n Example:\n >>> x = torch.arange(9, dtype=torch.float).view(3, 3)\n >>> x\n tensor([[0., 1., 2.],\n [3., 4., 5.],\n [6., 7., 8.]])\n >>> normalize_tensor(x, dim=0)\n tensor([[0.0000, 0.1231, 0.2074],\n [0.4472, 0.4924, 0.5185],\n [0.8944, 0.8616, 0.8296]])\n \"\"\"\n\n norm = tensor_norm(x, dim=dim, keepdim=True, norm=norm)\n\n return x / (norm + epsilon)\n\n\ndef unravel_index(\n indices: torch.LongTensor,\n shape: List[int],\n) -> torch.LongTensor:\n r\"\"\"Converts flat indices into unraveled coordinates in a target shape.\n\n This is a `torch` implementation of `numpy.unravel_index`.\n\n Args:\n indices: A tensor of (flat) indices, \\((*, N)\\).\n shape: The targeted shape, \\((D,)\\).\n\n Returns:\n The unraveled coordinates, \\((*, N, D)\\).\n\n Example:\n >>> unravel_index(torch.arange(9), shape=(3, 3))\n tensor([[0, 0],\n [0, 1],\n [0, 2],\n [1, 0],\n [1, 1],\n [1, 2],\n [2, 0],\n [2, 1],\n [2, 2]])\n \"\"\"\n\n coord = []\n\n for dim in reversed(shape):\n coord.append(indices % dim)\n indices = indices // dim\n\n coord = torch.stack(coord[::-1], dim=-1)\n\n return coord\n","sub_path":"piqa/utils/functional.py","file_name":"functional.py","file_ext":"py","file_size_in_byte":9886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"566215543","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\ndef sqrt_bisect(n, steps=20, epsilon=10**-5):\n \"\"\"Approximate square root by the bisection method.\"\"\"\n assert n > 0\n assert epsilon > 0\n low = 0\n high = n\n step = 1\n sqrt = (low + high) / 2\n while abs(sqrt**2 - n) > epsilon and step <= steps:\n if sqrt**2 < n:\n low = sqrt\n else:\n high = sqrt\n sqrt = (low + high) / 2\n step += 1\n return sqrt\n\n\ndef sqrt_newton(n, steps=20):\n \"\"\"Approximate square root by Newton's Method.\n \n - Initial guess: old_guess = n / 2\n - Iterations: new_guess = 1/2 * (old_guess + n / old_guess)\n \"\"\"\n sqrt = n / 2\n for step in range(steps):\n sqrt = 1 / 2 * (sqrt + n / sqrt)\n return sqrt\n\n\ndef main():\n import time\n\n n = 16\n\n start_time = time.time()\n print('Find square root of {0} by bisection: {1}'\n .format(n, sqrt_bisect(n, steps=20)))\n print('Time: {}'.format(time.time() - start_time))\n\n start_time = time.time()\n print('Find square root of {0} by Newton method: {1}'\n .format(n, sqrt_newton(n, steps=20)))\n print('Time: {}'.format(time.time() - start_time))\n\n n = 1234567890\n\n start_time = time.time()\n print('Find square root of {0} by bisection: {1}'\n .format(n, sqrt_bisect(n, steps=20)))\n print('Time: {}'.format(time.time() - start_time))\n\n start_time = time.time()\n print('Find square root of {0} by Newton method: {1}'\n .format(n, sqrt_newton(n, steps=20)))\n print('Time: {}'.format(time.time() - start_time))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"alg_sqrt.py","file_name":"alg_sqrt.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"132887667","text":"import numpy as np\nimport pickle\nimport sys\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nimport pdb\nimport data_hdf5 as d\n\n\ndef cond_prob(args):\n labels = ['blow_down',\n 'bare_ground',\n 'conventional_mine',\n 'blooming',\n 'cultivation',\n 'artisinal_mine',\n 'haze',\n 'primary',\n 'slash_burn',\n 'habitation',\n 'clear',\n 'road',\n 'selective_logging',\n 'partly_cloudy',\n 'agriculture',\n 'water',\n 'cloudy']\n\n # Get targets from training data hdf5 file\n reader = d.HDF_line_reader('input/train.h5', load_rgb=False)\n _, targets, file_ids = d.get_all_train(reader=reader)\n LABELS = d.LABELS\n REV_LABELS = {0 : 'blow_down',\n 1: 'bare_ground',\n 2: 'conventional_mine',\n 3: 'blooming',\n 4: 'cultivation',\n 5: 'artisinal_mine',\n 6: 'haze',\n 7: 'primary',\n 8: 'slash_burn',\n 9: 'habitation',\n 10: 'clear',\n 11: 'road',\n 12: 'selective_logging',\n 13: 'partly_cloudy',\n 14: 'agriculture',\n 15: 'water',\n 16: 'cloudy'}\n\n df = pd.DataFrame(np.array(targets), columns=labels)\n\n # Create 2dim co occurence matrix by matrix multiplication\n df_asint = df.astype(int)\n coocc2 = df_asint.T.dot(df_asint)\n\n # Create 3dim co occurence matrix by counting when sums of cols is 3\n coocc3 = np.zeros((len(LABELS), len(LABELS), len(LABELS)))\n for i in tqdm(range(len(LABELS))):\n for j in range(len(LABELS)):\n for k in range(len(LABELS)):\n if not (i == j or j == k or k == i):\n cols = df[[REV_LABELS[i], REV_LABELS[j], REV_LABELS[k]]]\n sums = np.sum(cols.as_matrix(), axis=1)\n coocc3[i, j, k] = np.sum(sums == 3)\n\n # Make probability from counts\n # P(A and B)\n c2 = coocc2.as_matrix()\n np.fill_diagonal(c2, 0)\n s2 = np.sum(c2)\n cooccp2 = coocc2.as_matrix() / s2\n\n # Make probability from counts\n # P(A and B and C)\n s3 = np.sum(coocc3)\n cooccp3 = coocc3 / s3\n\n # Count individual probabilities\n # P(A)\n summat = df_asint.values.sum(axis=0)\n summatp = summat / np.sum(summat)\n\n # Init conditional probability arrays\n condp2 = np.zeros((len(LABELS), len(LABELS)))\n condp3 = np.zeros((len(LABELS), len(LABELS), len(LABELS)))\n\n # For all A\n for i in tqdm(range(len(LABELS))):\n # For all B\n for j in range(len(LABELS)):\n # P(A|B) = P(A and B) / P(B)\n condp2[i, j] = cooccp2[i, j] / summatp[j]\n # For all C\n for k in range(len(LABELS)):\n # P(A|B,C) = P(A and B and C) / P(B and C)\n if cooccp2[j, k] == 0:\n condp3[i, j, k] = 0\n else:\n condp3[i, j, k] = cooccp3[i, j, k] / cooccp2[j, k]\n\n\n print('haldlaslf')\n # Put in pickle\n #pickle.dump(condp2, open('input/condp2.pkl', 'wb'))\n #pickle.dump(condp3, open('input/condp3.pkl', 'wb'))\n\n\nif __name__ == '__main__':\n cond_prob([])\n","sub_path":"cond_prob.py","file_name":"cond_prob.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"534095192","text":"# Copyright 2017: Mirantis Inc.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom rally.common import logging\nfrom rally import consts\nfrom rally.plugins.openstack import osclients\n\nLOG = logging.getLogger(__file__)\n\n\nclass OpenStackCredential(object):\n \"\"\"Credential for OpenStack.\"\"\"\n\n def __init__(self, auth_url, username, password, tenant_name=None,\n project_name=None,\n permission=consts.EndpointPermission.USER,\n region_name=None, endpoint_type=None,\n domain_name=None, endpoint=None, user_domain_name=None,\n project_domain_name=None,\n https_insecure=False, https_cacert=None,\n profiler_hmac_key=None, profiler_conn_str=None):\n self.auth_url = auth_url\n self.username = username\n self.password = password\n self.tenant_name = tenant_name or project_name\n self.permission = permission\n self.region_name = region_name\n self.endpoint_type = endpoint_type\n self.domain_name = domain_name\n self.user_domain_name = user_domain_name\n self.project_domain_name = project_domain_name\n self.endpoint = endpoint\n self.https_insecure = https_insecure\n self.https_cacert = https_cacert\n self.profiler_hmac_key = profiler_hmac_key\n self.profiler_conn_str = profiler_conn_str\n\n self._clients_cache = {}\n\n # backward compatibility\n @property\n def insecure(self):\n LOG.warning(\"Property 'insecure' is deprecated since Rally 0.10.0. \"\n \"Use 'https_insecure' instead.\")\n return self.https_insecure\n\n # backward compatibility\n @property\n def cacert(self):\n LOG.warning(\"Property 'cacert' is deprecated since Rally 0.10.0. \"\n \"Use 'https_cacert' instead.\")\n return self.https_cacert\n\n def to_dict(self):\n return {\"auth_url\": self.auth_url,\n \"username\": self.username,\n \"password\": self.password,\n \"tenant_name\": self.tenant_name,\n \"region_name\": self.region_name,\n \"endpoint_type\": self.endpoint_type,\n \"domain_name\": self.domain_name,\n \"endpoint\": self.endpoint,\n \"https_insecure\": self.https_insecure,\n \"https_cacert\": self.https_cacert,\n \"user_domain_name\": self.user_domain_name,\n \"project_domain_name\": self.project_domain_name,\n \"permission\": self.permission,\n \"profiler_hmac_key\": self.profiler_hmac_key,\n \"profiler_conn_str\": self.profiler_conn_str}\n\n def verify_connection(self):\n if self.permission == consts.EndpointPermission.ADMIN:\n self.clients().verified_keystone()\n else:\n self.clients().keystone()\n\n def list_services(self):\n return sorted([{\"type\": stype, \"name\": sname}\n for stype, sname in self.clients().services().items()],\n key=lambda s: s[\"name\"])\n\n @classmethod\n def get_validation_context(cls):\n return {\"users@openstack\": {}}\n\n def clients(self, api_info=None):\n return osclients.Clients(self, api_info=api_info,\n cache=self._clients_cache)\n","sub_path":"rally/plugins/openstack/credential.py","file_name":"credential.py","file_ext":"py","file_size_in_byte":3886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57326092","text":"# -*- coding: utf-8 -*-\nimport CORE.klasy\nimport os\nfrom posixpath import basename\nimport urllib.parse\nimport CORE.openfile\n\"\"\"\nTen skrypt jest uruchamiany przez skrypt second_script i on odpowiada za usunięcie z tekstu zbędnych znaków(,białe znki itp) jak również za \npodzielenie danego pliku na zdania a dokąłdnie na obiektu klasy Zdanie.\nW funkcji obrob jest najpierw uruchamiana odpowiednia funkcja do otwarcia pliku w zależności od jego typu a potem funkcja podziel.\nw funkcji podziel najpierw usuwamy zbędne elementy a potem dzielimy na zdania, w wyniku otrzymujemy tablicę wyn4, która skłąda się z obiektów klasy Zdanie\nTODO:\n-DONE-napisanie ifów które będą otwierały pliki w zależności od typów. to te same funkcje co w skrypcie linki.py\n\"\"\"\n\ndef podziel(content):\n\tb=list(content);\n\tbb=[];\n\tfor x in b:\n\t\tif (x==' ' or x==',' or x=='\\n' or x=='\\t' or x=='\\f' or x=='\\0' or x=='\\b' or x=='\\r' or x=='\\v'):\n\t\t\tbb.append('ð'); #znak oznaczający miejsca do usunięcia\n\t\telse: \n\t\t\tbb.append(x);\n\tbb.append(' ');\n\tbb.append(' ');\n\tz='';\n\twielkosc=len(bb);\n\tlicznik=1;\t#licznik do numerów zdań\n\tlicz=0;\t\t#licznik do liczenia pozycji nieznaczącego znaku w zdaniu\n\ty=0;\n\tws=[];\t\t# to bedzie tymczasowa tablica gdzie bedę trzymać numery nieznaczących znaków\n\twyn4=[];\t# to bedzie tablica która będize trzymać dany plik podzielony na zdania\n\tfor x in bb:\n\t\tif(x=='ð'):\n\t\t\tws.append(licz);\n\t\t\tlicz+=1;\n\t\t\ty+=1;\n\t\telse:\n\t\t\tlicz+=1;\n\t\t\tif(y|]','_',name)\n\t\t# folder_name = painter_id + '--' + name\n\t\tpainter_name = '--'.join([str(painter_id),str(name)])\n\n\t\t# 避免画师更新名字,进行判断id\n\t\tfor folder in os.listdir(self.path):\n\t\t\tif painter_id == folder.split('--')[0]:\n\t\t\t\tprint(u'[名字叫{}文件夹已存在！]'.format(painter_name))\n\t\t\t\tpainter_path = os.path.join(self.path,folder)\n\t\t\t\tos.chdir(painter_path) # 切换到目录\n\t\t\t\treturn painter_path\n\n\t\tprint(u'[建了一个{}文件夹！]'.format(painter_name))\n\t\tpainter_path = os.path.join(self.path,painter_name)\n\t\tos.makedirs(painter_path)\n\t\tos.chdir(painter_path) # 切换到目录\n\t\treturn painter_path\n\n\tdef mkdir_illusts(self,painter_path,illusts_id):\n\t\t'''\n\t\t创建作品illusts文件夹\n\t\t'''\n\t\tillusts_id_path = os.path.join(painter_path,str(illusts_id))\n\t\tisExists = os.path.exists(illusts_id_path)\n\n\t\tif not isExists:\n\t\t\tprint(u'\\n[在',os.getcwd(),'下建了一个', illusts_id, u'文件夹！]')\n\t\t\tos.makedirs(illusts_id_path)\n\t\t\tos.chdir(illusts_id_path) ##切换到目录\n\t\t\treturn illusts_id_path\n\t\telse:\n\t\t\tprint(u'\\n[在',os.getcwd(),'下已经有', illusts_id, u'文件夹！]')\n\t\t\tos.chdir(illusts_id_path) ##切换到目录\n\t\t\treturn illusts_id_path","sub_path":"v2.0/folder.py","file_name":"folder.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"280301706","text":"class BinarySearchTree:\n def __init__(self) -> None:\n self.tree = None\n\n class Node:\n def __init__(self, data) -> None:\n self.data = data\n self.left = None\n self.right = None\n\n def insert(self, value):\n # 如果是根结点，直接插入\n if self.tree is None:\n self.tree = self.Node(value)\n return\n\n p = self.tree\n while p is not None:\n if value > p.data:\n if p.right is None:\n p.right = self.Node(value)\n return\n p = p.right\n elif value < p.data:\n if p.left is None:\n p.left = self.Node(value)\n return\n p = p.left\n\n def find(self, value):\n p = self.tree\n while p is not None:\n if value > p.data:\n p = p.right\n elif value < p.data:\n p = p.left\n else:\n return p\n return None\n\n def delete(self, value):\n p = self.tree\n pp = None\n while p is not None and p.data != value:\n pp = p\n if value > p.data:\n p = p.right\n elif value < p.data:\n p = p.left\n\n if p is None:\n return\n\n if p.left is not None and p.right is not None:\n tmp_p = p.right\n tmp_pp = p\n # 找要删除结点的右子树中的最小值\n while tmp_p.left is not None:\n tmp_pp = tmp_p\n tmp_p = p.left\n p.data = tmp_p.data\n p = tmp_p\n pp = tmp_pp\n\n if p.left is not None:\n child = p.left\n elif p.right is not None:\n child = p.right\n else:\n child = None\n\n # 删除根结点\n if pp is None:\n self.tree = child\n elif pp.left is p:\n pp.left = child\n elif pp.right is p:\n pp.right = child\n\n def pre_order(self, node):\n if node is None:\n return\n print(node.data)\n self.pre_order(node.left)\n self.pre_order(node.right)\n\n def in_order(self, node):\n if node is None:\n return\n self.in_order(node.left)\n print(node.data)\n self.in_order(node.right)\n\n def post_order(self, node):\n if node is None:\n return\n self.post_order(node.left)\n self.post_order(node.right)\n print(node.data)\n\n\ndef test_binary_search_tree():\n\n binary_search_tree = BinarySearchTree()\n data = [1, 10, 20, 40, 13]\n for i in data:\n binary_search_tree.insert(i)\n assert 20 == binary_search_tree.find(20).data\n binary_search_tree.delete(20)\n assert binary_search_tree.find(20) is None\n # 1 10 40 13\n binary_search_tree.pre_order(binary_search_tree.tree)\n print(\"-----------------------\")\n # 1 10 13 40\n binary_search_tree.in_order(binary_search_tree.tree)\n print(\"-----------------------\")\n # 13 40 10 1\n binary_search_tree.post_order(binary_search_tree.tree)\n\n\n\nif __name__ == '__main__':\n test_binary_search_tree()","sub_path":"test_binary_tree.py","file_name":"test_binary_tree.py","file_ext":"py","file_size_in_byte":3206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"344760750","text":"from boto import kinesis\n#import testdata\n#import json\n\n\nkinesis = kinesis.connect_to_region(\"us-east-1\")\n#kinesis.list_streams()\n\nrecords = []\ncount = 0\nwhile count < 100000:\n x = '2,6002978,4924046,9418438,6949100,71.114.74.69,ge,100,165.00'\n record = {'Data': x,'PartitionKey': str(hash(str(count)))}\n records.append(record)\n if count % 400 == 0:\n kinesis.put_records(records, \"KinesisToLambda\")\n records=[]\n count += 1\nkinesis.put_records(records, \"KinesisToLambda\")\n","sub_path":"Python/python.to.kinesis2.py","file_name":"python.to.kinesis2.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"553192384","text":"from transformers import GPT2LMHeadModel, GPT2Tokenizer\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Assume batch size = 1\n# Problems:\n# 1. Next word prob != Classification prob\n# 2. Negation is not considered (not good v.s. not bad)\n# 3. Top k words don't contain valid words (The chicken tasts + negative)\n\n\nMODEL = 'gpt2-medium'\nDEV = 'cuda'\nTOP_K = 100\nLENGTH = 100\nWEIGHT = 0.4\nREDUCE = 'mean'\n\nCOND = 'positive science'\nCOND = 'negative science'\nCOND = 'positive'\nCOND = 'negative'\nCOND = 'negative politics'\nCOND = 'positive politics'\n\nPREFIX = 'The potato'\nPREFIX = 'The chicken tastes'\nPREFIX = 'To conclude'\n\n\ndef top_k_filtering(logits, top_k=1, filter_value=-float(\"Inf\"), min_tokens_to_keep=1):\n top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))\n ids_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]\n ids_to_retain = torch.topk(logits, top_k)[1][0]\n logits[ids_to_remove] = filter_value\n return logits, ids_to_retain\n\n\ndef conditioning(logprobs, cond_ids, model, input_ids, ids_to_retain):\n input_ids = input_ids.repeat(TOP_K, 1)\n input_ids = torch.cat([input_ids, ids_to_retain.unsqueeze(1)], dim=-1)\n next_logits = model(input_ids)[0][:, -1]\n next_logprobs = F.log_softmax(next_logits, dim=-1)\n if REDUCE == 'max':\n cond_logprobs = torch.max(next_logprobs[:, cond_ids], dim=-1)[0]\n elif REDUCE == 'mean':\n cond_logprobs = torch.mean(next_logprobs[:, cond_ids], dim=-1)\n # cond_logprobs /= (torch.max(cond_logprobs) - torch.min(cond_logprobs))\n # cond_logprobs *= (torch.max(logprobs[:, ids_to_retain]) - torch.min(logprobs[:, ids_to_retain]))\n\n # print(logprobs[:, ids_to_retain])\n # print('-' * 80)\n # print(cond_logprobs)\n # plt.scatter(logprobs[0, ids_to_retain].cpu().numpy(), cond_logprobs.cpu().numpy())\n # plt.show()\n\n logprobs[:, ids_to_retain] += WEIGHT * cond_logprobs\n probs = torch.exp(logprobs)\n return probs\n\n\ntokenizer = GPT2Tokenizer.from_pretrained(MODEL)\nmodel = GPT2LMHeadModel.from_pretrained(MODEL).to(DEV)\nCOND_IDS = tokenizer.encode(COND)\n\ninput_ids = torch.tensor([tokenizer.encode(PREFIX, add_special_tokens=True)]).to(DEV)\n\nfor t in range(LENGTH):\n # print(t, '=' * 80)\n with torch.no_grad():\n # print(tokenizer.decode(input_ids[0]))\n # print('-' * 80)\n logits = model(input_ids)[0][:, -1]\n logits, ids_to_retain = top_k_filtering(logits, TOP_K)\n # for k in range(TOP_K):\n # print(tokenizer.decode([ids_to_retain[k]]), end=' ')\n logprobs = F.log_softmax(logits, dim=-1)\n # r = np.random.randint(0, len(COND_IDS))\n # probs = conditioning(logprobs, COND_IDS, model, input_ids, ids_to_retain)\n probs = conditioning(logprobs, COND_IDS, model, input_ids, ids_to_retain)\n next_tokens = torch.multinomial(probs, num_samples=1)\n input_ids = torch.cat([input_ids, next_tokens], dim=-1)\n\nprint(tokenizer.decode(input_ids[0]))\n","sub_path":"ours/draft.py","file_name":"draft.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"230790308","text":"# Distributed with a free-will license.\r\n# Use it any way you want, profit or free, provided it fits in the licenses of its associated works.\r\n# SHT30\r\n# This code is designed to work with the SHT30_I2CS I2C Mini Module available from ControlEverything.com.\r\n# https://www.controleverything.com/content/Humidity?sku=SHT30_I2CS#tabs-0-product_tabset-2\r\n\r\nimport smbus\r\nimport time\r\n\r\n# Get I2C bus\r\nbus = smbus.SMBus(1)\r\n\r\n# SHT30 address, 0x44(68)\r\n# Send measurement command, 0x2C(44)\r\n#\t\t0x06(06)\tHigh repeatability measurement\r\n\r\n\r\n# SHT30 address, 0x44(68)\r\n# Read data back from 0x00(00), 6 bytes\r\n# cTemp MSB, cTemp LSB, cTemp CRC, Humididty MSB, Humidity LSB, Humidity CRC\r\nwhile True:\r\n bus.write_i2c_block_data(0x44, 0x2C, [0x06])\r\n\r\n time.sleep(0.5)\r\n data = bus.read_i2c_block_data(0x44, 0x00, 6)\r\n\r\n\r\n # Convert the data\r\n cTemp = ((((data[0] * 256.0) + data[1]) * 175) / 65535.0) - 45\r\n fTemp = cTemp * 1.8 + 32\r\n humidity = 100 * (data[3] * 256 + data[4]) / 65535.0\r\n \r\n a = round(cTemp, 1)\r\n \r\n print(a)\r\n\r\n # Output data to screen\r\n print (\"Relative Humidity : %.2f %%RH\" %humidity)\r\n print (\"Temperature in Celsius : %.2f C\" %cTemp)\r\n print (\"Temperature in Fahrenheit : %.2f F\" %fTemp)\r\n \r\n time.sleep(5)\r\n","sub_path":"singletonTH/New Sensor Test/M30 Sensor Test/SHT.py","file_name":"SHT.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615675460","text":"from selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.support.expected_conditions import StaleElementReferenceException\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass BasePage:\n \"\"\" Wrapper for selenium operations \"\"\"\n\n def __init__(self, driver):\n self._driver = driver\n self._wait = WebDriverWait(self._driver, 10)\n\n def click(self, webelement):\n el = self._wait.until(expected_conditions.element_to_be_clickable(webelement))\n self._highlight_element(el, \"green\")\n el.click()\n\n def fill_text(self, webelement, txt):\n el = self._wait.until(expected_conditions.element_to_be_clickable(webelement))\n el.clear()\n self._highlight_element(el, \"green\")\n el.send_keys(txt)\n\n def clear_text(self, webelement):\n el = self._wait.until(expected_conditions.element_to_be_clickable(webelement))\n el.clear()\n\n def scroll_to_bottom(self):\n self._driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n def submit(self, webelement):\n self._highlight_element(webelement, \"green\")\n webelement.submit()\n\n def get_text(self, webelement):\n el = self._wait.until(expected_conditions.visibility_of_element_located(webelement))\n self._highlight_element(el, \"green\")\n return el.text\n\n def move_to_element(self, webelement):\n action = ActionChains(self._driver)\n self._wait.until(expected_conditions.visibility_of(webelement))\n action.move_to_element(webelement).perform()\n\n def is_elem_displayed(self, webelement):\n try:\n return webelement.is_displayed()\n except StaleElementReferenceException:\n return False\n except NoSuchElementException:\n return False\n\n def _highlight_element(self, webelement, color):\n original_style = webelement.get_attribute(\"style\")\n new_style = \"background-color:yellow;border: 1px solid \" + color + original_style\n self._driver.execute_script(\n \"var tmpArguments = arguments;setTimeout(function () {tmpArguments[0].setAttribute('style', '\"\n + new_style + \"');},0);\", webelement)\n self._driver.execute_script(\n \"var tmpArguments = arguments;setTimeout(function () {tmpArguments[0].setAttribute('style', '\"\n + original_style + \"');},400);\", webelement)\n","sub_path":"pages/base_page.py","file_name":"base_page.py","file_ext":"py","file_size_in_byte":2526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"650400663","text":"from task_3 import column_to_list\n\n\ndef count_items(column_list):\n \"\"\"\n Função para contar a quantidade itens em uma lista de dados.\n Argumentos:\n data_list: Lista de dados.\n Retorna:\n Uma tupla contendo os types dos items e quantidades dos itens respectivamente.\n \"\"\"\n dictionary = {}\n for data in column_list:\n if not dictionary.get(data):\n dictionary[data] = 0\n dictionary[data] += 1\n return list(dictionary.keys()), list(dictionary.values())\n\n\ndef run(data_list):\n # TAREFA 12 - Desafio! (Opcional)\n # para que nós possamos usar essa função com outra categoria de dados.\n print(\"Você vai encarar o desafio? (yes ou no)\")\n\n answer = \"yes\"\n\n if answer == \"yes\":\n # ------------ NÃO MUDE NENHUM CÓDIGO AQUI ------------\n column_list = column_to_list(data_list, -2)\n types, counts = count_items(column_list)\n print(\"\\nTAREFA 11: Imprimindo resultados para count_items()\")\n print(\"Tipos:\", types, \"Counts:\", counts)\n assert len(types) == 3, \"TAREFA 11: Há 3 tipos de gênero!\"\n assert sum(counts) == 1551505, \"TAREFA 11: Resultado de retorno incorreto!\"\n # -----------------------------------------------------\n","sub_path":"task_11.py","file_name":"task_11.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"157824133","text":"# Copyright (c) 2012-2016 Seafile Ltd.\nimport re\nimport datetime\nimport time\nimport urllib\nimport logging\n\nfrom functools import wraps\n\nfrom rest_framework import status\n\nfrom synserv import ccnet_api\nfrom pyrpcsyncwerk import RpcsyncwerkError\n\nfrom restapi.api2.utils import api_error\nfrom restapi.utils import is_pro_version\nfrom restapi.base.templatetags.restapi_tags import email2nickname, \\\n email2contact_email\nfrom restapi.utils import get_log_events_by_time\n\ntry:\n from restapi.settings import MULTI_TENANCY\nexcept ImportError:\n MULTI_TENANCY = False\n\nlogger = logging.getLogger(__name__)\n\ndef api_check_group(func):\n \"\"\"\n Decorator for check if group valid\n \"\"\"\n @wraps(func)\n def _decorated(view, request, group_id, *args, **kwargs):\n group_id = int(group_id) # Checked by URL Conf\n try:\n group = ccnet_api.get_group(int(group_id))\n except RpcsyncwerkError as e:\n logger.error(e)\n error_msg = 'Internal Server Error'\n return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, error_msg)\n\n if not group:\n error_msg = 'Group %d not found.' % group_id\n return api_error(status.HTTP_404_NOT_FOUND, error_msg)\n\n return func(view, request, group_id, *args, **kwargs)\n\n return _decorated\n\ndef is_org_user(username, org_id=None):\n \"\"\" Check if an user is an org user.\n\n Keyword arguments:\n org_id -- An integer greater than zero. If provided,\n check if the user is a member of the specific org.\n \"\"\"\n\n if not is_pro_version() or not MULTI_TENANCY:\n return False\n\n try:\n if org_id:\n # Return non-zero if True, otherwise 0.\n return ccnet_api.org_user_exists(org_id, username) != 0\n else:\n orgs = ccnet_api.get_orgs_by_user(username)\n return len(orgs) > 0\n except Exception as e:\n logger.error(e)\n return False\n\ndef get_user_contact_email_dict(email_list):\n email_list = set(email_list)\n user_contact_email_dict = {}\n for email in email_list:\n if not user_contact_email_dict.has_key(email):\n user_contact_email_dict[email] = email2contact_email(email)\n\n return user_contact_email_dict\n\ndef get_user_name_dict(email_list):\n email_list = set(email_list)\n user_name_dict = {}\n for email in email_list:\n if not user_name_dict.has_key(email):\n user_name_dict[email] = email2nickname(email)\n\n return user_name_dict\n\ndef check_time_period_valid(start, end):\n if not start or not end:\n return False\n\n # check the date format, should be like '2015-10-10'\n date_re = re.compile(r'^(\\d{4})\\-([1-9]|0[1-9]|1[012])\\-([1-9]|0[1-9]|[12]\\d|3[01])$')\n if not date_re.match(start) or not date_re.match(end):\n return False\n\n return True\n\ndef get_log_events_by_type_and_time(log_type, start, end):\n start_struct_time = datetime.datetime.strptime(start, \"%Y-%m-%d\")\n start_timestamp = time.mktime(start_struct_time.timetuple())\n\n end_struct_time = datetime.datetime.strptime(end, \"%Y-%m-%d\")\n end_timestamp = time.mktime(end_struct_time.timetuple())\n end_timestamp += 24 * 60 * 60\n\n events = get_log_events_by_time(log_type, start_timestamp, end_timestamp)\n events = events if events else []\n return events\n\ndef generate_links_header_for_paginator(base_url, page, per_page, total_count, option_dict={}):\n\n def is_first_page(page):\n return True if page == 1 else False\n\n def is_last_page(page, per_page, total_count):\n if page * per_page >= total_count:\n return True\n else:\n return False\n\n if type(option_dict) is not dict:\n return ''\n\n query_dict = {'page': 1, 'per_page': per_page}\n query_dict.update(option_dict)\n\n # generate first page url\n first_page_url = base_url + '?' + urllib.urlencode(query_dict)\n\n # generate last page url\n last_page_query_dict = {'page': (total_count / per_page) + 1}\n query_dict.update(last_page_query_dict)\n last_page_url = base_url + '?' + urllib.urlencode(query_dict)\n\n # generate next page url\n next_page_query_dict = {'page': page + 1}\n query_dict.update(next_page_query_dict)\n next_page_url = base_url + '?' + urllib.urlencode(query_dict)\n\n # generate prev page url\n prev_page_query_dict = {'page': page - 1}\n query_dict.update(prev_page_query_dict)\n prev_page_url = base_url + '?' + urllib.urlencode(query_dict)\n\n # generate `Links` header\n links_header = ''\n if is_first_page(page):\n links_header = '<%s>; rel=\"next\", <%s>; rel=\"last\"' % (next_page_url, last_page_url)\n elif is_last_page(page, per_page, total_count):\n links_header = '<%s>; rel=\"first\", <%s>; rel=\"prev\"' % (first_page_url, prev_page_url)\n else:\n links_header = '<%s>; rel=\"next\", <%s>; rel=\"last\", <%s>; rel=\"first\", <%s>; rel=\"prev\"' % \\\n (next_page_url, last_page_url, first_page_url, prev_page_url)\n\n return links_header\n","sub_path":"fhs/usr/share/python/syncwerk/restapi/restapi/api3/endpoints/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"455699611","text":"#!/usr/bin/env python\r\nimport json\r\nimport scrapy\r\nfrom scrapy import signals\r\nfrom scrapy.xlib.pydispatch import dispatcher\r\n\r\nclass Mb123Page(scrapy.Spider):\r\n\tname = \"Mb123Page\"\r\n\tallowed_domains = [\"mobil123.com\"]\r\n\t#start_urls = [\"http://www.mobil123.com/dijual/toyota-fortuner-g-luxury-jawa-tengah-grogol/2842222/\"]\r\n\tstart_urls = []\r\n\tfile_input = \"/var/www/html/crawler/crawler/output/mb123UrlList.txt\"\r\n\tfile_output = \"/var/www/html/crawler/crawler/output/mb123Page.json\"\r\n\ttag = {}\r\n\tdatas = []\r\n\r\n\tdef __init__(self):\r\n\t\tdispatcher.connect(self.spider_opened, signals.spider_opened)\r\n\t\tdispatcher.connect(self.spider_closed, signals.spider_closed)\r\n\t\t\r\n\t\t\r\n\tdef spider_opened(self, spider):\r\n\t\twith open(self.file_input,\"r\") as read_file:\r\n\t\t\tself.start_urls =[url.strip() for url in read_file.readlines()]\r\n\t\tself.tag = {\r\n\t\t'header': '//*/div[@id=\"main-content\"]/*/h1[@class=\"post-title\"]/span[@class=\"title\"]/text()',\r\n\t\t'price': '//*/div[@id=\"main-content\"]/*/div[@class=\"post-highlight\"]/p[@class=\"price\"]/span/text()',\r\n\t\t'sellerType': '//*/div[@class=\"content\"]/p[@class=\"seller-type private pull-left\"]/text()',\r\n\t\t'location': '//*/div[@class=\"content\"]/p[@class=\"seller-type\"]/text()',\r\n\t\t'sellerPhone': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"list-contact-wrapper\"]/ul[@class=\"list-contact\"]/li[1]/text()',\r\n\t\t'sellerName': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"list-contact-wrapper\"]/h2[@class=\"seller-username\"]/text()',\r\n\t\t'brand': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-make\"]/td[@class=\"data\"]/a/span/text()',\r\n\t\t'model': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-model\"]/td[@class=\"data\"]/a/span/text()',\r\n\t\t'variant': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-variant\"]/td[@class=\"data\"]/text()',\r\n\t\t'year': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-year\"]/td[@class=\"data\"]/text()',\r\n\t\t'engineSize': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-engine\"]/td[@class=\"data\"]/text()',\r\n\t\t'transmission': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-transmission\"]/td[@class=\"data\"]/text()',\r\n\t\t'km': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-mileage\"]/td[@class=\"data\"]/text()',\r\n\t\t'color': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-color\"]/td[@class=\"data\"]/text()',\r\n\t\t'new_used': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-car-type\"]/td[@class=\"data\"]/text()',\r\n\t\t'sellerUploadDate': '//*/div[@id=\"main-content\"]/*/div[@class=\"section-body\"]/div[@class=\"row-fluid\"]/*/table[@class=\"list\"]/tr[@class=\"tr-updated\"]/td[@class=\"data\"]/text()',\r\n\t\t'description':'//*/div[@id=\"single-post-comment\"]/div[@class=\"section-body\"]/*/table[@class=\"wrap\"]/tr/td[@class=\"comment\"]/span/text()'\t\t\t\t\t\t\t\t\t\r\n\t\t}\r\n\t\t\r\n\r\n\tdef spider_closed(self, spider):\r\n\t\twith open(self.file_output, \"w\") as write_file:\r\n\t\t\tjson.dump(self.datas, write_file)\r\n \r\n\tdef parse(self, response):\r\n\t\tprice = ''.join((str(p.strip().replace('Rp\\n','')) for p in response.xpath(self.tag['price']).extract()))\r\n\t\tprice = ''.join(p for p in price if p.isdigit()) \r\n\r\n\t\tbrand = response.xpath(self.tag['brand']).extract()\r\n\t\tbrand = brand[0].replace('\\n','').strip() if len(brand) > 0 else \"\"\r\n\r\n\t\tmodel = response.xpath(self.tag['model']).extract()\r\n\t\tmodel = model[0].replace('\\n','').strip() if len(model) > 0 else \"\"\r\n\r\n\t\tvariant = response.xpath(self.tag['variant']).extract()\r\n\t\tvariant = variant[0].replace('\\n','').strip() if len(variant) > 0 else \"\"\r\n\r\n\t\tyear = response.xpath(self.tag['year']).extract()\r\n\t\tyear = year[0].replace('\\n','').strip() if len(year) > 0 else \"\" \r\n\r\n\t\tengineSize = response.xpath(self.tag['engineSize']).extract()\r\n\t\tengineSize = engineSize[0].replace('\\n','').strip() if len(engineSize) > 0 else \"\" \r\n\r\n\t\ttransmission = response.xpath(self.tag['transmission']).extract()\r\n\t\ttransmission = transmission[0].replace('\\n','').strip() if len(transmission) > 0 else \"\" \r\n\r\n\t\tkm = response.xpath(self.tag['km']).extract()\r\n\t\tkm = km[0].replace('\\n','').strip() if len(km) > 0 else \"\" \r\n\r\n\t\tcolor = response.xpath(self.tag['color']).extract()\r\n\t\tcolor = color[0].replace('\\n','').strip() if len(color) > 0 else \"\" \r\n\r\n\t\tnew_used = response.xpath(self.tag['new_used']).extract()\r\n\t\tnew_used = new_used[0].replace('\\n','').strip() if len(new_used) > 0 else \"\" \r\n\r\n\t\tsellerUploadDate = response.xpath(self.tag['sellerUploadDate']).extract()\r\n\t\tsellerUploadDate = sellerUploadDate[0].replace('\\n','').strip() if len(sellerUploadDate) > 0 else \"\"\r\n\r\n\t\t#description = ''.join((str(p.strip().replace('\\n','')) for p in response.xpath(self.tag['description']).extract()))\r\n\t\tdescription = ''.join(response.xpath(self.tag['description']).extract()).strip().replace('\\n', '')\r\n\t\tdata = {\r\n\t\t'website': self.allowed_domains[0],\r\n\t\t'url': response.request.url,\r\n\t\t'header': response.xpath(self.tag['header']).extract()[0].strip().replace('\\n',''),\r\n\t\t'price': price.strip(),\r\n\t\t'sellerType': response.xpath(self.tag['sellerType']).extract()[0].strip().replace('\\n',''),\r\n\t\t'location': response.xpath(self.tag['location']).extract()[0].strip().replace('≫','-').replace('\\n',''),\r\n\t\t'sellerPhone': response.xpath(self.tag['sellerPhone']).extract()[0].strip().replace('\\n',''),\r\n\t\t'sellerName': response.xpath(self.tag['sellerName']).extract()[0].strip().replace('\\n',''),\r\n\t\t'brand': brand,\r\n\t\t'model': model,\r\n\t\t'variant': variant,\r\n\t\t'year': year,\r\n\t\t'engineSize': engineSize,\r\n\t\t'transmission': transmission,\r\n\t\t'km': km,\r\n\t\t'color': color,\r\n\t\t'new_used': new_used,\r\n\t\t'sellerUploadDate': sellerUploadDate,\r\n\t\t'description': description\t\r\n\t\t}\r\n\r\n\t\tself.datas.append(data)\r\n\t\t","sub_path":"crawler/crawler/spiders/mb123Page.py","file_name":"mb123Page.py","file_ext":"py","file_size_in_byte":6230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"462708478","text":"from django.conf.urls import url\nfrom django.conf.urls import (handler404, handler500)\nfrom splash import views\n\n\nurlpatterns = [\n url(r'^$', views.NewTeamView.as_view(), name='new_team'),\n url(r'^new_team/$', views.NewTeamView.as_view(), name='new_team'),\n url(r'^prestart/$', views.PreStartView.as_view(), name='prestart'),\n url(r'^teams/$', views.TeamsPageView.as_view(), name='teams'),\n url(r'^data_entry/$', views.DataEntryView.as_view(), name='data_entry'),\n url(r'^driving_control/$', views.DrivingControlView.as_view(), name='driving_control'),\n url(r'^export/xls/$', views.export_data_xls, name='export_data_xls'),\n url(r'^entry_submission/$', views.entry_submission, name = 'entry_submission'),\n url(r'^handle_new_team/$', views.handle_new_team, name='handle_new_team'),\n url(r'^prestart_submission/$', views.prestart_submission, name='prestart_submission'),\n]\n\n","sub_path":"splash/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128637006","text":"#!/usr/bin/python3\n# coding: utf-8\nimport gzip, re\nimport os\nimport argparse\n\n\n# retokenise badly tokenised contractions\ndef retokenise(line):\n line = re.sub(\"(\"+\"|\".join(get_en_aux())+\")n 't\", r\"\\1 n't\", line)\n # print(line)\n return line\n\ndef get_en_tags():\n return re.compile(\"(^|.*\\, )((\"+\"|\".join(get_en_aux())+\") (?:n.t )?\"+\"(\"+\"|\".join(get_en_pros())+\")(?: not)?[ \\?\\!\\.\\\"\\'\\-\\:\\;]+?) *$\", re.I)\n\n# must match this\ndef get_en_neg_tags():\n # case sensitive\n # cannot_precede = \"(?:.(?!\\\\b\"+\"\\\\b|\\\\b\".join([\"or\", \"what\", \"where\", \"why\", \"when\", \"how\", \"that\", \"this\", \"it\", \"and\", \"but\", \"just\", \"definitely\"])+\"\\\\b))*\"\n contr_spec = \"(\"+\"|\".join(get_en_aux())+\") n.t (\"+\"|\".join(get_en_pros())+\") \\? *$\"\n noncontr_spec = \"(\"+\"|\".join(get_en_aux())+\") (\"+\"|\".join(get_en_pros())+\") not \\? *$\"\n contr = \", (\"+\"|\".join(get_en_aux())+\") n.t (\"+\"|\".join(get_en_pros())+\")[ \\?\\!\\.\\\"\\'\\-\\:\\;]+$\"\n noncontr = \", (\"+\"|\".join(get_en_aux())+\") (\"+\"|\".join(get_en_pros())+\") not[ \\?\\!\\.\\\"\\'\\-\\:\\;]+? *$\"\n return re.compile(\"^(.*? )(\"+contr+\"|\"+noncontr+\"|\"+contr_spec+\"|\"+noncontr_spec+\")\", re.I)\n\n# mustn't match this\ndef inv_en_neg_tags():\n preceding = \"(\\\\b\"+\"\\\\b|\\\\b\".join([\"or\", \"what\", \"where\", \"why\", \"when\", \"how\", \"that\", \"this\", \"it\", \"and\", \"but\", \"just\", \"definitely\"])+\"\\\\b)\"\n contr = \"(\"+\"|\".join(get_en_aux())+\")[\\,\\.\\-\\\"\\'\\`\\:\\; ]* n.t (\"+\"|\".join(get_en_pros())+\")[ \\?\\!\\.\\\"\\'\\-\\:\\;]+? *$\"\n noncontr = \"(\"+\"|\".join(get_en_aux())+\")[\\,\\.\\-\\\"\\'\\`\\:\\; ]*(\"+\"|\".join(get_en_pros())+\") not[ \\?\\!\\.\\\"\\'\\-\\:\\;]+? *$\"\n return re.compile(\"^.*?\"+preceding+\" (\"+contr+\"|\"+noncontr+\")$\", re.I)\n \n\ndef get_en_pros():\n return [\"I\", \"you\", \"[h\\']e\", \"thee\", \"ye\", \"yer\", \"she+\", \"we\", \"one\", \"they\", \"it\", \"i\\'\", \"there\", \"someone\", \"anyone\"]\n\ndef get_en_aux():\n return [\"have\", \"had\", \"has\", \"might\", \"may\", \"will\", \"would\", \"could\",\n \"can\", \"must\", \"should\", \"shall\", \"ought\", \"do\", \"does\",\n \"am\", \"are\", \"is\", \"was\", \"were\", \"had\", \"ai\", \"dare\", \"need\"]\n\ndef get_fr_tag_phrases():\n fr_tag_phrases = [\"n\\' est \\-ce pas\", \"non\", \"hein\", \"pas vrai\", \"d\\' accord\",\n \"ou quoi\", \"tu ne crois pas\", \"tu ne trouves? pas\", \"c\\' est ça\",\n \"dis\", \"dites\", \"ok\", \"okay\", \"voulez \\-vous\", \"veux \\-tu\", \"si\",\n \"oui\", \"alors\", \"tu vois\", \"tu ne vois pas\", \"vois \\-tu\",\n \"tu crois\", \"crois \\-tu\", \"souviens \\-toi\", \"souvenez \\-vous\"]\n return fr_tag_phrases\n\ndef get_en_not_permitted():\n en_not_permitted = [\"^.*?(is you|do it|have one|are he|are she|is not you|have it|is not you|not you) *[\\.\\!\\? \\'\\\"\\;\\:\\-]*$\", \"^.*?\\.( ?\\.)+[ \\?\\.\\\"\\']*$\"]\n return en_not_permitted\n\ndef get_regex_en_special_tags():\n # return re.compile(\".+ (inni.|\\, right \\?|eh \\?|\\, see \\?|\\, remember \\?|\\, you know \\?|\\, or what \\?)[ \\\"\\'\\?\\.]*$\", re.I)\n return re.compile(\"^(.+) ((?:inn+i.|\\, ri+ght \\?|e+h+ \\?|\\, see+ \\?|\\, remember \\?|\\, you know \\?|\\, or what \\?|\\, ye+a+h+ \\?|\\, aye|\\, you see|\\, like|\\, ok(ay)? \\?|do n't y..? think \\?|\\, correct \\?)[ \\\"\\'\\?\\!\\.\\-\\;\\:]*)$\", re.I)\n\ndef get_en_neg_words():\n return [\"n[\\'o]t\", \"never\", \"no longer\", \"nobody\", \"nowhere\", \"nothin[^ ]?\", \"no[- ]?one\",\n \"none\", \"[h\\']ardly\", \"rarely\", \"scarcely\", \"seldom\", \"barely\", \"neither\",\n \"nor\", \"ne\\'er\", \"[^ ]*nae\", \"nuffin[^ ]?\", \"can ?nae\", \"nowt\", \" no \", \"absolutely no\"] # removed 'no' - treat separately\n\n# special case of are giving is (for collectives such as 'the team is happy, aren't they?')\n# 'I'm here, aren't I', so are -> 'm\ndef get_aux_verb_regex(aux_verb):\n contractions = {\"am\": \"([\\'a]m|ai)\",\n \"is\": \"([\\'i]s|ai)\",\n \"ai\": \"(ai|is|am|are|\\'re)\", # ain't you/it/I/we etc.\n \"are\": \"([a\\']re|ai|re|[a\\']m|theyre|your\\'?e)\",\n \"has\": \"([h\\']as|\\'?s|ai)\",\n \"have\": \"('ve|have|ai|has)\", # for collectives, e.g. the team has done blah blah, haven't they?\n \"would\": \"(wou[lI]d|\\'d|is|will)\",\n \"should\": \"shou[lI]d\",\n \"will\": \"(will|\\'ll|gonna|going to)\",\n \"were\": \"(were|\\'re|are|was)\",\n \"was\": \"(was|were)\",\n \"do\": \"(do|does|have|got)\", # change of interlocutor (he doesn't, do you?)\n }\n if aux_verb.lower() in contractions:\n return contractions[aux_verb.lower()]\n else: return aux_verb.lower()\n\ndef get_neg_raising_preds(): # and others -> need to refine\n return [\"believe(s?|ing|d)\", \"suppos(es?|ing)\", \"think(s|ing)?\", \"likely\", \"seem(s|ed)?\", \"ought\", \"gonna\", \"going to\",\n \"it (w?.s|were) like\", \"guess(es|ed|ing)?\", \"thought\", \"happen to\", \"imagin(es?|ed|ing)\"]\n\ndef get_neg_neg_raising_preds(): # and others\n return [\"it 's not like\", \"it is n't like\"]\n\n\ndef tagpro2auxpro_regex(pro):\n pros = {\"I\": \"[\\bIi\\b]\",\n \"we\": \"(?!(s?he|they|there))[^ ]+\",\n \"you\": \"(you|we|she|it|he|I|they)\", # can have change of interlocutor, 3rd -> 2nd person\n \"there\": \"there\",\n \"it\": \"([il]t|this|that|[^ ]+)\",\n \"they\":\"(they|[^ ]+|people)\",\n \"he\": \"[^ ]+\",\n \"she\": \"[^ ]+\"} \n\n if pro in pros: return pros[pro]\n else: return \"(\"+pro+\"|[^ ]+|)\"\n \n\ndef is_viable_anchor(anchor, tag):\n nonviable = re.compile(\"[\\'\\\"\\.\\,\\:\\;\\` \\/\\\\\\(\\)]*((yeah|ok(ay)?|er*|[oôau0ûq]+h+|well|of course|yes|no|hey|[uh]m+|so|sorry|o[iy]|hell|but|why|no|please|and|aye|oh) )+[\\'\\\"\\.\\,\\:\\;\\` \\/\\\\\\(\\)]*$\", re.I)\n empty = re.compile(\"([\\'\\\"\\.\\,\\:\\;\\`\\?\\!\\- ]|[^a-z0-9])*$\", re.I)\n \n if re.match(nonviable, anchor): return False\n if re.match(empty, anchor): return False\n return True\n\n\n \n\n#=================== General functions ===================\ndef print_my_line(i, is_en_type, is_fr_type, search_en, search_fr, strict):\n # Both en and fr question\n if search_en and search_fr and is_en_type and is_fr_type:\n os.sys.stdout.write(str(i+1)+\"\\n\")\n \n # Either en question or fr question but not both\n elif strict and search_en and not search_fr and \\\n is_en_type and not is_fr_type:\n os.sys.stdout.write(str(i+1)+\"\\n\")\n \n elif strict and search_fr and not search_en and \\\n is_fr_type and not is_en_type:\n os.sys.stdout.write(str(i+1)+\"\\n\")\n \n # Either en question or fr question \n elif not strict and search_en and not search_fr and is_en_type:\n os.sys.stdout.write(str(i+1)+\"\\n\")\n \n elif not strict and search_fr and not search_en and is_fr_type:\n os.sys.stdout.write(str(i+1)+\"\\n\")\n\n#=================== Tag question functions ===================\n\nen_tags = get_en_tags() #re.compile(\"(^|.*\\, )(\"+\"|\".join(get_en_aux())+\") (n.t )?\"+\"(\"+\"|\".join(get_en_pros())+\")( not)?[ \\?\\.\\\"\\']*$\", re.I)\nen_neg_tags = get_en_neg_tags()\nen_neg_tags_inv = inv_en_neg_tags()\nen_special_tags = get_regex_en_special_tags()\nen_non_tag = re.compile(\"(\"+\"|\".join(get_en_not_permitted())+\")\", re.I)\nfr_tags = re.compile(\"(.*?)\\, (\"+\"|\".join(get_fr_tag_phrases())+\") \\?$\", re.I)\n\n# is a tag question or not (grammatical or miscellaneous)\ndef is_en_tag(line):\n if is_en_gram_tag(line):\n return True\n if is_en_gram_tag_negtag(line):\n return True\n if is_en_misc_tag(line):\n return True\n return False \n\n\n# is a tag question (grammatical)\ndef is_en_gram_tag(line):\n # must not match this\n if re.match(en_non_tag, line):\n return False\n if re.match(en_neg_tags_inv, line):\n return False\n \n # can match this\n # if is_en_gram_tag_negtag(line) or is_en_gram_tag_postag(line):\n if re.match(en_neg_tags, line) or re.match(en_tags, line):\n\n # check anchors\n anchor, tag = get_anchor_and_tag(line)\n if not anchor: return False\n # does the anchor contain something that confirms that it is a tag question?\n if not is_viable_anchor(anchor, tag): return False\n \n return True\n return False # default\n\n\ndef is_en_gram_tag_negtag(line):\n \n # must not match this\n if re.match(en_non_tag, line):\n return False\n if re.match(en_neg_tags_inv, line):\n return False\n \n # must match this\n if is_en_gram_tag(line) and re.match(en_neg_tags, line):\n\n # check anchors\n anchor, tag = get_anchor_and_tag(line)\n if not anchor: return False\n # does the anchor contain something that confirms that it is a tag question?\n if not is_viable_anchor(anchor, tag): return False\n\n return True\n return False # default\n\n \ndef is_en_gram_tag_postag(line):\n yes = (is_en_gram_tag(line) and not is_en_gram_tag_negtag(line))\n\n # if \"n't ?\" in line and not yes:\n # print(line)\n \n return yes\n \n \n# is a tag question (miscellaneous)\ndef is_en_misc_tag(line):\n # must not match this\n if re.match(en_non_tag, line):\n return False\n # can match this\n if re.match(en_special_tags, line):\n\n anchor, tag = get_anchor_and_tag(line)\n if not anchor: return False\n # does the anchor contain something that confirms that it is a tag question?\n if is_viable_anchor(anchor, tag):\n return True\n return False # default\n\n# is a French tag question\ndef is_fr_tag(line):\n \n if re.match(fr_tags, line):\n\n return True\n return False\n\n\ndef get_anchor_and_tag(line):\n anchor_tag_match = re.match(en_tags, line)\n anchor_neg_tag_match = re.match(en_neg_tags, line)\n anchor_special_tag_match = re.match(en_special_tags, line)\n # print(line)\n # print(\"the matches = \")\n # print(anchor_tag_match)\n # print(anchor_neg_tag_match)\n # print(anchor_special_tag_match)\n \n if anchor_tag_match:\n anchor = anchor_tag_match.group(1)\n tag = anchor_tag_match.group(2)\n elif anchor_special_tag_match:\n anchor = anchor_special_tag_match.group(1)\n tag = anchor_special_tag_match.group(2)\n elif anchor_neg_tag_match:\n anchor = anchor_neg_tag_match.group(1)\n tag = anchor_neg_tag_match.group(2)\n else:\n anchor, tag = None, None\n # os.sys.stderr.write(line)\n # print(tag)\n # input()\n \n if anchor: return anchor.strip(), tag.strip()\n else: return anchor, tag\n \ndef get_auxverb_tagpro(line):\n anchor_tag_match = re.match(en_tags, line)\n anchor_neg_tag_match = re.match(en_neg_tags, line)\n anchor_special_tag_match = re.match(en_special_tags, line)\n \n if anchor_tag_match:\n tagpro = anchor_tag_match.group(4)\n auxverb = anchor_tag_match.group(3)\n elif anchor_special_tag_match:\n tagpro = None\n auxverb = None\n elif anchor_neg_tag_match:\n auxverb = anchor_neg_tag_match.group(4)\n tagpro = anchor_neg_tag_match.group(3)\n else:\n auxverb, tagpro = None, None\n os.sys.stderr.write(line)\n\n # os.sys.stderr.write(auxverb+\" \"+tagpro+\"\\n\")\n if auxverb: return auxverb.strip(), tagpro.strip() \n else: return auxverb, tagpro\n\n# all tag questions\ndef tag_questions(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n fr = fr.strip()\n \n # English\n found_en = False\n if search_en and is_en_tag(en): found_en = True\n \n # French\n found_fr = False\n if search_fr and is_fr_tag(fr): found_fr = True\n\n # check anchors\n # if search_en: anchor, tag = get_anchor_and_tag(en)\n # if not anchor: continue \n\n # does the anchor contain something that confirms that it is a tag question?\n # if search_en and not is_viable_anchor(anchor, tag):\n # continue\n \n # Send to print function\n if found_en or found_fr:\n print_my_line(i, found_en, found_fr, search_en, search_fr, strict)\n\n# grammatical tag questions\ndef gramtag_questions(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n\n # English\n found_en = False\n if search_en and is_en_gram_tag(en): found_en = True\n \n # French\n found_fr = False\n if search_fr and is_fr_tag(fr): found_fr = True\n\n # check anchors\n if search_en: anchor, tag = get_anchor_and_tag(en)\n if not anchor: continue \n\n # does the anchor contain something that confirms that it is a tag question?\n if search_en and not is_viable_anchor(anchor, tag):\n continue\n \n # Send to print function\n if found_en or found_fr:\n print_my_line(i, found_en, found_fr, search_en, search_fr, strict)\n\n# miscellaneous tag questions\ndef misctag_questions(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_misc_tag(en): found_en = True\n \n # French\n found_fr = False\n if search_fr and is_fr_tag(fr): found_fr = True\n\n # check anchors\n if search_en: anchor, tag = get_anchor_and_tag(en)\n if not anchor: continue \n\n # does the anchor contain something that confirms that it is a tag question?\n if search_en and not is_viable_anchor(anchor, tag):\n continue\n \n # Send to print function\n if found_en or found_fr:\n print_my_line(i, found_en, found_fr, search_en, search_fr, strict) \n\n\na_no_negwords = re.compile(\"^.*?(\"+\"|\".join(get_en_neg_words())+\") ?\", re.I)\n\nadverbs = [\"really\", \"certainly\", \"definitely\", \"surely\", \"often\", \"mostly\", \"possibly\", \"potentially\", \"maybe\", \"even\"]\nadv_re = \"( (\"+\"|\".join(adverbs)+\") )\"\n \ndef is_negative_anchor(anchor, aux_verb, tag_pro):\n return is_positive_anchor(anchor, aux_verb, tag_pro, True)\n\ndef is_positive_anchor(anchor, aux_verb, tag_pro, neg=False):\n # doesn't contain any negative words\n # os.sys.stderr.write(anchor+\"\\n\")\n if not re.match(a_no_negwords, anchor):\n # print(anchor)\n # print(aux_verb)\n # os.sys.stderr.write(\"no neg words whatsoever\\n\")\n if not neg: return True\n else: return False\n\n # contains a negative word\n else:\n if not tag_pro: tag_pro=\"\"\n if not aux_verb: aux_verb=\"\"\n\n # print(anchor)\n\n # input()\n # if \"you 've never been\" in anchor:\n # print(\"|\"+anchor+\"|\")\n # print(\"|\"+aux_verb+\"|\")\n # print(\"|\"+tag_pro+\"|\")\n # print(\"^(.*? )?(\"+\"|\".join(get_en_pros())+\") ((\"+\"|\".join(get_en_aux())+\") ?)?(\"+\"|\".join(get_en_neg_words())+\") ?([^\\.\\, ])? (\"+\"|\".join(get_neg_raising_preds())+\")( [^ ]+)\")\n # print(re.match(\"^(.*? )?(\"+\"|\".join(get_en_pros())+\") ((\"+\"|\".join(get_en_aux())+\") )?(\"+\"|\".join(get_en_neg_words())+\") ?([^\\.\\, ])? (\"+\"|\".join(get_neg_raising_preds())+\")( [^ ]+)\", anchor, re.I))\n\n # input()\n # print(re.match(\"^(|.*? )?\"+tag_pro+\" (([^ ]+ )(\"+\"|\".join(get_en_neg_words())+\"| no\"+tag_pro+\")( [^ ]+)* *)\", anchor, re.I))\n # print(^(.*? )?(?!(who|if|when|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+\" ?(\"+\"|\".join(get_en_neg_words())+\") \"+get_aux_verb_regex(aux_verb)+\" ?(?!\"+get_aux_verb_regex(aux_verb)+\")\")\n # print(re.match(\"^(.*? )?(?!(who|if|when|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+\" ?(\"+\"|\".join(get_en_neg_words())+\") \"+get_aux_verb_regex(aux_verb)+\" ?(?!\"+get_aux_verb_regex(aux_verb)+\")\", anchor))\n # print(re.match(\"^(.*? )?(?!(who|if|when|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+\" ?\"+get_aux_verb_regex(aux_verb)+\" ?(\"+\"|\".join(get_en_neg_words())+\") ?(?!.*? \"+get_aux_verb_regex(aux_verb)+\"( |$))\", anchor, re.I))\n #-------------------------> almost definitely negative\n\n\n # you don't think / you think, do (n't) you?\n if re.match(\"you do n't\"+adv_re+\"? ?(think|suppose|imagine|believe|know)\", anchor) and tag_pro==\"you\" and aux_verb==\"do\":\n if neg: return True\n elif re.match(\"you (do )?\"+adv_re+\"?(think|suppose|imagine|believe|know)\", anchor) and tag_pro==\"you\" and aux_verb==\"do\":\n if not neg: return True\n\n\n # you know..., don't you?\n elif re.match(\"(^|.*?[ \\,]*|.*? and )(you|s?he|we) (do )?knows? .*?\", anchor, re.I) and aux_verb in [\"do\", \"does\", \"did\", None]:\n\n # print(\"you know\")\n if not neg: return True\n\n elif re.match(\"(^|.*?[ \\,]*|.*? and )(you|s?he|we) \"+get_aux_verb_regex(aux_verb)+adv_re+\"? knows? .*?\", anchor, re.I) and aux_verb in [\"do\", \"does\", \"did\", None]:\n if not neg: return True\n \n\n # only one auxiliary in the sentence and is negated\n elif re.match(\"(.*? )(\"+ \"|\".join(get_en_aux())+\") (\"+\"|\".join(get_en_neg_words())+\")(?!.*? (\"+\"|\".join(get_en_aux())+\")( |$))\", anchor, re.I):\n if neg: return True\n \n # print(\"here\")\n # print(tag_pro)\n # print(aux_verb)\n # print(\"^(.*? )?(?!(who|if|when|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+\" ?\"+get_aux_verb_regex(aux_verb)+\" ?\"+adv_re+\"?(\"+\"|\".join(get_en_neg_words())+\"| no) ?(?!.*? \"+tagpro2auxpro_regex(tag_pro)+\" \"+get_aux_verb_regex(aux_verb)+\"( |$))\")#, anchor, re.I))\n \n # negated auxiliary in the anchor (of equivalent form to the auxiliary in the tag question). if several, take last one\n elif re.match(\"^(.*? )?(?!(who|if|when|why|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+\" ?\"+get_aux_verb_regex(aux_verb)+\" ?\"+adv_re+\"?(\"+\"|\".join(get_en_neg_words())+\"| no) ?(?!.*? \"+tagpro2auxpro_regex(tag_pro)+\" \"+get_aux_verb_regex(aux_verb)+\"( |$))\", anchor, re.I) or \\\n re.match(\"^(.*? )?(?!(who|if|when|why|\\\") ?([^ ]+ )?)\"+tagpro2auxpro_regex(tag_pro)+adv_re+\"? ?(\"+\"|\".join(get_en_neg_words())+\"| no) \"+adv_re+\"?\"+get_aux_verb_regex(aux_verb)+\" ?(?!.*? \"+tagpro2auxpro_regex(tag_pro)+\" \"+get_aux_verb_regex(aux_verb)+\"( |$))\", anchor, re.I) or \\\n (\"cannot\" in anchor and \"can\" in aux_verb) or (\"dont\" in anchor and \"do\" in aux_verb):\n # print(\"neg repeated aux\")\n \n \n if neg: return True\n else: return False\n \n\n # negated auxiliary in the anchor (of equivalent form to the auxiliary in the tag question). if several, take last one\n elif re.match(\"^(.*? )?\"+get_aux_verb_regex(aux_verb)+\" ?(\"+\"|\".join(get_en_neg_words())+\"| no) ?(?!.*? \"+get_aux_verb_regex(aux_verb)+\"( |$))\", anchor, re.I) or \\\n re.match(\"^(.*? )?(\"+\"|\".join(get_en_neg_words())+\"| no) \"+get_aux_verb_regex(aux_verb)+\" ?(?!.*? \"+get_aux_verb_regex(aux_verb)+\"( |$))\", anchor, re.I) or \\\n (\"cannot\" in anchor and \"can\" in aux_verb) or (\"dont\" in anchor and \"do\" in aux_verb):\n # print(\"neg repeated aux\")\n \n if neg: return True\n else: return False \n \n # search for last pronoun (when verb is do) TODO\n elif aux_verb in [\"do\", \"does\", \"did\", \"\"] and aux_verb not in anchor and \\\n (re.match(\"^(|.*? )?\"+tag_pro+\" ([^ ]+ )?(\"+\"|\".join(get_en_neg_words())+\"| no\"+tag_pro+\")( [^ ]+)* *\", anchor, re.I) or \\\n re.match(\"^(|.*? )?\"+tag_pro+\" (\"+\"|\".join(get_en_neg_words())+\"| no\"+tag_pro+\") \", anchor, re.I)):\n # print(\"last pron\")\n if neg: return True\n \n \n # certain pronouns must be repeated I -> I, and some cannot have certain pronouns corresponding, we =/= s/he/they\n\n \n \n\n # negative subject w/ they/it/there \n # neg polarity item as subject of auxiliary\n elif re.match(\"(^|.*? )(no.?one|nobody|nothin.?|nuffin.?|nought|not everyone|not all|neither|no|not one|none of th..|none of it) \"+get_aux_verb_regex(aux_verb)+\" .*?\", anchor, re.I):\n if neg: return True\n\n elif re.match(\"(^|.*? )(no.?one|nobody|nothin.?|nuffin.?|nought|not everyone|not all|neither|no|not one|none of th..|none of it) \", anchor, re.I) and \\\n tag_pro in [\"they\", \"it\", \"there\"] and aux_verb in [\"does\", \"did\", \"do\"]:\n if neg: return True \n\n # future tense and neg -> negative\n elif (re.match(\".*?(may|will|going to) (\"+\"|\".join(get_en_neg_words())+\").*?\\,? *$\", anchor, re.I) or \\\n re.match(\".*?(\"+\"|\".join(get_en_neg_words())+\") (may|will|going to).*?\\,? *$\", anchor, re.I)) and \\\n aux_verb in [\"shall\", \"will\", \"may\", \"might\", \"\"]:\n if neg: return True\n\n # neg raising\n elif re.match(\"^(.*? )?(\"+\"|\".join(get_en_pros())+\") ((\"+\"|\".join(get_en_aux())+\") ?)?(\"+\"|\".join(get_en_neg_words())+\") ?([^\\.\\, ])? (\"+\"|\".join(get_neg_raising_preds())+\")( [^ ]+)\", anchor, re.I):# or \\\n # re.match(\"^(.*? )?(\"+\"|\".join(get_neg_neg_raising_preds())+\") ?(?!\"+get_aux_verb_regex(aux_verb)+\")*?\"+get_aux_verb_regex(aux_verb)+\"(?!\"+get_aux_verb_regex(aux_verb)+\")*?$\", anchor, re.I) or \\\n # re.match(\"^(.*? )?(\"+\"|\".join(get_neg_raising_preds())+\")(?!\"+get_aux_verb_regex(aux_verb)+\")[^ ]?\"+get_aux_verb_regex(aux_verb)+\"(?!\"+get_aux_verb_regex(aux_verb)+\")*?$\", anchor, re.I):\n if neg: return True\n\n #-------------------------> almost definitely positive\n # there is an identical auxiliary in the anchor and no negation has been detected up until now\n elif re.match(\"(^|.*? )\"+get_aux_verb_regex(aux_verb)+\" \", anchor, re.I):\n if not neg: return True\n\n #-----------------------------------------------------------------------\n # LESS SURE - cases where the auxiliary verb in the anchor is easy to detect \n # no auxiliary can be found\n \n # 'not' at the beginning of the anchor (with potentially one (changed) word before) and aux is a 'to be' form (probable ellipsis of verb) -> negative\n elif (re.match(\"([^ ]+ )*([\\.\\,])* (not|never) .*?\", anchor, re.I) or \\\n re.match(\"^(not|never) \", anchor, re.I)):# and aux_verb in [\"is\", \"are\", \"am\", \"were\", \"was\", \"\", \"do\", \"does\"]:\n if neg: return True\n\n # no at the beginning of the sentence and no commas or full stops after (apart from a final one) \n elif (re.match(\"^no [^\\,\\.]+?( \\,)? *$\", anchor, re.I) or \\\n re.match(\"^(nothing like|none of) .*?\", anchor, re.I)) and \\\n aux_verb in [\"is\", \"will\", \"was\"] and tag_pro in [\"there\", \"it\", \"\"]:\n # print(\"hi\")\n if neg: return True\n\n # imperatives, \"just do that...\", \"never just do that...\" etc.\n elif (re.match(\"(^|.*? )just [^ \\.\\,]+? (\"+\"|\".join(get_en_neg_words())+\") [^\\.\\,]+\\,?\", anchor, re.I) or \\\n re.match(\"(^|.*? )(\"+\"|\".join(get_en_neg_words())+\") just [^ \\.\\,]+? [^\\.\\,]+\\,?\", anchor, re.I)) and \\\n aux_verb in [\"will\", \"would\", \"can\", \"could\", \"\"] and tag_pro in [\"you\", \"\"]:\n if neg: return True\n\n elif re.match(\"(^|.*? )just [^\\.\\,]+\\,?\", anchor, re.I) and aux_verb in [\"will\", \"would\", \"can\", \"could\", \"\"] and tag_pro in [\"you\", \"\"]:\n if not neg: return True # pos\n\n # imperatives with will, would, shall\n elif re.match(\"^do n't\", anchor) and tag_pro==\"you\" and aux_verb in [\"shall\", \"will\", \"would\"]:\n if neg: return True\n else: return False\n\n # imperatives, \"let us do that...\", \"let us never do\"... \n elif re.match(\"(^|.*? )let .s (\"+\"|\".join(get_en_neg_words())+\") ?[^\\.\\,]+\\,?\", anchor, re.I) and aux_verb in [\"will\", \"shall\", \"\"] and tag_pro in [\"we\", \"\"]:\n if neg: return True\n \n elif re.match(\"(^|.*? )let .s [^\\.\\,]+\\,?\", anchor, re.I) and aux_verb in [\"will\", \"shall\", \"\"] and tag_pro in [\"we\", \"\"]:\n if not neg: return True\n\n # otherwise positive\n elif not neg: return True # check!!! TODO\n\n # print(\"end\")\n return False\n\n\ndef gramtag_posanchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n\n # os.sys.stderr.write(tag+\"|\"+anchor+\"|\"+auxverb+\"|\"+tagpro+\"\\n\")\n # input()\n \n if is_positive_anchor(anchor, auxverb, tagpro):\n found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict)\n \ndef gramtag_neganchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n # os.sys.stderr.write(tag+\"|\"+anchor+\"|\"+auxverb+\"|\"+tagpro+\"\\n\")\n # input()\n\n if is_negative_anchor(anchor, auxverb, tagpro):\n # os.sys.stderr.write(\"negative anchor\\n\")\n found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict)\n\n \ndef gramtag_postag(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag_postag(en):\n found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n \n\ndef gramtag_negtag(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n\n # English\n found_en = False\n if search_en and is_en_gram_tag_negtag(en):\n found_en = True \n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n\ndef gramtag_postagposanchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag_postag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_positive_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n \ndef gramtag_postagneganchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag_postag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_negative_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n\ndef gramtag_negtagneganchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag_negtag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_negative_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n\ndef gramtag_negtagposanchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_gram_tag_negtag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_positive_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n\ndef misctag_posanchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_misc_tag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_positive_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict) \n\n\ndef misctag_neganchor(en_file, fr_file, search_en, search_fr, strict):\n # Go through and match sentences\n for i, (en, fr) in enumerate(zip(en_file, fr_file)):\n if en.strip()==\"\": continue\n en = retokenise(en)\n \n # English\n found_en = False\n if search_en and is_en_misc_tag(en):\n anchor, tag = get_anchor_and_tag(en)\n auxverb, tagpro = get_auxverb_tagpro(en)\n if is_negative_anchor(anchor, auxverb, tagpro): found_en = True\n\n # Send to print function\n if found_en:\n print_my_line(i, found_en, False, search_en, search_fr, strict)\n \nif __name__ ==\"__main__\":\n \n parser = argparse.ArgumentParser()\n parser.add_argument(\"english_file\")\n parser.add_argument(\"french_file\")\n parser.add_argument(\"fr_en_or_both\", choices=[\"en\", \"fr\", \"both\"])\n parser.add_argument(\"-strict\", action='store_true')\n parser.add_argument(\"tagtype\", choices = [\"all\", \"gram\", \"misc\", \"grampostag\", \"gramnegtag\",\n \"grampostag-posanchor\", \"grampostag-neganchor\",\n \"gramnegtag-posanchor\", \"gramnegtag-neganchor\",\n \"gramposanchor\", \"gramneganchor\", \"miscposanchor\",\n \"miscneganchor\"])\n args = parser.parse_args()\n\n search_en, search_fr = False, False\n if args.fr_en_or_both in [\"both\", \"en\"]: search_en = True\n if args.fr_en_or_both in [\"both\", \"fr\"]: search_fr = True\n\n tt = args.tagtype\n\n if \".gz\" in args.english_file: en_file = gzip.open(args.english_file, \"rt\", encoding=\"utf-8\")\n else: en_file = open(args.english_file, \"rt\", encoding=\"utf-8\")\n\n if \".gz\" in args.french_file: fr_file = gzip.open(args.french_file, \"rt\", encoding=\"utf-8\")\n else: fr_file = open(args.french_file, \"rt\", encoding=\"utf-8\")\n \n if tt==\"all\":\n tag_questions(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"gram\":\n gramtag_questions(en_file, fr_file, search_en, search_fr, args.strict) \n elif tt==\"misc\":\n misctag_questions(en_file, fr_file, search_en, search_fr, args.strict)\n \n elif tt==\"grampostag\":\n gramtag_postag(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"gramnegtag\":\n gramtag_negtag(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"gramposanchor\":\n gramtag_posanchor(en_file, fr_file, search_en, search_fr, args.strict) \n elif tt==\"gramneganchor\":\n gramtag_neganchor(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"grampostag-posanchor\":\n gramtag_postagposanchor(en_file, fr_file, search_en, search_fr, args.strict) \n elif tt==\"grampostag-neganchor\":\n gramtag_postagneganchor(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"gramnegtag-neganchor\":\n gramtag_negtagneganchor(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"gramnegtag-posanchor\":\n gramtag_negtagposanchor(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"miscposanchor\":\n misctag_posanchor(en_file, fr_file, search_en, search_fr, args.strict)\n elif tt==\"miscneganchor\":\n misctag_neganchor(en_file, fr_file, search_en, search_fr, args.strict)\n\n en_file.close()\n fr_file.close()\n\n \n","sub_path":"scripts/classify_tag_questions.py","file_name":"classify_tag_questions.py","file_ext":"py","file_size_in_byte":33806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"127898774","text":"def fun(n1, n2, n3):\n res = 1\n while (n2 > 0):\n if (n2 & 1):\n res = (res * n1) % n3\n n2 = n2 >> 1\n n1 = (n1 ** 2) % n3\n return res\n\n\nx = 1000000007\nt = int(input())\nfor _ in range(t):\n n = int(input())\n ans = fun(2, n - 1, x)\n print(ans)","sub_path":"Codechef/Python Solutions/XOREquality.py","file_name":"XOREquality.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"218390682","text":"import os\nimport sys\nimport warnings\nfrom .version import __version__, git_version\nimport torch\n\ncwd = os.path.dirname(os.path.abspath(__file__))\n\nFI_PROVIDER_PATH = os.path.join(cwd, \"lib/prov\")\nos.environ['FI_PROVIDER_PATH'] = FI_PROVIDER_PATH\n\nfrom . import _C as ccl_lib\n\nif hasattr(torch, 'xpu'):\n if torch.xpu.is_available():\n # try:\n # load the CCL/XPU library\n import ctypes\n my_c_library = ctypes.cdll.LoadLibrary(os.path.join(cwd, \"lib/libtorch_ccl_xpu.so\"))\n # except OSError:\n # print(\"Cannot load xpu CCL. CCL doesn't work for XPU device\")\n\n__all__ = []\n__all__ += [name for name in dir(ccl_lib)\n if name[0] != '_' and\n not name.endswith('Base')]\n\n\ndef is_available(tensors):\n devices = set()\n for tensor in tensors:\n if not tensor.is_contiguous():\n return False\n device = tensor.get_device()\n if device in devices:\n return False\n devices.add(device)\n\n return True\n\n","sub_path":"torch_ccl/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"348178214","text":"import subprocess, argparse, glob, sys, os\n\nDEFAULT_SUBTITLE_FORMAT = 'srt'\nDEFAULT_CONCURRENCY = 10\nDEFAULT_SRC_LANGUAGE = 'en'\nDEFAULT_DST_LANGUAGE = 'en'\n\nparser = argparse.ArgumentParser()\nparser.add_argument('source_path', default = '', help=\"Path to the video or audio file to subtitle. Default is all files in the current directory that end in .mov or .mp4.\",\n nargs='?')\nparser.add_argument('-C', '--concurrency', help=\"Number of concurrent API requests to make\",\n type=int, default=DEFAULT_CONCURRENCY)\nparser.add_argument('-o', '--output',\n help=\"Output path for subtitles (by default, subtitles are saved in \\\n the same directory and name as the source path)\")\nparser.add_argument('-F', '--format', help=\"Destination subtitle format\",\n default=DEFAULT_SUBTITLE_FORMAT)\nparser.add_argument('-S', '--src-language', help=\"Language spoken in source file\",\n default=DEFAULT_SRC_LANGUAGE)\nparser.add_argument('-D', '--dst-language', help=\"Desired language for the subtitles\",\n default=DEFAULT_DST_LANGUAGE)\nparser.add_argument('-K', '--api-key',\n help=\"The Google Translate API key to be used. \\\n (Required for subtitle translation)\")\nparser.add_argument('--list-formats', help=\"List all available subtitle formats\",\n action='store_true')\nparser.add_argument('--list-languages', help=\"List all available source/destination languages\",\n action='store_true')\n\nargs = parser.parse_args()\n\nif sys.platform == 'darwin':\n app_path = '/Applications/autosub/autosub'\nelif sys.platform == 'win32':\n app_path = '%ProgramFiles%/autosub/autosub'\nelse:\n print('Only Mac and Windows are currently supported. Exiting.')\n sys.exit(1)\n\nif not (args.list_formats or args.list_languages):\n if not args.source_path:\n args.source_path = glob.glob('*.mov') + glob.glob('*.mp4')\n else: \n args.source_path = glob.glob(args.source_path)\n\n if not args.output:\n args.output = [file[:-4] + '.srt' for file in args.source_path]\n else:\n args.output = args.output.split(':')\n\n if not len(args.output) == len(args.source_path):\n print(\"Number of videos doesn't match number of specified output files. Exiting.\")\n sys.exit(1)\n\n if not args.api_key:\n keystr = ''\n else:\n keystr = ' -K ' + str(args.api_key)\n\n for source, output in tuple(zip(args.source_path, args.output)):\n subprocess.call('python \"' + app_path + '\" \"' + str(source) + '\" -C ' + str(args.concurrency) + ' -o \"' + str(output) + '\" -F ' + str(args.format) + ' -S ' + str(args.src_language) + ' -D ' + str(args.dst_language) + str(keystr), cwd = os.getcwd(), shell = True)\n\nelif args.list_formats:\n subprocess.Popen('python \"' + app_path + '\" --list-formats', cwd = os.getcwd(), shell = True).communicate()\n\nelif args.list_languages:\n subprocess.Popen('python \"' + app_path + '\" --list-languages', cwd = os.getcwd(), shell = True).communicate()\n\n","sub_path":"autosub-helper.py","file_name":"autosub-helper.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"532004337","text":"import threading\nfrom web_fetcher import *\nfrom web_fetcher_simple import *\nfrom parser import *\nimport string\nimport time\n\ncycle = 60\n\nTYPE_KAIYUAN = \"KAIYUAN\"\nTYPE_DEGUOREXIAN = \"DEGUOREXIAN\"\nTYPE_HUARENJIE = \"HUARENJIE\"\n\n\nclass comparer(threading.Thread):\n def __init__(self, callback):\n super(comparer, self).__init__()\n self.type_l = [TYPE_KAIYUAN, TYPE_DEGUOREXIAN, TYPE_HUARENJIE]\n self.callback = callback\n\n self.rets=[[],[],[]]\n self.old_rets=[[],[],[]]\n\n def run(self):\n while True:\n for i in range(0, 3):\n fetch = web_fetcher_simple(self.type_l[i])\n ret = fetch.process()\n if ret != RET_OK:\n continue\n\n psr = parser(self.type_l[i], fetch.pg_src)\n ret = psr.process()\n if ret != RET_OK:\n continue\n\n if len(psr.results) == 0:\n continue\n\n self.rets[i] = psr.results\n self.compare(i)\n self.old_rets[i] = self.rets[i]\n #time.sleep(1)\n\n print(self.rets)\n print(self.old_rets)\n time.sleep(cycle)\n\n\n def compare(self, index):\n #print(self.type_l[index])\n #print(self.rets[index])\n if len(self.old_rets[index]) == 0:\n if len(self.rets[index]) == 0:\n return RET_OK\n else:\n self.callback(self.type_l[index], self.rets[index][0])\n return RET_OK\n\n if self.old_rets[index][0] == self.rets[index][0]:\n return RET_OK\n else:\n self.callback(self.type_l[index], self.rets[index][0])\n return RET_OK\n","sub_path":"comparer.py","file_name":"comparer.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"333459145","text":"# -*- coding: utf-8 -*-\nimport re\n\n# 将正则表达式编译成 Pattern 对象\npattern = re.compile(r'\\d+')\n# 使用 search() 查找匹配的子串，不存在匹配的子串时将返回 None\n# 这里使用 match() 无法成功匹配\nm = pattern.search('hello 123456 789')\nif m:\n # 使用 Match 获得分组信息\n print( 'matching string:', m.group())\n # 起始位置和结束位置\n print( 'position:', m.span())","sub_path":"webSpider/study/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"190604041","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/ali/ownCloud/Project/python/django-aparnik-framework-project/testandbuildprojectframework/aparnik/contrib/settings/migrations/0016_auto_20190204_1825.py\n# Compiled at: 2020-01-05 09:49:45\n# Size of source mod 2**32: 1812 bytes\nfrom django.db import migrations\n\ndef add_keys(apps, schema_editor):\n \"\"\"\n We can't import the Post model directly as it may be a newer\n version than this migration expects. We use the historical version.\n \"\"\"\n import django.apps as apps\n Setting = apps.get_model('settings', 'Setting')\n FileField = apps.get_model('filefields', 'FileField')\n key = ''\n for key_file_type, value_file_type in FileField.FILE_TYPE:\n try:\n value = ''\n if key_file_type == 'P':\n value = 'https://cdn.aparnik.com/static/img/icon_pdf.png'\n else:\n if key_file_type == 'M':\n value = 'https://cdn.aparnik.com/static/img/icon_movie.png'\n else:\n if key_file_type == 'V':\n value = 'https://cdn.aparnik.com/static/img/icon_voice.png'\n else:\n if key_file_type == 'I':\n value = 'https://cdn.aparnik.com/static/img/icon_image.png'\n else:\n if key_file_type == 'L':\n value = 'https://cdn.aparnik.com/static/img/icon_image.png'\n else:\n continue\n key = 'FILE_TYPE_%s_ICON' % key_file_type\n Setting.objects.get(key=key)\n except Exception:\n Setting.objects.create(title=('آیکن مربوط به فایل تایپ %s' % value_file_type),\n key=key,\n value=value,\n value_type='s',\n is_show=False,\n is_variable_in_home=False)\n\n\nclass Migration(migrations.Migration):\n dependencies = [\n ('settings', '0015_auto_20190201_1930')]\n operations = [\n migrations.RunPython(add_keys, reverse_code=(migrations.RunPython.noop))]","sub_path":"pycfiles/django-apar-1.1.6.45.tar/0016_auto_20190204_1825.cpython-37.py","file_name":"0016_auto_20190204_1825.cpython-37.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57983872","text":"from django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.http import require_POST, require_GET\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.contrib.auth import authenticate, login, logout\n\nfrom django.http import HttpResponse\nfrom django.urls import reverse_lazy\nfrom django.shortcuts import render\n\nfrom webApp.models import NewsStories, Authors\nfrom datetime import datetime, date\nimport datetime\nimport json\n\n# Create your views here.\n\n'''\nLog In \n\t//Success - 200 OK w/ text/plain payload giving some welcome \n\t//Fail - 401 w/ Error message \n'''\n@csrf_exempt\ndef user_login(request):\n try:\n #Get details from login request\n username = request.POST['username']\n password = request.POST['password']\n #attempt tp authenticate user\n user = authenticate(username=username, password=password)\n\n #returns none if not authenticated\n if user is not None:\n if user.is_active:\n #logs the user in\n login(request, user)\n\n #checks if login was successfull\n if(request.user.is_authenticated):\n return HttpResponse('Login succesful, welcome!', status=200)\n else:\n return HttpResponse('Invalid Login', status = 401)\n else:\n return HttpResponse('Invalid Login',status = 401)\n else:\n # Return an 'invalid login' error message.\n return HttpResponse('Invalid Login', status = 401)\n except Exception:\n return HttpResponse('Error occured, unable to login', status = 401)\n\n \n'''\nLog Out\n\t//Success - 200 OK w/ text/plain payload giving goodbye message\n\t// Fail - 401 \n'''\n@require_POST\n@csrf_exempt\n@login_required(login_url='/loginRequired')\ndef user_logout(request):\n try:\n #first check that user is logged in\n if(request.user.is_authenticated):\n #log the user out\n logout(request)\n # give succesfull response\n return HttpResponse('OK',status=200)\n else:\n return HttpResponse('Error Occured, unable to logout',status=401)\n except Exception:\n return HttpResponse('Error Occured, unable to logout',status=401)\n\n\n \n'''\nPost A Story \n\t// Success - 201 CREATED \n\t// Fail - 503 Service Unavilable w/ text/plain\n'''\n@require_POST\n@csrf_exempt\n@login_required(login_url='/loginRequired')\ndef story_post(request):\n #get body contents from reqeust\n content = json.loads(request.body)\n\n #extract all of the data\n headline = content['headline']\n category = content['category']\n region = content['region']\n details = content['details']\n\n categoryChoices =['pol', 'art','tech','trivia']\n regionChoices =['uk','eu','w']\n\n #make sure choices are correct\n if(category in categoryChoices) and (region in regionChoices) and isinstance(headline,str) and isinstance(details,str):\n #date of creation\n dateStamp = date.today()\n\n #author is user logged in\n #author object needed as it's the foreign key\n author = Authors(author_username = request.user)\n story = NewsStories(story_headline = headline, story_cat =category, story_region =region, story_details=details, story_date=dateStamp, story_author=author)\n #save\n author.save()\n story.save()\n\n return HttpResponse('CREATED',status=201)\n\n else:\n return HttpResponse('Validation Failed')\n\n'''\nGet Stories\n\t// Success - 200 OK \t\treplies w/ \tJSON payload \n\t\t+ key, string\n\t\t+ headline, string\n\t\t+ story_cat, string\n\t\t+ story_region, string\n\t\t+ author, string\n\t\t+ story_date, string\n\t\t+ story_details, string \n\t// Fail - 404 w/ text/plain \n'''\n\ndef story_get(request):\n #get body of the request\n content = json.loads(request.body)\n #set to null initially\n category = \"\"\n region = \"\"\n\n #if not star then assign the search query value\n if content['story_cat'] != '*':\n category = content['story_cat']\n\n #if not star then assign the search query value\n if content['story_region'] !='*':\n region = content['story_region']\n\n #if no date star then run query without date filter\n #__ contains used as all of the string will contain the null character\n #Only looking for null character if it's a * for that field\n if content['story_date'] == '*':\n all_stories = NewsStories.objects.all().filter(story_cat__contains=category, story_region__contains=region)\n else:\n date = datetime.datetime.strptime(content['story_date'], '%d/%m/%Y').date()\n all_stories = NewsStories.objects.all().filter(story_cat__contains=category, story_region__contains=region).filter(story_date__gte=date)\n\n #checks to see how many stories found\n if not all_stories:\n return HttpResponse(\"No stories found matching criteria \", status=404)\n else:\n #array of stories\n stories = []\n #loops through each story found and creates a JSON object\n for story in all_stories:\n temp = {\n \"key\": str(getattr(story, \"story_id\")),\n \"headline\": str(getattr(story, \"story_headline\")),\n \"story_cat\" : str(getattr(story, \"story_cat\")),\n \"story_region\" : str(getattr(story, \"story_region\")),\n \"author\" : str(getattr(story, \"story_author\")),\n \"story_date\" : str(getattr(story, \"story_date\")),\n \"story_details\": str(getattr(story, \"story_details\")),\n }\n #appends it to the end of the array\n stories.append(temp)\n\n #array then converted into JSON array of stories\n text= {}\n text['stories'] = stories\n return HttpResponse(json.dumps(text), status=200)\n\n\n\n\n\n'''\nDelete Stories \n+ Delete a story \n- POST -> /api/deletestory/ \tin \tJSON \n\t+ story_key, string\n\t// Success - 201 CREATED\n\t// Fail - 503 Service unavailable w/ text/plain giving reason \n'''\n\n@csrf_exempt\n@login_required(login_url='/loginRequired')\ndef story_delete(request):\n try:\n #loads data body\n content = json.loads(request.body)\n #extracts key of the stroy to be delteted\n key = content['story_key']\n #deletes story\n NewsStories.objects.filter(story_id=key).delete()\n return HttpResponse('CREATED',201)\n except Exception:\n return HttpResponse('Error occured, record not deleted',501)\n\ndef loginRequired(request):\n return HttpResponse('Login required to view page',401)\n","sub_path":"webProj/webApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"251338572","text":"from sheets import prod\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport time\nfrom datetime import datetime\nimport requests\nimport pytz\nimport os\n\ndef print_progress_bar(iteration, total, prefix=\"\", suffix=\"\", length=30, fill=\"=\", head=\">\", track=\".\"):\n filled_length = int(length * iteration // total)\n if filled_length == 0:\n bar = track * length\n elif filled_length == 1:\n bar = head + track * (length - 1)\n elif filled_length == length:\n bar = fill * filled_length\n else:\n bar = fill * (filled_length-1) + \">\" + \".\" * (length-filled_length)\n print(\"\\r\" + prefix + \"[\" + bar + \"] \" + str(iteration) + \"/\" + str(total), suffix, end = \"\\r\")\n if iteration == total: \n print()\n\n#################GAMEOLOGY################################\n##########################################################\nfor index,link in enumerate(prod.col_values(11)[1:]):\n print(index,link)\n if link != '':\n response = requests.get(link)\n soup = BeautifulSoup(response.text,'lxml')\n print_progress_bar(index+1,prod.col_count)\n price = soup.find('span', attrs={\n 'class':'price'\n })\n if price != None: prod.update_acell(\"G{}\".format(index+2),price.string)\n\n################Games Empire##############################\n##########################################################\n\nfor index,link in enumerate(prod.col_values(12)[1:]):\n if link != '':\n response = requests.get(link)\n soup = BeautifulSoup(response.text,'lxml')\n print_progress_bar(index,prod.col_count)\n price = soup.find('div', attrs={\n 'class':'price--main'\n })\n if price != None: prod.update_acell(\"H{}\".format(index+2),price.find('span',class_='money').string.strip())\n\n\n################Gamesmen##################################\n##########################################################\n\nfor index,link in enumerate(prod.col_values(13)[1:]):\n if link != '':\n response = requests.get(link)\n soup = BeautifulSoup(response.text,'lxml')\n print_progress_bar(index,prod.col_count)\n price = soup.find('span', attrs={\n 'class':'price'\n })\n if price != None: prod.update_acell(\"I{}\".format(index+2),price.string) \n\n################Advent Games##############################\n##########################################################\n\nfor index,link in enumerate(prod.col_values(14)[1:]):\n if link != '':\n print(index,link)\n response = requests.get(link)\n soup = BeautifulSoup(response.text,'lxml')\n print_progress_bar(index,prod.col_count)\n price = soup.find('div', attrs={\n 'class':'our-price'\n })\n if price != None: prod.update_acell(\"J{}\".format(index+2),('$' + price.find('meta')['content'])) ","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"432893957","text":"import numpy as np\nfrom itertools import product\nfrom scipy.spatial.distance import cdist, squareform\n\nfrom ase import Atoms\nfrom ase.io import read, write\nfrom ase.visualize import view\n\ndef loadStructure():\n atoms = read('fromThomas/data_SnO.traj', index=':')\n structure = atoms[0]\n return structure\n\n\nif __name__ == \"__main__\":\n struct = loadStructure()\n\n pos = struct.positions\n pbc = struct.get_pbc()\n cell = struct.get_cell()\n Natoms = struct.get_number_of_atoms()\n atomic_numbers = struct.get_atomic_numbers() # num\n atomic_types = sorted(list(set(atomic_numbers)))\n atomic_count = [list(atomic_numbers).count(i) for i in atomic_types]\n volume = struct.get_volume()\n print(pbc)\n print(cell)\n print('Natoms:', Natoms)\n print(atomic_numbers)\n print(atomic_types)\n print(atomic_count)\n print(volume)\n\n pos = struct.positions\n\n xyz = np.array([1,3,2])\n cell_displacement = xyz @ cell\n displaced_pos = cell_displacement + pos\n print(displaced_pos)\n\n deltaRs = np.apply_along_axis(np.linalg.norm,1,displaced_pos-pos[0])\n deltaRs2 = cdist(pos[0].reshape((1,3)), displaced_pos)\n print(deltaRs)\n print(deltaRs2)\n\n kk = {type:list(atomic_numbers).count(type) for type in atomic_types}\n\n print(kk)\n\n","sub_path":"krrThomas/aseExamples.py","file_name":"aseExamples.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"479031861","text":"import numpy as np\nimport argparse\nimport pickle\n\nimport matplotlib.pyplot as plt\n\n#import MNIST_Loader\n\n\nclass NN(object):\n\n def __init__(self,\n input_size=3072,\n output_size=10,\n hidden_layers_size=[512, 1024],\n init='zeros',\n activation='sigmoid',\n lr=0.1):\n\n self.input_size = input_size\n self.hidden_layers_size = hidden_layers_size\n self.output_size = output_size\n self.lr = lr\n self.train = False\n self.init=init\n\n self._initialize_weights(init)\n self._initialize_activation(activation)\n\n def _initialize_weights(self, init_method):\n def _random(neurons_in, neurons_out):\n return np.random.normal(0, 0.01, (neurons_in, neurons_out))\n def _zeros(neurons_in, neurons_out):\n return np.zeros((neurons_in, neurons_out))\n def _normal(neurons_in, neurons_out):\n return np.random.normal(0, 1, (neurons_in, neurons_out))\n def _glorot(neurons_in, neurons_out):\n low = -np.sqrt(6 / (neurons_in + neurons_out))\n high = np.sqrt(6 / (neurons_in + neurons_out))\n return np.random.uniform(low, high, (neurons_in, neurons_out))\n\n init_weights = {\n 'random': _random,\n 'zeros': _zeros,\n 'normal':_normal,\n 'glorot': _glorot\n }\n\n sizes = [self.input_size] + self.hidden_layers_size + [self.output_size]\n self.W = [init_weights[init_method](sizes[i], sizes[i+1]) for i in range(len(sizes) - 1 )]\n self.b = [np.zeros((1, neurons)) for neurons in sizes[1:]]\n\n def _initialize_activation(self, activation):\n if activation == 'sigmoid':\n self.activation_f, self.activation_deriv_f = self._sigmoid, self._sigmoid_deriv\n elif activation == 'tanh':\n self.activation_f, self.activation_deriv_f = self._tanh, self._tanh_deriv\n elif activation == 'linear':\n self.activation_f, self.activation_deriv_f = self._linear, self._linear_deriv\n\n def _sigmoid(self, X):\n return 1 / (1 + np.exp(-X))\n\n def _sigmoid_deriv(self, X):\n return self._sigmoid(X) * (1 - self._sigmoid(X))\n\n def _tanh(self, X):\n return (np.exp(X) - np.exp(-X)) / (np.exp(X) + np.exp(-X))\n\n def _tanh_deriv(self, X):\n return 1 - self._tanh(X)**2\n\n def _linear(self, X):\n return X\n\n def _linear_deriv(self, X):\n return 1\n\n def _add_bias(self, h, W, b):\n h = np.concatenate([h, np.ones((1, h.shape[1]))], axis=0)\n W = np.concatenate([W, b])\n return h, W\n\n def forward(self, X, model_W=None):\n model_W = self.W if model_W is None else model_W\n h = X\n cache = [(h, None)]\n for i, (W, b) in enumerate(zip(model_W, self.b)):\n # Add bias\n hb, Wb = self._add_bias(h, W, b)\n a = np.dot(Wb.T, hb)\n # Different activation function for last layer (softmax)\n if i == len(model_W) - 1:\n h = self._softmax(a)\n else:\n h = self.activation_f(a)\n cache.append((h, a))\n return h, cache\n\n def _softmax(self, X):\n \"\"\" Softmax activation function \"\"\"\n e_x = np.exp(X - np.max(X))\n return e_x / e_x.sum(axis=0)\n\n def loss(self, prediction, target):\n \"\"\" Cross-entropy loss \"\"\"\n return -np.log((prediction * target).sum(axis=0)).mean()\n\n def backward(self, target, prediction, cache):\n grads = []\n grad_a = - (target - prediction)\n for i in range(len(cache) - 1):\n index = len(cache) - i - 2\n grad_W = np.dot(grad_a, cache[index][0].T)\n grad_b = np.sum(grad_a, axis=1)\n if index:\n grad_h = np.dot(self.W[index], grad_a)\n grad_a = np.multiply(grad_h, self.activation_deriv_f(cache[index][1]))\n grads.append((grad_W.T, grad_b))\n return [g for g in reversed(grads)]\n\n def update_weights(self, grads, batch_size):\n if not self.train:\n raise Exception('You should not update weights while validating/testing')\n self.W = [self.W[i] - (self.lr * grads[i][0] / batch_size) for i in range(len(self.W))]\n self.b = [self.b[i] - (self.lr * grads[i][1] / batch_size) for i in range(len(self.W))]\n\n def training(self):\n self.train = True\n\n def eval(self):\n self.train = False\n\n\ndef check_grads(model, batch, p=1):\n model_input, target = preprocess(batch)\n # Keep only one example\n model_input = model_input[:, :1]\n target = target[:, :1]\n # Get the grads\n prediction, cache = model.forward(model_input)\n grads = model.backward(target, prediction, cache)\n # Get the numerical approximation of grads for p values for different values of N\n diff = []\n legends = []\n for k in range(5):\n N = 10**k\n # Get the numerical app gradients\n num_grads = get_numerical_grads(model_input, target, model, N, p)\n # Compare the numerical and the 'real' gradients\n diff.append(np.max(abs(grads[2][0][:p, :model.W[2].shape[1]] - num_grads[:p, :model.W[2].shape[1]])))\n legends.append(f'N = {N}')\n # Plot the difference\n plt.plot(diff)\n plt.legend(legends)\n plt.show()\n\n\ndef get_numerical_grads(X, y, model, N, p):\n num_grad = np.zeros(model.W[2].shape)\n perturb = np.zeros(model.W[2].shape)\n e = 1 / N\n for i in range(p):\n for j in range(model.W[2].shape[1]):\n perturb[i, j] = e\n W = model.W.copy()\n W[2] += perturb\n loss2 = model.loss(model.forward(X, model_W=W)[0], y)\n W[2] -= 2 * perturb\n loss1 = model.loss(model.forward(X, model_W=W)[0], y)\n num_grad[i, j] = (loss2 - loss1) / (2 * e)\n perturb[i, j] = 0\n return num_grad\n\n\ndef get_accuracy(target, prediction):\n res = np.argmax(target, axis=0) == np.argmax(prediction, axis=0)\n return len(res[res]) / len(res)\n\n\ndef train(model, trainset, validset, testset, epochs, check_grad=False):\n loss_vector = np.zeros([epochs, 1])\n patience = 0\n best_accuracy = 0\n best_W = None\n best_b = None\n\n\n for epoch in range(epochs):\n\n # Training\n loss = 0\n model.training()\n\n for i, batch in enumerate(zip(*trainset)):\n batch_x, batch_y = batch\n target = preprocess(np.asarray([batch_y]))\n model_input = np.asarray([batch_x]).transpose()\n prediction, cache = model.forward(model_input)\n grads = model.backward(target, prediction, cache)\n model.update_weights(grads, batch_size=len(batch))\n loss += model.loss(prediction, target)\n loss_vector[epoch, 0] = loss / (i+1)\n print(f'Train loss={loss / (i + 1)} at epoch {epoch}')\n\n # Validation\n loss = 0\n accuracy = 0\n model.eval()\n for i, batch in enumerate(zip(*validset)):\n batch_x, batch_y = batch\n target = preprocess(np.asarray([batch_y]))\n model_input = np.asarray([batch_x]).transpose()\n prediction, _ = model.forward(model_input)\n loss += model.loss(prediction, target)\n accuracy += get_accuracy(target, prediction)\n print(f'Valid loss={loss / (i + 1)} at epoch {epoch}')\n print(f'Valid accuracy={accuracy / (i+1)} at epoch {epoch}')\n\n if accuracy / (i + 1) > best_accuracy:\n best_accuracy = accuracy / (i + 1)\n best_W = model.W.copy()\n best_b = model.b.copy()\n patience = 0\n else:\n patience += 1\n\n if patience > 2:\n break\n\n if check_grad:\n # Hack to get the first batch only\n for batch in trainset:\n check_grads(model, batch)\n break\n\n model.W = best_W\n model.b = best_b\n\n test_accuracy = 0\n model.eval()\n for j, batch in enumerate(zip(*testset)):\n batch_x, batch_y = batch\n target = preprocess(np.asarray([batch_y]))\n model_input = np.asarray([batch_x]).transpose()\n prediction, _ = model.forward(model_input)\n test_accuracy += get_accuracy(target, prediction)\n print(f'Test accuracy={test_accuracy / (j+1)}')\n\n return loss_vector, accuracy / (i+1), test_accuracy / (j+1)\n\n\ndef preprocess(target, n_class=10):\n \"\"\"\n Transform model_input in flat vector and target in one-hot encoded\n \"\"\"\n target_one_hot = np.zeros((n_class, target.shape[0]))\n target_one_hot[target, np.arange(target.shape[0])] = 1\n\n return target_one_hot\n\n\ndef plot_loss(loss_vector, epochs, legend):\n t = np.arange(loss_vector.size)\n plt.ylabel('Average Loss')\n plt.xlabel('Epoch')\n plt.title('Initialization Effect')\n plt.grid(True)\n plt.xlim(0, epochs)\n plt.plot(t, loss_vector)\n plt.legend(legend, loc='upper right')\n plt.show()\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--epochs', default=20, type=int)\n parser.add_argument('--batch_size', default=256, type=int)\n parser.add_argument('--lr', default=1.e-2, type=float)\n parser.add_argument('--activation', default='sigmoid')\n parser.add_argument('--init', default='glorot')\n parser.add_argument('--h1', default=512, type=int)\n parser.add_argument('--h2', default=1024, type=int)\n args = parser.parse_args()\n\n init_methods = ['zeros', 'normal', 'glorot'] if args.init == 'all' else [args.init]\n\n # The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes.\n # The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks.\n # There are 6,000 images of each class.\n datapath = 'cifar10.pkl'\n\n if datapath is not None:\n u = pickle._Unpickler(open(datapath, 'rb'))\n u.encoding = 'latin1'\n trainset, validset, testset = u.load()\n else:\n trainset, validset, testset = None, None, None\n\n #tuples: features = trainset[0], targets = trainset[1]\n # trainset = (trainset[0][:10], trainset[1][:10])\n # validset = (validset[0][:10], validset[1][:10])\n # testset = (testset[0][:10], testset[1][:10])\n\n #trainset, validset, testset = MNIST_Loader.load_dataset(args.batch_size)\n\n for init in init_methods:\n model = NN(hidden_layers_size=[args.h1, args.h2],\n init=init,\n activation=args.activation,\n lr=args.lr)\n\n results = train(model=model,\n trainset=trainset,\n validset=validset,\n testset=testset,\n epochs=args.epochs)\n loss_vector, valid_accuracy, test_accuracy = results\n with open('hp_results.txt', 'a') as text_file:\n text_file.write(f'{args.epochs} {args.batch_size} {args.lr} {args.activation} {args.init} {args.h1} {args.h2} {valid_accuracy} {test_accuracy}\\n')\n\n if args.init == 'all':\n plot_loss(loss_vector, epochs=args.epochs, legend=init_methods)\n","sub_path":"examples/ift_6135/problem_1.py","file_name":"problem_1.py","file_ext":"py","file_size_in_byte":11182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"468718238","text":"import time\nimport datetime\ntime.time()\ntime_now = datetime.datetime.fromtimestamp(\n int(time.time())\n ).strftime('%d-%m-%Y %H:%M:%S')\n\n\ntemp = \"05-01-2018 09:21:20\"\n\n\nprint('Tími núna --> {}, Tíma tékkaður{}'.format(time_now,temp))\n\nif datetime.date(temp) > datetime.date(time_now):\n print('petur er faggi')\n\nif datetime.date(temp) < datetime.date(time_now):\n print('petur er meiri faggi')","sub_path":"Testing_Environment.py","file_name":"Testing_Environment.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"463912597","text":"from re import search\nfrom typing import List, Dict, Any\nfrom urllib.parse import urljoin\n\nfrom bs4 import BeautifulSoup\n\nfrom base_offer import BaseOffer\nfrom crawlers.crawler import Crawler, create_browser\nfrom offer import Offer\n\nOFFER_LIST = 'https://portal.immobilienscout24.de/ergebnisliste/25479099/1?sid=89llld3tht7l7u4ssnbu88q961'\n\n\nclass Milia(Crawler):\n\n def get_offer_link_list(self) -> List[Dict[str, Any]]:\n browser = create_browser()\n browser.open(OFFER_LIST)\n offers = [\n {\n 'fetch': lambda rel_link=link['href']: self.get_offer(urljoin(OFFER_LIST, rel_link)),\n 'offer': BaseOffer(link=urljoin(OFFER_LIST, link['href'])),\n 'crawler': 'Milia'\n }\n for link in browser.page.select('.result__list--element h3 > a')\n ]\n browser.close()\n return offers\n\n def get_offer(self, link: str) -> Offer:\n browser = create_browser()\n browser.open(link)\n offer = Offer(\n address=browser.page.find('div', class_='expose--text__address').text,\n email=None,\n images=[\n urljoin(OFFER_LIST, img['src'][:img['src'].index('/ORIG')])\n for img in browser.page.select('img.sp-thumbnail')\n ],\n link=link,\n rent={\n 'price': int(search('\\d+', extract_information_from_table(browser.page, 'Gesamtmiete:')).group()),\n 'total': True\n },\n rooms=extract_information_from_table(browser.page, 'Zimmer:'),\n size=int(search('\\d+', extract_information_from_table(browser.page, 'Wohnfläche ca.:')).group()),\n title=browser.page.select('.is24__block__responsive--col1 .expose--text > h4')[0].text\n )\n browser.close()\n return offer\n\n\ndef extract_information_from_table(page: BeautifulSoup, attribute: str) -> str:\n table_rows = page.find_all('li')\n return next((\n table_row.select('p:nth-child(2)')[0].text\n for table_row in table_rows\n if table_row.select('p:first-child') and table_row.select('p:first-child')[0].text == attribute\n ), 'NaN')\n","sub_path":"crawlers/milia.py","file_name":"milia.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"539554385","text":"_author_ = 'Harsha'\n\n# variable can be start by using Lower/Uppercase/Underscore --> _var or Var or var\n\n_Name = \"Dhulipala\"\nName1 = \"Vijaya\"\nname2 = \"Harsha\"\nage = 27\n\nprint(_Name + ' ' + Name1 + ' ' + name2 )\n\n# print (Name1 + age)\n# TypeError: must be str, not int\n# We cant concatenate a String and an Int datatypes\n\n#print (Name1 + age)\n\nprint (age)\n\n","sub_path":"05 The Basics of Python/Session 3.py","file_name":"Session 3.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"224868534","text":"from math import log10\n\nfrom jiayan.globals import re_zh_include, stopchars\n\n\"\"\"\nthis program first reuses the implementation of tokenizer in jiayan\nthen does some modification based on the specific features of my corpus object: Tang poetry\n\n唐诗语言结构: 唐诗主要分为五言与七言，五言采用2+2+1 或者2+1+2 的句法, 七言采用2+2+1+2 和2+2+2+1 的句法\n分词结合诗歌结构: 在不考虑诗词结构时转移概率仅由语言模型决定，但考虑唐诗结构后转移概率可以与字在诗句中的位置挂钩：\n以下是所有可能的更改模式：\n五言: 前两个字的合并问题 1+1+2+1 -> 2+1+2 ; 1+1+1+2 -> 2+1+2 \n 后三个字的合并问题 2+1+1+1 -> 2+1+2 / 2+2+1 (base on the last 3 words' prob)\n 跨 2字 3字 的问题 1+2+1+1 -> 1+1+1+1\n七言: 前面两两成对合并问题 1+1+2+1+2 -> 2+2+1+2 ; 2+1+1+2+1 -> 2+2+2+1 ; 1+1+1+1+(1+2) -> 2+2+(1+2)\n 后三字合并问题 (2+2)+1+1+1 -> (2+2)+2+1 / (2+2)+1+2\n 跨 2 2 字、跨 4 3 字的问题 1+2+1+(1+2) -> 1+1+1+1+(1+2) ; 2+1+2+2 -> 2+1+1+1+2\n程序中的干预类型\n1. 五言诗句中，2 3如果没有分开，则直接切分\n 七言诗句中，2 2 3如果没有分开，则直接切分\n2. 对仗冲突: \n 前后两句理应对账，诗句的句法也应当基本相同，因此如果出现句法不同的情况，视转移概率的大小，来判断是要拆还是合并\n 目前只实现了 二字不匹配的合并： 1 1 - 2 --> 2 - 2 \n 连续三字与不连续三字的不匹配的合并 3 - 1 2 / 3 - 1 1 1 --> 3 - 3\n 对于拆分问题、三字的 1 2 - 2 1 的不匹配问题干预还没有进行实现\n 此外首联与尾联均不进行对仗的干预\n\n\n* 看起来似乎不经过第一步的隐马尔可夫模型的序列求优，切词已经可以做出很多切分\n* 甚至看起来可以将五字、七字的切分，简化为3字、2字单元的切分，\n* 但以隐马作为一个预处理的结果，再进行干预，这种 分词 - 干预的思路和操作比较直观与易于解释\n* 而且计算开销在这里也不是比较重要的事情，所以就采用这种流程\n\n--------------------- previous documents written by jiayan's author ---------------------\n\nUse HMM to consider word detection as a char sequence tagging problem.\n\nWith a word dict and a char sequence, there could be lots of tokenizing solutions, and the best one will have\nthe biggest multiplication probability of words:\n(see Max Probability Tokenizing: [https://blog.csdn.net/u010189459/article/details/37956689])\np(S) = p(w1) * p(w2) * p(w3)...p(wn)\n\nHowever, without a word dict we don't know how to tokenize the sentence by word. But here we can use\nlanguage model to compute a possible word probability first:\np(w) = p(c1, c2, c3, c4) = p(c1) * p(c2|c1) * p(c3|c1, c2) * p(c4|c1, c2, c3)\n\nHere the word \"w\" is a 4-char word, with c1, c2, c3 and c4, and the probabilities of each char occurring in relative\nposition could be computed with N-grams model.\n\nSo assume the longest word we want is 4-char word, then in a sentence with length L (L char sequence), each char \ncould be in 4 possible positions of one word, and each associates with its probability of being at that position\n(k indicates the kth char in the sequence)\n\n1. the beginning of the word (b): p(ck)\n2. the second char of the word (c): p(ck|ck-1)\n3. the third char of the word (d): p(ck|ck-2, ck-1)\n4. the fourth char of the word (e): p(ck|ck-3, ck-2, ck-1)\n\nSo, a char sequence could be tagged in a char level with labels {b, c, d, e} first, and be chunked based on the\ntags. Now we can see the word level problem is broken down to char level problem with hidden states, so this is the \ndecoding problem of HMM, we can use viterbi algorithm to get the best tag/state sequence for the char/observation\nsequence.\n\nFor viterbi, we need (a)initial starting probabilities of each state, (b)transition probabilities between states, and\n(c)emission probabilities of states emitting different observations. Let's draw a table to see what they should be in\nthis problem:\n\n----------------------------------------------------\n start -> b b b\n c c c\n d d d\n e e e\n\nchar sequence: char1 char2 char3 ...\n-----------------------------------------------------\n\nSo for each char in the sequence, there are 4 possible states.\nFor (a), only \"b\" can start a sequence, so p(b|) = 1, and p(c|) = p(d|) = p(e|) = 0\nFor (b), consider the longest word: \"bcde\", we can see the state transitions are limited in:\n i. b -> b, b -> c: the beginning of a word either goes to a new word beginning, or the 2nd char;\n ii. c -> b, c -> d: the 2nd char either goes to a new word beginning, or the 3rd char;\n iii. d -> b, d -> e: the 3rd char either goes to a new word beginning, or the 4th char;\n iv. e -> b, e -> e: the 4th char either goes to a new word beginning, or the 5th char ...\nFor (c), the emission probability of one char at a certain state could be computed with N-grams model, e.g.,\n p(ck|d) = p(ck|ck-1, ck-2)\n\nThe only parameters that we cannot compute here are transition probabilities, which we can manually set.\n\nDifferences from regular HMM tokenizing:\n(a) regular HMM tokenizing uses label set {B, M, E, S} to tag char sequence, which is very vague to indicate\n exact char position within a word, especially \"M\", thus hard to compute emission probabilities;\n(b) regular HMM tokenizing requires large data to compute transition and emission probabilities, but here our\n goal is the opposite, to generate that word corpus;\n(c) regular HMM tokenizing computes transition probabilities from data, but here we set them manually;\n(d) regular HMM tokenizing computes emission probabilities from data, but here we use char level N-grams \n language model.\n\nDisadvantages:\n(a) slow: read the sentence data to build ngrams from min word length to max word length, and read again to tokenize\n the whole data, and by this to build word corpus; viterbi on each sentence in data\n(b) bad at long word: need to fine tune transition probabilities to control the word lengths, which is time consuming,\n and the detected long words are not as good as short words.\n(c) fake word frequency: since word corpus is built by tokenizing, which can lead to inaccurate sentence splits, the\n word count doesn't reflect true frequency, e.g., \"天下\" in \"于天下\". So we use its true frequency count in \n the ngrams dict when filtering.\n\"\"\"\n\n\nclass TangCharHMMTokenizer:\n\n def __init__(self, lm):\n self.lm = lm\n self.inits = {'b': 0.0, 'c': -3.14e100, 'd': -3.14e100, 'e': -3.14e100}\n\n # the transition probabilities are manually fine tuned;\n # in principle, we would like the word length the shorter the better;\n # low to-b and high to-next-char-in-word transition probs lead to long words;\n # high to-b and low to-next-char-in-word transition probs lead to short words.\n\n # 下面是转移概率的设定，通过对之前人工分词语料的分析，发现成词概率相当高\n # 但是在下面如果设定较高的话，结果比较差，因此仍然是按照低成词概率来设定\n\n # 自己制定的一个概率，为了减少二字成词的概率\n trans = {'bb': 0.9, 'bc': 0.1,\n 'cb': 0.996, 'cd': 0.004,\n 'db': 0.999, 'de': 0.001,\n 'eb': 0.9999, 'ee': 0.0001}\n\n # 以下的是jiayan作者指定的转移概率，可以修改\n # trans = {'bb': 0.85, 'bc': 0.15,\n # 'cb': 0.9925, 'cd': 0.0075,\n # 'db': 0.999, 'de': 0.001,\n # 'eb': 0.9999, 'ee': 0.0001}\n\n # trans = {'bb': 0.8, 'bc': 0.2,\n # 'cb': 0.9925, 'cd': 0.0075,\n # 'db': 0.999, 'de': 0.001,\n # 'eb': 0.9999, 'ee': 0.0001}\n\n # convert the decimal probabilities to logs to avoid overflow\n self.trans = {states: log10(trans_prob) for states, trans_prob in trans.items()}\n\n def tokenize(self, text: str):\n \"\"\" Gets the tags of given sentence, and tokenizes sentence based on the tag sequence.\n \"\"\"\n # split text by whitespaces first, then split each segment into char chunks by zh chars\n for seg in text.strip().split(): # 如果split()没有参数意味着会以whitespace来分割\n if seg:\n for chunk in re_zh_include.split(seg):\n # if zh chars, tokenize them\n if re_zh_include.match(chunk):\n tags = self.viterbi(chunk)\n\n word = chunk[0]\n for i in range(1, len(chunk)):\n if tags[i] == 'b':\n if not self.valid_word(word):\n for char in word:\n yield char\n else:\n yield word\n word = chunk[i]\n else:\n word += chunk[i]\n if word:\n if not self.valid_word(word):\n for char in word:\n yield char\n else:\n yield word\n\n # if not zh chars, we assume they are all punctuations, split them\n else:\n for char in chunk:\n yield char\n\n def sentences(self, words):\n sentences = []\n temp = []\n for w in words:\n if w == '，' or w == '。' or w == ',':\n temp.append(w)\n sentences.append(temp)\n temp = []\n else:\n temp.append(w)\n if temp:\n sentences.append(temp)\n return sentences\n\n def validate(self, sentences):\n if len(sentences) % 2 != 0:\n return -1\n s1 = sentences[0]\n length = 0\n for w in s1:\n length = length+len(w)\n # print(\"length: \"+length)\n for s in sentences:\n temp_length = 0\n for w in s:\n temp_length = temp_length+len(w)\n if temp_length != length:\n return -1\n return length - 1\n\n def intervene_tokenize(self, text: str):\n words = self.tokenize(text)\n sentences = self.sentences(list(words)) # 用于得到分句的分词结果。\n valid_num = self.validate(sentences)\n if valid_num == -1:\n return words\n else:\n self.segment_intervene(sentences, valid_num)\n self.confront_intervene(sentences, valid_num)\n\n new_words = []\n for s in sentences:\n for w in s:\n new_words.append(w)\n return new_words\n\n def segment_intervene(self, sentences, length):\n \"\"\" 用于不合理的拆分的判断与修改\n \"\"\"\n # print(sentences)\n for s in sentences:\n pos = 0\n list_len = len(s)\n i = 0\n seven_two_mark = False\n while i < list_len:\n if length == 5:\n '''五言句子3-2分割为必须'''\n pos += len(s[i])\n if pos == 2:\n i += 1\n break\n if pos >= 3:\n w = s.pop(i)\n s.insert(i, w[:2-pos])\n i += 1\n s.insert(i, w[2-pos:])\n break\n i += 1\n\n elif length == 7:\n ''' 七言句子2-2-3分割为必须 '''\n pos += len(s[i])\n if pos == 2:\n seven_two_mark = True\n if pos >= 3 and not seven_two_mark:\n w = s.pop(i)\n s.insert(i, w[:2-pos])\n i += 1\n s.insert(i, w[2-pos:])\n seven_two_mark = True\n if pos == 4 and seven_two_mark:\n i += 1\n break\n if pos >= 5 and seven_two_mark:\n w = s.pop(i)\n s.insert(i, w[:4-pos])\n i += 1\n s.insert(i, w[4-pos:])\n break\n i += 1\n\n def confront_intervene(self, sentences, length):\n \"\"\" 用于对仗的判断与修改\n \"\"\"\n count = int(len(sentences) / 2)\n '''由于绝句不对仗，律诗大部分首联尾联不对仗，所以取1 ~ count-1'''\n for i in range(1, count-1):\n s1 = sentences[2 * i]\n s2 = sentences[2 * i + 1]\n news1 = []\n news2 = []\n if length == 5:\n p1 = self.compare(self.get_part(s1, 0, 5), self.get_part(s2, 0, 5), 2)\n p2 = self.compare(self.get_part(s1, 1, 5), self.get_part(s2, 1, 5), 3)\n news1.extend(p1[0])\n news1.extend(p2[0])\n news1.append(s1[-1])\n news2.extend(p1[1])\n news2.extend(p2[1])\n news2.append(s2[-1])\n elif length == 7:\n p1 = self.compare(self.get_part(s1, 0, 7), self.get_part(s2, 0, 7), 2)\n p2 = self.compare(self.get_part(s1, 1, 7), self.get_part(s2, 1, 7), 2)\n p3 = self.compare(self.get_part(s1, 2, 7), self.get_part(s2, 2, 7), 3)\n news1.extend(p1[0])\n news1.extend(p2[0])\n news1.extend(p3[0])\n news1.append(s1[-1])\n news2.extend(p1[1])\n news2.extend(p2[1])\n news2.extend(p3[1])\n news2.append(s2[-1])\n\n def compare(self, list1, list2, length):\n \"\"\" 接收两个字符列表，返回一个包含两个调整过的字符列表的大列表\n \"\"\"\n # 这里目前只考虑的是一个分一个没分时合并的问题\n # TODO 一个分一个没分时拆分做法\n result = []\n if length == 2:\n if len(list1) > len(list2):\n if self.seg_score(list1[0]) + self.seg_score(list1[1]) < self.seg_score(list1[0]+list1[1])+log10(0.2):\n temp = list1[0]+list1[1]\n list1 = [temp]\n print(temp)\n elif len(list1) < len(list2):\n if self.seg_score(list2[0]) + self.seg_score(list2[1]) < self.seg_score(list2[0]+list2[1])+log10(0.2):\n temp = list2[0]+list2[1]\n list2 = [temp]\n print(temp)\n elif length == 3:\n # TODO 实际上三个词的分解会涉及到地名被拆分，所以未来估计要加入词表，用来减少这种问题\n # TODO 1-2 2-1如何调整的做法\n if len(list1) == 1:\n temp = list1[0]\n list1 = []\n count = 0\n for w in list2:\n list1.append(temp[count: count+len(w)])\n count += len(w)\n print(list1)\n elif len(list2) == 1:\n temp = list2[0]\n list2 = []\n count = 0\n for w in list1:\n list2.append(temp[count: count+len(w)])\n count += len(w)\n print(list2)\n\n result.append(list1)\n result.append(list2)\n\n return result\n\n def get_part(self, sentence, num, length):\n \"\"\" 这里得到的是诗句中某个部分的集合\n 如果是五言则是2-3的某个部分\n 如果是七言则是2-2-3的某个部分\n 这里是否是五言七言由使用者控制！！！别写错了，因为不想写validate了。。。\n \"\"\"\n start_index = 0\n end_index = 0\n count = 0\n if num == 0:\n start_index = 0\n end_index = 2\n elif num == 1 and length == 5:\n start_index = 2\n end_index = 5\n elif num == 1 and length == 7:\n start_index = 2\n end_index = 4 # 本来这里居然是5，写的时候贪图方便直接复制，铸成大错\n elif num == 3:\n start_index = 4\n end_index = 7\n\n result = []\n for w in sentence:\n count += len(w)\n if start_index < count <= end_index:\n result.append(w)\n return result\n\n def viterbi(self, sent):\n \"\"\" Chooses the most likely char tag sequence of given char sentence.\n \"\"\"\n emits = self.get_emission_probs(sent)\n\n # record the best path for each state for each char, {path1: path_prob, path2: path_prob, ...};\n # paths grow at each decoding step, eventually contains the best paths for each state of last char;\n # we assume the initial state probs = 1st char's emission probs\n paths = {state: prob + self.inits[state] for state, prob in emits[0].items()}\n\n # for each char\n for i in range(1, len(sent)):\n # print(paths)\n\n # record best paths and their probs to all states of current char\n cur_char_paths = {}\n\n # for each state of current char\n for state, emit_prob in emits[i].items():\n\n # record all possible paths and their probs to current state\n cur_state_paths = {}\n\n # for each state of previous char\n for path, path_prob in paths.items():\n trans_states = path[-1] + state\n\n # compute the path prob from a previous state to current state\n if trans_states in self.trans:\n cur_state_paths[path + state] = path_prob + emit_prob + self.trans[trans_states]\n\n # choose the best path from all previous paths to current state\n best_path = sorted(cur_state_paths, key=lambda x: cur_state_paths[x])[-1]\n\n # for current state of current char, we found its best path\n cur_char_paths[best_path] = cur_state_paths[best_path]\n\n # the paths grow by one char/state\n paths = cur_char_paths\n\n return sorted(paths, key=lambda x: paths[x])[-1]\n\n def get_emission_probs(self, sent):\n \"\"\" Computes emission probability of each state emitting relative char in the given char sequence. \"\"\"\n return [\n\n {'b': self.seg_prob(sent[i]),\n 'c': self.seg_prob(sent[i - 1:i + 1]),\n 'd': self.seg_prob(sent[i - 2:i + 1]),\n 'e': self.seg_prob(sent[i - 3:i + 1])\n }\n\n for i in range(len(sent))\n ]\n\n def seg_score(self, seg):\n \"\"\" 这里仅仅获取这这个seg片段的 n-gram score 值\n \"\"\"\n return self.lm.score(' '.join(seg), bos=False, eos=False)\n\n def seg_prob(self, seg):\n \"\"\" Computes the segment probability based on ngrams model.\n If given an empty segment, it means it's impossible for current char to be at current position of a word,\n thus return default low log prob -100.\n \"\"\"\n return (self.lm.score(' '.join(seg), bos=False, eos=False) -\n self.lm.score(' '.join(seg[:-1]), bos=False, eos=False)) \\\n or -100.0\n\n def valid_word(self, word):\n \"\"\" Checks if a word contains stopchars, if yes, it's not a valid word. \"\"\"\n for char in word:\n if char in stopchars:\n return False\n return True\n","sub_path":"jiayan/tokenizer/tang_tokenizer.py","file_name":"tang_tokenizer.py","file_ext":"py","file_size_in_byte":20030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"497688119","text":"\"\"\"\nThis is almost identical to the course schedule 1 problem except you need to use topological sort \nin addition to DFS to get the final ordering.\n\"\"\"\n\n\nfrom collections import defaultdict\nclass Solution(object):\n \n def findOrder(self, numCourses, prerequisites):\n \"\"\"\n :type numCourses: int\n :type prerequisites: List[List[int]]\n :rtype: List[int]\n \n You can use DFS to traverse the graph. \n If you see a loop then the course schedule is not possible\n \n What is the difference between this problem and the last? \n Last one, return true or false based on if it is possible or not.\n This one, need to return one of the possible solutions.\n \n iterate through all the nodes in the graph,\n starting at the node, perform dfs and mark nodes as visited. \n during traversal, if you have already seen that node before, then you have identified a loop,\n meaning that the course schedule is not possible. In this case, you will return an empty list. \n \n once all the nodes have been visited, then you can use topological sort to backtrack and get the correct solution\n \"\"\"\n self.numCourses = numCourses\n self.visited = [False for _ in range(numCourses)]\n self.stack = []\n \n if prerequisites == []:\n return list(range(numCourses))\n \n #first create an adjacency list to represent the graph\n self.graph = defaultdict(list)\n for prereq in prerequisites:\n pre = prereq[0]\n course = prereq[1]\n self.graph[course].append(pre)\n\n #print(self.graph)\n for node in range(numCourses):\n ans = self.dfs(node, self.visited, self.stack)\n if not ans:\n return [] #return an empty list if there is no solution\n \n return self.stack \n \n #this is actually a topological sort using dfs\n def dfs(self, node, visited, stack):\n #print(node, visited)\n if visited[node] == True: #loop detected\n return False\n \n if visited[node] == \"good\":\n return True\n \n visited[node] = True\n\n #print(\"stack\",stack)\n for next_node in self.graph[node]:\n print(\"node,next\", node, next_node)\n if not self.dfs(next_node, visited, stack):\n return False\n \n \n stack.insert(0,node)\n visited[node] = \"good\" \n # after reaching the end, \n # you can mark the node as good, \n # it has already been verified as not having a loop\n \n return True\n\n","sub_path":"leetcode/course_schedule_2.py","file_name":"course_schedule_2.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"176021603","text":"# -*- coding: utf-8 -*-\nimport random\n\nrolled = []\nrolledtimes = 0;\nbiggest = []\n\nfreq = int(input('How many times would you like to roll the dice? '))\n\ndef roll():\n rand = random.randrange(1,7)\n return rand\ndef probability():\n for i in range(0,6):\n print('Calculation of probability:')\n percentage = \"{:.2f}\".format((count[i] / freq)*100)\n percent = str(percentage) + '%'\n print(' ', i + 1, ':', percent)\ndef theoretical():\n result = \"{:.2f}\".format((1/6)*freq)\n denominator = \"{:.2f}\".format(((1/6)*freq)*6)\n print('\\nIn theory, each dice should roll {} out of {} times'.format(result,denominator))\ndef findBiggest():\n for i in range(1,7):\n biggest.append(rolled.count(i))\n print('\\n', 'The most times a dice is rolled is', max(biggest), 'times')\ndef findSmallest():\n for i in range(1,7):\n biggest.append(rolled.count(i))\n print('\\n', 'The least times a dice is rolled is', min(biggest), 'times')\n\nfor i in range(1,freq + 1):\n number = roll()\n rolled.append(number)\n rolledtimes+=1\ncount = [rolled.count(1),rolled.count(2),rolled.count(3),rolled.count(4),rolled.count(5),rolled.count(6)]\nprint('After being rolled {} times:\\n\\n1 is rolled {} times\\n2 is rolled {} times\\n3 is rolled {} times\\n4 is rolled {} times\\n5 is rolled {} times\\n6 is rolled {} times\\n'.format(rolledtimes,count[0],count[1],count[2],count[3],count[4],count[5]))\n\nprobability()\nfindBiggest()\nfindSmallest()\ntheoretical()\n","sub_path":"rolling_dice.py","file_name":"rolling_dice.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"80034657","text":"import requests\nimport json\nimport re\nimport MySQLdb\n#print i_list\nfileout=open('./thread_db.txt','w+')\nconn=MySQLdb.connect('localhost',user='root')\ncur=conn.cursor()\nout=cur.execute('use gatherer_dashboard')\nout=cur.execute(\"select distinct(gatherer_ip) from gatherer_vip_mapping where gatherer_thread_count='NA'\")\nfields=cur.fetchall()\nfor element in fields:\n\tfileout.write(element[0]+\"\\n\")\nconn.close()\nfileout.close()\n\t\n\t\n","sub_path":"gatherer_tool/gatherer_validation/check_gatherer_thread_count_db.py","file_name":"check_gatherer_thread_count_db.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487695117","text":"import cv2\nimport matplotlib.pyplot as plt\nfig, ax = plt.subplots(nrows=10,ncols=10,sharex='all',sharey='all')\nax = ax.flatten()\nfor i in range(11):\n name='getImages_format/'+str(i+1)+'.jpg'\n img=cv2.imread(name,cv2.IMREAD_GRAYSCALE)\n ax[i].imshow(img,cmap='gray')\nax[0].set_xticks([])\nax[0].set_yticks([])\nplt.tight_layout()\nplt.show()","sub_path":"大论文代码/第四章/tensorflow学习代码/colab/图片显示.py","file_name":"图片显示.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"199319875","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\nfrom onlylady.items import OnlyLadyItem\n\n\nclass OlSpider(scrapy.Spider):\n name = 'ol' # 爬虫名称\n allowed_domains = ['hzp.onlylady.com'] # 允许这个爬虫爬取的域名\n start_urls = ['http://hzp.onlylady.com/brand.html'] # 起始的页面\n headers = {\n \"HOST\": \"hzp.onlylady.com\",\n \"Referer\": \"http://hzp.onlylady.com/cosmetics.html\",\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36\"\n }\n\n # 设置headers，下面的每一个如果要接着爬取的时候，写进入\n\n def parse(self, response):\n # 获取所有品牌的链接\n brand_urls = response.css('#sortByLetter .brandsWraper a::attr(href)').extract()\n for brand_url in set(brand_urls):\n yield scrapy.Request(brand_url, headers=self.headers, callback=self.more)\n\n def more(self, response):\n # 进入某个品牌链接之后，获取进入所有商品的链接\n more_url = response.css('.more::attr(href)').extract_first('')\n yield scrapy.Request(more_url, headers=self.headers, callback=self.goods)\n\n def goods(self, response):\n # 进入所有商品的链接之后，获取商品的详情链接，以及图片链接\n goods_nodes = response.css('.commentItem .left .imgWraper a')\n for goods_node in goods_nodes:\n goods_url = goods_node.css('::attr(href)').extract_first('') # 获取商品详情页链接\n image_url = goods_node.css('img::attr(src)').extract_first('') # 获取商品展示图片的连接\n yield scrapy.Request(goods_url, headers=self.headers, meta={\"image_url\": image_url}, callback=self.detail)\n # meta表示把图片的url暂时存起来，下面的一些函数可以来meta来接收这个参数\n\n # 获取下一页的信息，处理分页的逻辑\n next_url = response.css('.comment_bar .page .next::attr(href)').extract_first('')\n if next_url:\n yield scrapy.Request(next_url, headers=self.headers, callback=self.goods)\n\n def detail(self, response):\n # 到达详情页之后，获取详情页中的一些参数，并提交到我们编写的OnlyLadyItem()中，yield提交items\n zh_name = response.css('.detail_pro .detail_l .p_r .name h2::text').extract_first('')\n type = response.css('.detail_pro .detail_l .p_r dl')[0].css('dd a::attr(title)')[0].extract()\n brand = \\\n response.css('.detail_pro .detail_l .p_r dl')[0].css('dd')[1].css('a::attr(title)').extract_first('').split(\n ' ')[0]\n try:\n price = response.css('.price::text').extract_first('').split('￥')[-1]\n except:\n price = \"\"\n image_url = response.meta.get('image_url', 'image_url') # 通过response.meta.get来接收上个函数存储的meta中的image_url\n items = OnlyLadyItem()\n items['zh_name'] = zh_name\n items['type'] = type\n items['brand'] = brand\n items['price'] = price\n items['image_url'] = image_url\n yield items\n","sub_path":"cosmetics_websites/onlylady/spiders/ol.py","file_name":"ol.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"271677744","text":"#we now iterate through all the items and get the price\n\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\n\nr=requests.get(\"http://pythonhow.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS\", headers={'User-agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0'})\nc=r.content\nsoup=BeautifulSoup(c,\"html.parser\")\n\nall=soup.find_all(\"div\",{\"class\":\"propertyRow\"})\n\n#At this point we are starting to work through the items in the soup list and pulling data out in a way which we can use and iterpret. The example below does so with a for loop and applies a bunch of replace methods to remove characters from strings which arent needed.\n\nfor item in all:\n print(str(item.find(\"h4\",{\"class\":\"propPrice\"})).replace('\\n','').replace('

    ','').replace(' ','').replace('

    ',''))\n name = (str(item.find_all(\"span\",{\"class\",\"propAddressCollapse\"})).replace('[, ','').replace(']',''))\n name = (re.sub(r'.*>', '>', name))\n print(name.replace('>',''))\n try:\n print(str(item.find('span',{'class','infoBed'})).replace('','').replace(' Beds','').replace('',''))\n except:\n print('None')\n try:\n print(str(item.find('span',{'class','infoSqFt'})).replace('','').replace(' Sq. Ft','').replace('',''))\n except:\n print('None')\n try:\n print(str(item.find('span',{'class','infoValueFullBath'})).replace('','').replace(' Full Baths','').replace('',''))\n except:\n print('None')\n try:\n print(str(item.find('span',{'class','infoValueHalfBath'})).replace('','').replace(' Half Bath','').replace('',''))\n except:\n print('None')\n print('')\n","sub_path":"app7_webscraping/3_loop_through_all_divs.py","file_name":"3_loop_through_all_divs.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"618184821","text":"# FINAL GUI\r\n\r\nfrom tkinter import *\r\nfrom tkinter import messagebox\r\n\r\nroot = Tk()\r\nroot.geometry(\"800x800\")\r\nroot.maxsize(400, 400)\r\nroot.minsize(400, 400)\r\nroot.title(\"Currency Converter\")\r\nroot.config(bg=\"#99FFFF\")\r\n\r\n\r\ndef clicked():\r\n if(clicked1.get() == options[0] or clicked2.get() == options[0]):\r\n messagebox.showinfo(\"Info\", \"Please select a valid currency\")\r\n else:\r\n pass\r\n\r\n\r\n# Label\r\n\r\n\r\nLabel(root, text=\"Welcome to Currency Converter\",\r\n border=2, relief=SOLID, bg=\"indigo\", fg=\"yellow\", font=\"Arial 19 bold\").pack()\r\n\r\n# Frames\r\n\r\nframe1 = LabelFrame(root, text=\"Select FROM CURRENCY\",\r\n padx=5, pady=5, background=\"#FF9933\", foreground=\"navy blue\")\r\nframe1.place(x=10, y=100)\r\n\r\nframe2 = LabelFrame(root, text=\"Select TO CURRENCY\",\r\n padx=5, pady=5, background=\"#138808\", foreground=\"yellow\")\r\nframe2.place(x=265, y=100)\r\n\r\nframe3 = LabelFrame(root, text=\"Enter your Amount here\",\r\n padx=5, pady=5, background=\"#CCCCCC\", foreground=\"navy blue\")\r\nframe3.place(x=10, y=180)\r\n\r\nframe4 = LabelFrame(root, text=\"Converted Amount\",\r\n padx=5, pady=5, background=\"#CCCCCC\", foreground=\"navy blue\")\r\nframe4.place(x=263, y=180)\r\n\r\n# Manual Entry\r\n\r\na = StringVar(root)\r\namt = Entry(frame3, justify=CENTER, borderwidth=2).pack()\r\n\r\nres = Text(frame4, borderwidth=2, height=0.9,\r\n width=13, state=DISABLED).pack()\r\n\r\noptions = [\"...\",\r\n \"USD\",\r\n \"AUD\",\r\n \"CAD\",\r\n \"INR\"\r\n ]\r\n\r\nclicked1 = StringVar()\r\nclicked2 = StringVar()\r\n\r\nclicked1.set(options[0])\r\nclicked2.set(options[0])\r\n\r\n# Drop Menu\r\ndrop1 = OptionMenu(frame1, clicked1, *options).pack()\r\ndrop2 = OptionMenu(frame2, clicked2, *options).pack()\r\n\r\n# Conversion Button\r\nbtn1 = Button(root, text=\"Get Value\", fg=\"#000080\",\r\n bg=\"white\", font=(\"Italic\", 10, \"bold\"), command=clicked).place(x=172, y=159)\r\n\r\n# Exit Button\r\nbtn2 = Button(root, text=\"Exit\", font=(\"Italic\", 10, \"bold\"), command=root.quit,\r\n width=8).place(x=176, y=300)\r\n\r\nroot.mainloop()\r\n","sub_path":"final_GUI.py","file_name":"final_GUI.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"34235208","text":"import numpy\nimport torch\nimport skimage.io\nimport skimage.transform\nimport skimage.color\nimport os\nimport torchvision.datasets\nimport torchvision.transforms as transforms\n\nclass DataSetIndex():\n def __init__(self, path):\n mnist_ds = torchvision.datasets.MNIST(path, download=True)\n\n data = mnist_ds.train_data\n labels = mnist_ds.train_labels\n\n test_split = 0.1\n valid_split = 0.1\n\n n_test = int(data.size(0) * test_split)\n n_valid = int(data.size(0) * valid_split)\n\n n_train = data.size(0) - n_test - n_valid\n\n order = numpy.random.permutation(data.size(0))\n\n data = data[order, :, :]\n labels = labels[order]\n\n #print('OK')\n self.test_data = data[:n_test, :, :]\n self.test_labels = labels[:n_test]\n\n self.valid_data = data[n_test:n_test + n_valid, :, :]\n self.valid_labels = labels[n_test:n_test + n_valid]\n\n self.train_data = data[n_test + n_valid:, :, :]\n self.train_labels = labels[n_test + n_valid:]\n\n def shuffle(self):\n new_order = numpy.arange(len(self.train_data))\n numpy.random.shuffle(new_order)\n\n self.train_data = self.train_data[new_order]\n self.train_labels = self.train_labels[new_order]\n\n\nclass DataSet():\n def __init__(self, ds_index, mode='train'):\n self.ds_index = ds_index\n self.mode = mode\n\n self.train_transform = transforms.Compose([])\n self.valid_transform = transforms.Compose([])\n self.test_transform = transforms.Compose([])\n\n def __len__(self):\n if self.mode == 'test':\n return self.ds_index.test_data.size(0)\n\n elif self.mode == 'valid':\n return self.ds_index.valid_data.size(0)\n\n else:\n return self.ds_index.train_data.size(0)\n\n\n def __getitem__(self, index):\n img = None\n target = None\n\n if self.mode == 'test':\n img = self.test_transform(self.ds_index.test_data[index, :, :])\n target = self.ds_index.test_labels[index]\n\n elif self.mode == 'valid':\n img = self.valid_transform(self.ds_index.valid_data[index, :, :])\n target = self.ds_index.valid_labels[index]\n\n else:\n img = self.train_transform(self.ds_index.train_data[index, :, :])\n target = self.ds_index.train_labels[index]\n\n img = img.unsqueeze(0)\n\n img = img.float()\n\n return [img], [target]\n","sub_path":"examples/dataset_mnist.py","file_name":"dataset_mnist.py","file_ext":"py","file_size_in_byte":2467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"340363198","text":"import socket\nimport ssl\nimport logging\nimport time\nimport datetime\nfrom .parsers import http\nfrom .parsers import html\nfrom .exceptions import SHANotFound\nimport os\nfrom json import dump\n\n\nclass VTClient:\n def __init__(self, domain, task_manager, event, info):\n \"\"\"\n Konstruktor klasy VTClient\n :param domain: adres strony VirusTotal\n :param task_manager: obiekt klasy TasksManager do pobierania kolejnych zadan\n :param event: Obiekt synchronizacyjny\n :param info: slownik z konfiguracja programu, m.in port, sciezka do zapisu plikow json\n \"\"\"\n self.domain = domain\n self.task_manager = task_manager\n self.event = event\n self.info = info\n self.isConnected = False\n self.ssl_sock = None\n\n def start(self):\n \"\"\"\n Pelta glowna procesu klienta VT. Pobiera zadanie z kolejki lub zawiesza sie.\n Inicjuje sciaganie kodu html strony VT, parsowanie i zapis do pliku\n \"\"\"\n logging.info(\"Starting VT Client.\")\n try:\n while True:\n\n task = self.task_manager.get_next_task()\n time.sleep(1)\n if task is None:\n logging.debug(\"VTClient: No tasks. Going to sleep\")\n self.event.wait()\n self.event.clear()\n continue\n\n elif task[1] > time.time():\n logging.debug(\"VTClient: Found task for sha:\" + task[0] + \", but it should be analysed later\")\n if self.event.wait(task[1] - time.time()):\n self.event.clear()\n continue\n\n sha = task[0]\n logging.debug(\"VTClient: Found task for sha:\" + sha + \", sending request\")\n byte_sha = str.encode(sha)\n\n try:\n byte_result = self._send_request(byte_sha)\n if byte_result is None:\n time.sleep(10)\n continue\n result = byte_result.decode('UTF-8')\n\n logging.info(\"VTClient: Html for sha: \" + sha + \" successfully downloaded.\")\n json = html.parse(result, sha)\n except SHANotFound:\n json = {\"info\": \"error\", \"error\": \"SHA256 not found.\"}\n except Exception as e:\n json = {\"info\": \"error\", \"error\": str(e)}\n\n path = self.info[\"ResultsFolder\"]+sha+\"/\"\n\n if not os.path.isdir(path):\n continue\n\n ts = time.time()\n filename = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')+'.json'\n with open(os.path.join(path, filename), 'w') as temp_file:\n dump(json, temp_file, indent=\"\\t\")\n\n self.task_manager.update_task(sha, filename)\n time.sleep(self.info[\"ServerReqFreq\"])\n\n except (KeyboardInterrupt, EOFError):\n self._shutdown()\n\n def _send_request(self, sha):\n \"\"\"\n Metoda laczaca sie z serwisem VT i pobierajaca HTML\n :param sha: skrot sha256 (bytestring)\n :return: bytestring z HTML\n \"\"\"\n path = b'/en/file/' + sha + b'/analysis/'\n\n sock = socket.socket()\n context = ssl.create_default_context()\n self.ssl_sock = context.wrap_socket(sock, server_hostname=self.domain)\n\n try:\n self.ssl_sock.connect((self.domain, self.info[\"VTPORT\"]))\n\n except OSError as e:\n logging.error(\"Could not connect to VirusTotal \"+str(e))\n return None\n\n except ValueError as e:\n logging.error(\"Could not connect to VirusTotal \" + str(e))\n return None\n\n query = http.HTTPRequest.vt_request_header(path, self.domain) + b\"\\r\\n\"\n\n try:\n self.ssl_sock.sendall(query)\n\n except (OSError, socket.error):\n logging.error(\"Could not send request to VirusTotal\")\n return None\n\n self.ssl_sock.settimeout(300)\n result = b\"\"\n receive = b\"\"\n\n while receive.find(b\"\\r\\n\\r\\n\") < 0:\n try:\n receive = self.ssl_sock.recv(1024)\n result += receive\n\n except OSError as e:\n logging.error('Cannot receive VT HTTP header ' + str(e))\n return None\n\n results = result.split(b\"\\r\\n\\r\\n\")\n header = results[0]\n\n if len(results) == 2:\n content = results[1]\n else:\n content = b\"\"\n\n info = http.HTTPResponse.parse(header)\n\n if info[\"Code\"] == \"302\":\n raise SHANotFound(\"SHA does not exist in VirusTotal\")\n\n if info[\"Code\"] != \"200\":\n logging.error(\"Could not connect to VirusTotal. Code: \" + info[\"Code\"])\n return None\n\n\n html_code = b\"\"\n if \"Transfer-Encoding\" in info and info[\"Transfer-Encoding\"] == \"chunked\": # chunkowanie html\n while True:\n while content.find(b\"\\r\\n\") < 0: # znajdz \\r\\n za rozmiarem chunka\n try:\n receive = self.ssl_sock.recv(1024)\n content += receive\n except OSError as e:\n logging.error('Cannot receive VT HTML' + str(e))\n return None\n\n idx = content.find(b\"\\r\\n\")\n chunk = content[0:idx]\n\n size = int(chunk, 16) # ustal rozmiar chunka\n\n content = content[idx+2:]\n\n dif = size - len(content) + 3\n\n while dif > 0:\n try:\n if dif > 1024:\n receive = self.ssl_sock.recv(1024)\n content += receive\n dif -= len(receive)\n else:\n receive = self.ssl_sock.recv(dif)\n content += receive\n dif -= len(receive)\n except OSError as e:\n logging.error('Cannot receive VT HTML' + str(e))\n return None\n\n contents = content.split(b\"\\r\\n\")\n\n html_code += contents[0]\n part_size = int(contents[1], 16)\n\n if part_size == 0:\n break\n\n content = contents[1]\n try:\n\n length = 0\n\n while length < 4:\n rec = self.ssl_sock.recv(4) # ostanie 4 bajty \\r\\n\n length += len(rec)\n\n except OSError as e:\n logging.error('VT HTML did not ended properly: ' + str(e))\n\n else: # pobieranie html bez chunk\n while content.find(b\"\\r\\n\") < 0: # znajdz \\r\\n za rozmiarem chunka\n try:\n receive = self.ssl_sock.recv(1024)\n content += receive\n except OSError as e:\n logging.error('Cannot receive VT HTML' + str(e))\n return None\n\n html_code = content\n\n self.ssl_sock.shutdown(socket.SHUT_RDWR)\n return html_code\n\n def _shutdown(self):\n \"\"\"\n Metoda porzadkowa wolania za zakonczenie procesu.\n \"\"\"\n try:\n if self.ssl_sock is not None:\n self.ssl_sock.shutdown(socket.SHUT_RDWR)\n\n except OSError:\n pass\n\n finally:\n logging.info('Shutting down VT Client')\n","sub_path":"src/scrapper/vtclient.py","file_name":"vtclient.py","file_ext":"py","file_size_in_byte":7584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"404664968","text":"from datetime import datetime,timedelta\n\nfrom apps.mainsite.models import Links\n\n\ndef get_links(paper=''):\n\n now = datetime.now()\n #one week earlier\n print(now)\n mytime = now + timedelta(days=-7)\n mytime = mytime.strftime('%Y-%m-%d %H:%M:%S')\n # print(mytime)\n\n if paper:\n links = Links.objects(engsource=paper, insertdatetime__gte=mytime).only('title', 'url', 'source', 'mindesc','imagepath','insertdate').\\\n order_by('-insertdate', 'itemid', '-insertdatetime')\n else:\n links = Links.objects(insertdatetime__gte=mytime).only('title', 'url', 'source', 'mindesc', 'imagepath','insertdate').\\\n order_by('-insertdate', 'itemid', '-insertdatetime')\n return links\n","sub_path":"apps/mainsite/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"68065065","text":"\"\"\"Plant URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom .views import *\nfrom django.conf.urls.static import static\nfrom django.conf import settings\nfrom django.conf.urls import url\n\nurlpatterns = [\n path('register_user/', RegisterUser.as_view(), name='register_user'),\n path('login/', Login.as_view(), name='login'),\n path('create-shop/', CreateShop.as_view(), name='create-shop'),\n path('create-category/', CreateCategory.as_view(), name='create-category'),\n path('create-item/', CreateItem.as_view(), name='create-item'),\n path('get-shop-items/', GetShopItems.as_view(), name='get-shop-items'),\n path('get-orders-items/', GetOrdersItems.as_view(), name='get-orders-items'),\n path('orders/', Orders.as_view(), name='orders'),\n path('slider/', SliderImages.as_view(), name='slider'),\n path('upload-image/', UploadImage.as_view(), name='upload-image'),\n path('get-user-info/', GetUserInfo.as_view(), name='get-user-info'),\n]\n\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n\n\n\n\n","sub_path":"plant_life/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"269419173","text":"#-*- coding: utf-8 -*-\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport urllib.request\nimport time\nimport os\n\nurl = 'http://weheartit.com/inspirations/taylorswift?page='\n\n\nheader = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n\n# 这里用来获取页面数据\ndef get_page(url):\n wb_data = requests.get(url, headers=header)\n soup = BeautifulSoup(wb_data.text, 'lxml')\n imgs = soup.select('#main-container > div > div > div > div > div > a > img')\n\n download_links = []\n for img in imgs:\n links = img.get('src')\n download_links.append(links)\n download_imgs(download_links)\n\n# 这里是下载模块\ndef download_imgs(url):\n folder_path = 'C:/Users/nulli/Desktop/a3'\n for item in url:\n for i in range(1, ):\n file_name = str(time.time()) + item[-5:]\n dest_dir = os.path.join(folder_path, file_name)\n urllib.request.urlretrieve(item, dest_dir)\n print('下载完成！')\n\n# 这里是对页面加载进行处理\ndef get_more_page(start, end):\n for n in range(start, end):\n get_page(url+str(n))\n time.sleep(2)\n\n# 以下可以用来去除列表中的重复值---不过没用了\n# def remove_duplicates(seq):\n# checked = []\n# for e in seq:\n# if e not in checked:\n# checked.append(e)\n# print(checked)\n\n# 这里设置开始与结束的页面数值\nget_more_page(1, 2)\n\n\n# proxies = {\n# 'http': 'http://127.0.0.1:8087',\n# 'https': 'http://127.0.0.1:8087',\n# }\n#\n# login_data = {\n# 'email': 'youxiassssssssssssssssssssssss@163.com',\n# 'pass': 'mima',\n# }\n#\n# r = s.get('https://www.facebook.com/login.php?login_attempt=1', proxies=proxies, verify=False)","sub_path":"practice/week1/1-4/meimei.py","file_name":"meimei.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"368754277","text":"import collections\nimport json\nimport socket\nimport sys\nimport threading\nimport traceback\n\nMAX_NUMBER_OF_USERS = 100\n\n\ndef send(socket, str):\n return socket.sendall(\"{}\\n\".format(str).encode())\n\n\nclass User:\n def __init__(self, chat, addr, socket):\n self.chat = chat\n self.addr = addr\n self.socket = socket\n self.name = \"{}:{}\".format(*addr)\n\n def execute(self):\n try:\n for message in iter_messages(self.socket):\n if message[\"type\"] == \"message\":\n self.chat.post_message(self, message[\"data\"])\n elif message[\"type\"] == \"history\":\n self.on_message(self.chat, repr(tuple(self.chat.messages)))\n elif message[\"type\"] == \"users\":\n self.on_message(self.chat, repr(tuple(user.name for user in self.chat.users.values())))\n elif message[\"type\"] == \"set-name\":\n self.chat.user_rename(self.name, message[\"data\"])\n self.name = message[\"data\"]\n else:\n print(\"Unknown message type {} from {}\".format(message[\"type\"], self.name))\n\n except Exception as e:\n print(\"Client {} error: {}\".format(self.addr, e))\n finally:\n self.chat.user_disconnect(self)\n self.socket.close()\n\n def on_message(self, source, msg):\n send(self.socket, json.dumps({\n \"type\": \"message\",\n \"data\": {\n \"source\": source.name,\n \"message\": msg\n }\n }))\n\n\nclass Chat:\n def __init__(self):\n self.users = {}\n\n # use a fixed size queue to avoid running out of memory\n self.messages = collections.deque(maxlen=1000)\n\n @property\n def name(self):\n return \"[Server]\"\n\n def has_free_slots(self):\n return len(self.users) < MAX_NUMBER_OF_USERS\n\n def user_connect(self, user):\n self.users[user.addr] = user\n print(\"Client connected: {}\".format(user.name))\n self._broadcast(self, \"{} has entered the chat\".format(user.name))\n\n def user_disconnect(self, user):\n del self.users[user.addr]\n print(\"Client disconnected: {}\".format(user.name))\n self._broadcast(self, \"{} has left the chat\".format(user.name))\n\n def user_rename(self, oldname, name):\n msg = \"{} renamed to {}\".format(oldname, name)\n print(msg)\n self._broadcast(self, msg)\n\n def post_message(self, user, msg):\n assert user.addr in self.users\n self.messages.append((user.name, msg))\n print(\"{}: {}\".format(user.name, msg))\n self._broadcast(user, msg)\n\n def _broadcast(self, source, msg):\n try:\n users = list(self.users.values())\n for receiver in users:\n receiver.on_message(source, msg)\n except:\n traceback.print_exc()\n\n\ndef init_server(port):\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sock.bind(('0.0.0.0', port))\n sock.listen(10)\n return sock\n\n\ndef iter_messages(socket):\n buffer = \"\"\n while True:\n data = socket.recv(1)\n if not data:\n break\n buffer += data.decode()\n while '\\n' in buffer:\n index = buffer.index('\\n')\n line = buffer[:index]\n yield json.loads(line)\n buffer = buffer[index + 1:]\n\n\ndef chat_server(chat, port):\n sock = init_server(port)\n\n try:\n while True:\n client, addr = sock.accept()\n if chat.has_free_slots():\n user = User(chat, addr, client)\n chat.user_connect(user)\n thread = threading.Thread(target=user.execute)\n thread.daemon = True\n thread.start()\n else:\n client.close()\n except KeyboardInterrupt:\n print(\"Server exiting\")\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) < 2:\n print(\"Usage: python server.py \")\n exit(1)\n\n PORT = int(sys.argv[1])\n\n print(\"Server listening on 127.0.0.1:{}\".format(PORT))\n\n chat = Chat()\n chat_server(chat, PORT)\n","sub_path":"labs/08/server_solution.py","file_name":"server_solution.py","file_ext":"py","file_size_in_byte":4208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"182184723","text":"\n#from 13_15.rule\nimport sys\nsys.path.append('/etc/openhab2/scripts/control')\nimport time\nimport database\nimport util\n\nstart_time,end_time=database.reader(\"no6\").split(\"-\")\n\nwhile True:\n time_now = time.strftime(\"%H:%M\", time.localtime())\n \n if time_now == start_time:\n \n util.trun_off()\n\n colors = \"180,0,100\"\n util.api_call(\"lightBulbsColor\",colors)\n util.api_call(\"lightStripsColor\",colors)\n util.api_call(\"lightStripsColor\",\"ON\")\n\n data=\"https://api-soft3888.ddns.net/black.png\"\n util.api_call(\"chromecastPlay\",data)\n\n time.sleep(61) \n","sub_path":"scripts/not_in_use/no6.py","file_name":"no6.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"274025564","text":"#Lors d'un mouvement, l'ensemble des tuiles du plateau sont déplacés dans la même direction jusqu'à rencontrer les bords du plateau ou une autre tuile sur leur chemin.\r\n#Si deux tuiles, ayant le même nombre, entrent en collision durant le mouvement,\r\n#elles fusionnent en une nouvelle tuile de valeur double (par ex. : deux tuiles de valeur « 2 » donnent une tuile de valeur « 4 »).\r\n#À chaque mouvement, une tuile portant un 2 ou un 4 apparaît dans une case vide de manière aléatoire.\r\n\r\nfrom tkinter import *\r\nfrom random import *\r\nfrom time import *\r\n\r\nclass Carré(Canvas):\r\n def __init__(self, master, posx = 0, posy = 0, **options):\r\n Canvas.__init__(self, master, bg = \"white\",width = 100, height = 100, **options)\r\n self.nb = 0\r\n self.posx, self.posy = posx, posy\r\n self.text = self.create_text(float(self[\"width\"])/2, float(self[\"height\"])/2, text = \" \")\r\n self.master = master\r\n\r\n def change(self, nb):\r\n self.nb = nb\r\n\r\n if self.nb == 2:\r\n self[\"bg\"] = '#e4e6cc'\r\n elif self.nb == 4:\r\n self[\"bg\"] = '#b8b692'\r\n elif self.nb == 8:\r\n self[\"bg\"] = '#cb9d6b'\r\n elif self.nb == 16:\r\n self[\"bg\"] = '#d97e68'\r\n elif self.nb == 32:\r\n self[\"bg\"] = '#fe5c2e'\r\n elif self.nb == 64:\r\n self[\"bg\"] = '#ff1a1a'\r\n elif self.nb == 128:\r\n self[\"bg\"] = '#e7d674'\r\n elif self.nb == 256:\r\n self[\"bg\"] = '#d0d26f'\r\n elif self.nb == 512:\r\n self[\"bg\"] = '#ffff00'\r\n elif self.nb == 1024:\r\n self[\"bg\"] = '#8888ff'\r\n elif self.nb == 2048:\r\n self[\"bg\"] = '#0000ff'\r\n else:\r\n self[\"bg\"] = 'white'\r\n\r\n if self.nb != 0:\r\n self.itemconfig(self.text, text = str(self.nb))\r\n else:\r\n self.itemconfig(self.text, text = \" \")\r\n\r\n self.update()\r\n\r\nclass fen2048(Frame):\r\n def __init__(self, master, time = 0.05, **options):\r\n Frame.__init__(self, master,width = 400, height = 400, **options)\r\n self.master = master\r\n self.time = time\r\n\r\n self.focus_set()\r\n\r\n self.bind(\"\", self.clavierup)\r\n self.bind(\"\", self.clavierdown)\r\n self.bind(\"\", self.clavierleft)\r\n self.bind(\"\", self.clavierright)\r\n\r\n self.list = []\r\n for x in range(0,4):\r\n self.list.append([])\r\n for y in range(0,4):\r\n carré = Carré(self, posx = x, posy = y)\r\n carré.grid(column = x, row = y)\r\n self.list[x].append(carré)\r\n self.clavier()\r\n\r\n def clavier(self):\r\n end = True\r\n for essai in range(1000):\r\n carré = self.list[randint(0,3)][randint(0,3)]\r\n if carré.nb == 0:\r\n carré.change(randint(1,2)*2)\r\n end = False\r\n break\r\n if end == True:\r\n print(\"GAME OVER...\")\r\n sleep(5)\r\n quit()\r\n\r\n def clavierup(self,event):\r\n for t in range(3):\r\n for x in range(0,4):\r\n for y in range(0,4):\r\n carré = self.list[x][y]\r\n if carré.nb != 0 and y != 0 and self.list[x][y-1].nb == 0:\r\n self.list[x][y-1].change(carré.nb)\r\n carré.change(0)\r\n try:\r\n if self.list[x][y-1].nb == carré.nb:\r\n self.list[x][y-1].change(carré.nb*2)\r\n carré.change(0)\r\n except IndexError:\r\n pass\r\n sleep(self.time)\r\n\r\n self.clavier()\r\n\r\n def clavierdown(self,event):\r\n for t in range(3):\r\n for x in range(0,4):\r\n for y in range(0,4):\r\n carré = self.list[x][y]\r\n if carré.nb != 0 and y != 3 and self.list[x][y+1].nb == 0:\r\n self.list[x][y+1].change(carré.nb)\r\n carré.change(0)\r\n try:\r\n if self.list[x][y+1].nb == carré.nb:\r\n self.list[x][y+1].change(carré.nb*2)\r\n carré.change(0)\r\n except IndexError:\r\n pass\r\n sleep(self.time)\r\n self.clavier()\r\n\r\n def clavierleft(self,event):\r\n for t in range(3):\r\n for x in range(0,4):\r\n for y in range(0,4):\r\n carré = self.list[x][y]\r\n if carré.nb != 0 and x != 0 and self.list[x-1][y].nb == 0:\r\n self.list[x-1][y].change(carré.nb)\r\n carré.change(0)\r\n try:\r\n if self.list[x-1][y].nb == carré.nb:\r\n self.list[x-1][y].change(carré.nb*2)\r\n carré.change(0)\r\n except IndexError:\r\n pass\r\n sleep(self.time)\r\n self.clavier()\r\n\r\n def clavierright(self,event):\r\n for t in range(3):\r\n for x in range(0,4):\r\n for y in range(0,4):\r\n carré = self.list[x][y]\r\n if carré.nb != 0 and x != 3 and self.list[x+1][y].nb == 0:\r\n self.list[x+1][y].change(carré.nb)\r\n carré.change(0)\r\n try:\r\n if self.list[x+1][y].nb == carré.nb:\r\n self.list[x+1][y].change(carré.nb*2)\r\n carré.change(0)\r\n except IndexError:\r\n pass\r\n sleep(self.time)\r\n self.clavier()\r\n\r\nif __name__ == \"__main__\":\r\n fen = Tk()\r\n fen.title(\"un petit 2048 \")\r\n mainfen = fen2048(fen)\r\n mainfen.pack(expand = 1)\r\n fen.mainloop()\r\n\r\n","sub_path":"2048.py","file_name":"2048.py","file_ext":"py","file_size_in_byte":6180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"277410114","text":"#class Solution(object):\r\n# def permute(self, nums):\r\n# \"\"\"\r\n# :type nums: List[int]\r\n# :rtype: List[List[int]]\r\n# \"\"\"\r\n# # time O(n!)\r\n# res=[]\r\n# visited=[False]*len(nums)\r\n# self.dfs(nums,visited,res,[])\r\n# return res\r\n# \r\n# def dfs(self,nums,used,result,curr):\r\n# if len(curr)==len(nums):\r\n# result.append(curr+[]) # or .append(list(curr))\r\n# return \r\n# for i in range(len(nums)):\r\n# if not used[i]:\r\n# used[i]=True\r\n# curr.append(nums[i])\r\n# self.dfs(nums,used,result,curr)\r\n# curr.pop()\r\n# used[i]=False\r\n \r\n#class Solution(object):\r\n# def permute(self, nums):\r\n# \"\"\"\r\n# :type nums: List[int]\r\n# :rtype: List[List[int]]\r\n# \"\"\"\r\n# def backtracking(nums,visited,cnt,perm,res):\r\n# if cnt==self.n:\r\n# res.append(perm)\r\n# return\r\n# for i in range(self.n):\r\n# if not visited[i]:\r\n# visited[i]=True\r\n# backtracking(nums,visited,cnt+1,perm+[nums[i]],res)\r\n# visited[i]=False\r\n# \r\n# if not nums:\r\n# return [[]]\r\n# res=[]\r\n# self.n=len(nums)\r\n# visited=[False]*self.n\r\n# backtracking(nums,visited,0,[],res)\r\n# return res\r\n \r\n# http://www.cnblogs.com/grandyang/p/4358848.html \r\nclass Solution(object):\r\n def permute(self, nums):\r\n \"\"\"\r\n :type nums: List[int]\r\n :rtype: List[List[int]]\r\n \"\"\"\r\n if not nums:\r\n return [[]]\r\n \r\n res=[]\r\n last=nums[-1]\r\n nums.pop()\r\n perms=self.permute(nums)\r\n for perm in perms:\r\n for i in range(len(perm)+1):\r\n perm.insert(i,last)\r\n res.append(perm[:])\r\n perm.pop(i)\r\n return res\r\n \r\nif __name__ == \"__main__\":\r\n print(Solution().permute([1,3,2]))","sub_path":"46. Permutations.py","file_name":"46. Permutations.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"504749378","text":"\"\"\"\nPlot the B decay asymmetry in B->J/PsiKs.\n\"\"\"\nimport numpy as np\nimport matplotlib.pylab as plt\ntau = 1.519 # psec\nsin2phi1 = 0.66\ndelta_m = 0.51\neta = -1.0\nS = -1.0*eta*sin2phi1\n\nt = np.linspace(-10, 10, 1000)\nf = 1.0/(2.0*tau)*np.exp(-abs(t)/tau)\nq = -1.0 # Btag = Bbar\nf_B2fcp = 1.0/(2.0*tau)*np.exp(-abs(t)/tau)*(1.0+q*S*np.sin(delta_m*t))\nq = 1.0 # Btag = B\nf_Bbar2fcp = 1.0/(2.0*tau)*np.exp(-abs(t)/tau)*(1.0+q*S*np.sin(delta_m*t))\nasym = (f_Bbar2fcp-f_B2fcp)/(f_B2fcp+f_Bbar2fcp)\nasym0 = np.zeros(1000)\n\nplt.subplot(2, 1, 1)\nplt.plot(t, f, 'k--')\nplt.plot(t, f_Bbar2fcp, 'r-')\nplt.plot(t, f_B2fcp, 'b-')\nax2 = plt.subplot(2, 1, 2)\nplt.plot(t, asym, 'k-')\nax2.set_title(\"Decay Time Difference $\\Delta t$ (psec)\")\nplt.plot(t, asym0, 'k--')\nplt.savefig('tcpv.png')\n","sub_path":"B2JpsiKs_decay_asym.py","file_name":"B2JpsiKs_decay_asym.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"300728696","text":"import gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nimport json\n# from model import Website\n\nscope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']\ncreds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)\nclient = gspread.authorize(creds)\n\nsheet = client.open(\"list of websites\").sheet1\n\nwebsites = sheet.get_all_records()\n\ncell = sheet.find(\"http://freewestmedia.com\")\nWebsite_line = (cell.row)\nWebsite_Info = sheet.row_values(Website_line)\n# print(Website_Info[0])\nWebsite = json.dumps(websites)\n\n\n# print(Website)\nwith open('websites.txt', 'w') as outfile:\n json.dump(websites, outfile)","sub_path":"chrome_extension/spreadsheet.py","file_name":"spreadsheet.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"496862693","text":"class Vowels:\r\n def __init__(self,napis):\r\n self.napis = napis\r\n self.limit = len(napis)\r\n\r\n def __iter__(self):\r\n self.a = 0\r\n return self\r\n\r\n def __next__(self):\r\n while self.a < self.limit:\r\n x = self.napis[self.a]\r\n self.a += 1\r\n if x.lower() in ('a','e','i','o','u','y'):\r\n return x\r\n raise StopIteration\r\n\r\nfor x in Vowels('Ala ma kota'):\r\n print(x)\r\n","sub_path":"tp_zad_5_2.py","file_name":"tp_zad_5_2.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"553840238","text":"# -*- coding: utf-8 -*-\n\"\"\"FAB - Edit Company's Directory Profile page\"\"\"\nimport logging\n\nfrom requests import Response, Session\n\nfrom tests import get_absolute_url\nfrom tests.functional.utils.context_utils import Feedback\nfrom tests.functional.utils.request import Method, check_response, make_request\n\nURL = get_absolute_url(\"ui-supplier:feedback\")\nEXPECTED_STRINGS_FORM = [\n \"Get UK companies to fulfil your business needs\",\n (\"Tell us what kind of goods or services you need. We'll put you in touch \"\n \"with relevant UK suppliers.\"),\n \"Your name\", \"Email address\", \"Organisation name\", \"Country\",\n \"Describe what you need\", \"Maximum 1000 characters.\",\n \"I agree to the great.gov.uk terms and conditions\", \"Send\"\n]\nEXPECTED_STRINGS_SUCCESSFUL_SUBMISSION = [\n \"Your request has been submitted\",\n \"Thank you for letting us know about your organisation’s needs.\",\n (\"UK government staff based in your region will be in touch to let you know\"\n \" how UK businesses can help you.\")\n]\nEXPECTED_STRINGS_ERRORS = [\n \"This field is required.\",\n \"Tick the box to confirm you agree to the terms and conditions.\"\n]\n\n\ndef go_to(session: Session) -> Response:\n \"\"\"Go to FAS send Feedback form page.\n\n :param session: Buyer session object\n :return: response object\n \"\"\"\n headers = {\"Referer\": get_absolute_url(\"ui-supplier:feedback\")}\n response = make_request(Method.GET, URL, session=session, headers=headers)\n should_be_here(response)\n return response\n\n\ndef should_be_here(response):\n \"\"\"Check if User is on the correct page.\n\n :param response: response object\n \"\"\"\n check_response(response, 200, body_contains=EXPECTED_STRINGS_FORM)\n logging.debug(\"Buyer is on FAS send Feedback page\")\n\n\ndef should_see_feedback_submission_confirmation(response):\n check_response(\n response, 200, body_contains=EXPECTED_STRINGS_SUCCESSFUL_SUBMISSION)\n logging.debug(\"Feedback submission was confirmed.\")\n\n\ndef should_see_errors(response):\n check_response(response, 200, body_contains=EXPECTED_STRINGS_ERRORS)\n logging.debug(\"Buyer was presented with Feedback submission errors.\")\n\n\ndef submit(\n session: Session, feedback: Feedback, *, referer: str = None) -> Response:\n \"\"\"Submit feedback form.\n\n :param session: Buyer session object\n :param feedback: a namedtuple with Feedback request details\n :param referer: (optional) Originating page. Defaults to \"{FAS}/feedback\"\n :return: response object\n \"\"\"\n if referer:\n headers = {\"Referer\": referer}\n else:\n headers = {\"Referer\": get_absolute_url(\"ui-supplier:feedback\")}\n\n data = {\n \"full_name\": feedback.name,\n \"email_address\": feedback.email,\n \"company_name\": feedback.company_name,\n \"country\": feedback.country,\n \"comment\": feedback.comment,\n \"terms\": feedback.terms,\n \"g-recaptcha-response\": feedback.g_recaptcha_response,\n }\n response = make_request(\n Method.POST, URL, session=session, headers=headers, data=data, trim=False)\n return response\n","sub_path":"tests/functional/pages/fas_ui_feedback.py","file_name":"fas_ui_feedback.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"121427888","text":"# -*- coding: utf-8 -*-\nimport math\nimport numpy as np\nimport numpy.random as rd\n\nclass iGMM:\n def __init__(self):\n pass\n\n def main(self, training, iteration=100):\n\n # set hyper params\n gamma = 1. # concentration parameter\n eta = np.mean(training) # mean parameter\n xi = 1. # precision parameter\n a = 1. # shape parameter\n b = 1. + np.var(training) # inverse scale parameter\n\n N = len(training)\n latent_topic_n = [-1] * N\n asg_data_k = []\n X_k = []\n C_k = []\n num_topic = 0\n\n for _ in xrange(iteration):\n for n, x in enumerate(training):\n z = latent_topic_n[n]\n\n if z > -1:\n asg_data_k[z] -= 1\n\n X_k[z] -= x\n C_k[z] -= math.pow(x, 2)\n\n if asg_data_k[z] == 0:\n num_topic -= 1\n\n del asg_data_k[z]\n del X_k[z]\n del C_k[z]\n\n # トピックを詰める\n for tmp_n, tmp_k in enumerate(latent_topic_n):\n if tmp_k >= z:\n latent_topic_n[tmp_n] -= 1\n\n probs_sampling = np.zeros(num_topic + 1)\n\n # 既存トピック\n for k in xrange(num_topic):\n logprobs_sampling = math.log(asg_data_k[k])\n\n xi_k = xi + asg_data_k[k] + 1\n a_k = a + 0.5 * (asg_data_k[k] + 1)\n eta_k = (xi * eta + X_k[k] + x) / xi_k\n b_k = b + 0.5 * (xi * math.pow(eta, 2) - xi_k * math.pow(eta_k, 2) + C_k[k] + math.pow(x, 2))\n\n logprobs_sampling -= 0.5 * math.log(xi_k)\n logprobs_sampling -= a_k * math.log(b_k)\n logprobs_sampling += math.lgamma(a_k)\n\n xi_k = xi + asg_data_k[k]\n a_k = a + 0.5 * asg_data_k[k]\n eta_k = (xi * eta + X_k[k]) / xi_k\n b_k = b + 0.5 * (xi * math.pow(eta, 2) - xi_k * math.pow(eta_k, 2) + C_k[k])\n\n logprobs_sampling += 0.5 * math.log(xi_k)\n logprobs_sampling += a_k * math.log(b_k)\n logprobs_sampling -= math.lgamma(a_k)\n\n probs_sampling[k] = math.exp(logprobs_sampling)\n\n\n # 新しいトピック\n k = num_topic\n logprobs_sampling = math.log(gamma)\n\n xi_k = xi + 1\n a_k = a + 0.5\n eta_k = (xi * eta + x) / xi_k\n b_k = b + 0.5 * (xi * math.pow(eta, 2) - xi_k * math.pow(eta_k, 2) + math.pow(x, 2))\n\n logprobs_sampling -= 0.5 * math.log(xi_k)\n logprobs_sampling -= a_k * math.log(b_k)\n logprobs_sampling += math.lgamma(a_k)\n\n logprobs_sampling += 0.5 * math.log(xi)\n logprobs_sampling += a_k * math.log(b)\n logprobs_sampling -= math.lgamma(a)\n\n probs_sampling[k] = math.exp(logprobs_sampling)\n\n # サンプリング確率の総和が1になるように正規化\n probs = probs_sampling / probs_sampling.sum()\n\n # トピックzをサンプリング\n z = latent_topic_n[n] = rd.choice(num_topic+1, 1, p=probs)[0]\n\n # 新しいトピックが選ばれたら\n if z == num_topic:\n num_topic += 1\n asg_data_k.append(0)\n X_k.append(0)\n C_k.append(0)\n\n # カウントに割り当てたトピック分を足す\n asg_data_k[z] += 1\n X_k[z] += x\n C_k[z] += math.pow(x, 2)\n\n # calculate results\n means = {}\n vars = {}\n probs = {}\n for k in xrange(num_topic):\n xi_k = xi + asg_data_k[k]\n a_k = a + 0.5 * asg_data_k[k]\n eta_k = (xi * eta + X_k[k]) / xi_k\n b_k = b + 0.5 * (xi * math.pow(eta, 2) - xi_k * math.pow(eta_k, 2) + C_k[k])\n means[k] = eta_k\n vars[k] = b_k / a_k\n probs[k] = float(asg_data_k[k]) / N\n\n ll = 0.\n for x in training:\n for k in xrange(num_topic):\n ll += math.log(probs[k])\n ll += math.log(1. / math.sqrt(2 * math.pi * vars[k]))\n ll -= math.pow(x - means[k], 2) / (2 * vars[k])\n\n return {'latent_topics': latent_topic_n, 'probs': probs, 'means': means, 'vars': vars, 'log_likelihood': ll}\n","sub_path":"iGMM.py","file_name":"iGMM.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"508573900","text":"# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\n\n'''\n BFS Approach\n \n Time Complexity: O(n) where n is the number of nodes in the tree\n'''\nclass Solution:\n def minDepth(self, root: Optional[TreeNode]) -> int:\n queue = [root] if root else []\n level = 1 if root else 0\n \n while(len(queue) > 0):\n print(level, len(queue))\n for i in range(len(queue)):\n currNode = queue.pop(0)\n if currNode.left == None and currNode.right == None:\n return level\n else:\n if currNode.left != None:\n queue.append(currNode.left)\n if currNode.right != None:\n queue.append(currNode.right)\n level += 1\n \n return level\n","sub_path":"Minimum Depth of Binary Tree/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"450684453","text":"import unittest\nimport os\nfrom unittest.mock import MagicMock\nfrom SegundoParcial import promedio\n\nclass TestPromedio(unittest.TestCase):\n def testMostrar(self):\n entrada = []\n\n def setUp(self):\n archivo = open(\"calificaciones_test.txt\", \"w\")\n archivo.write(\"Jose_Lopez quimica 89.00\"\"\\nJose_Lopez matematicas 85.34\"\"\\nMaria_Martinez fisica 95.50\"\"\\nMaria_Martinez español 90.00\")\n\n def testReader(self):\n entrada = ['Jose_Lopez quimica 89.00\\n', 'Jose_Lopez matematicas 85.34\\n', 'Maria_Martinez fisica 95.50\\n',\n 'Maria_Martinez espanol 90.00\\n']\n salida_esperada = {'Jose_Lopez': 87.17, 'Maria_Martinez': 92.75}\n\n fileMock = MagicMock()\n fileMock.reader.return_value = entrada\n\n real = promedio(fileMock)\n self.assertEqual(salida_esperada, real)\n\n def test_integration(self):\n salida_esperada = [(\"Jose_Lopez quimica\", 89.00),\n (\"Jose_Lopez matematicas\", 85.34),\n (\"Maria_Martinez fisica\", 95.50),\n (\"Maria_Martinez español\", 90.00)]\n\n real = promedio()\n self.assertEqual(salida_esperada, real)\n\n def tearDown(self):\n os.remove(\"calificaciones_test.txt\")\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"ago-dic-2020/practicas/SegundoParcial/SegundoParcial_test.py","file_name":"SegundoParcial_test.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"479382875","text":"import numpy as np\r\nimport pandas as pd\r\n\r\n#this will be a list of random integers used to reference stocks out of S&P 500 and used for number of shares\r\nrand_list = list(np.random.randint(0, 500, 35))\r\n\r\n#make shares list necessary for line below\r\nshares = [] \r\n\r\n#this deletes duplicates\r\n[shares.append(x) for x in rand_list if x not in shares]\r\n\r\n\r\n#we want to pull the S&P 500 tickers\r\nsp = pd.read_html(\"https://en.wikipedia.org/wiki/List_of_S%26P_500_companies\")[0]['Symbol']\r\n\r\n#make a list to hold the random tickers\r\ntickers = []\r\n\r\n#go through the shares list\r\nfor i in shares:\r\n \r\n #get the random ticker and add it our list\r\n tickers.append(sp[i])\r\n\r\n#now we want make out dataframe the way that streamlit\r\n\r\n#index column: tickers\r\n#next column: number of shares\r\n#next column: start date (not added yet)\r\n\r\n#put everything into the dataframe\r\noutput = pd.DataFrame({\"share_count\": shares}, index = tickers)\r\n\r\n#then output the file as xls (picked that format because file given from LITG came as xls)\r\noutput.to_excel(\"holdings.xlsx\")","sub_path":"dash_beta/generate_rand_holdings.py","file_name":"generate_rand_holdings.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"262036355","text":"import urllib.request\nimport json\nfrom lxml import html\nimport os\n\n# Funcao que transforma em string o HTML relativo a URL\ndef pagina_html(url):\n pagina = urllib.request.urlopen(url).read()\n return pagina\n\n# Funcao que transforma string em arvore de tags HTML pelo lxml\ndef xpath_arvore(url):\n pagina = pagina_html(url)\n arvore = html.document_fromstring(pagina)\n return arvore\n\n# Para executar apenas quando for chamado diretamente\nif __name__ == '__main__':\n\n # Criando pasta para armazenamento dos dados recolhidos\n # A pasta sera criada no mesmo lugar que o script estiver sendo executado\n # o nome da pasta sera ./jardiel\n directory = './jardiel'\n if not os.path.exists(directory):\n os.makedirs(directory)\n \n #criando um cliente com o mongo\n #client = pymongo.MongoClient(\"mongodb+srv://Jardiel:brasil317@brasil317-xxopu.mongodb.net/test\")\n\n # O nome dos elementos em que a tabela foi dividida\n elementos_da_tabela = ['orgao superior', 'UASG', 'Identificacao da Compra', 'Material_Servico', 'Descricao Item', 'Valor da compra', 'Data fim da ATA']\n\n # URL utilizada\n url_compras = 'http://compras.dados.gov.br'\n\n # Inicializando o dicionario de links de informacoes\n link_informacoes = []\n\n # Usando o lxml para fazer uma arvore com as TAGS de html\n url_home = url_compras+'/docs/home.html'\n painel_de_compras = xpath_arvore(url_home)\n\n # Pegando o link de todos os Modulos disponiveis\n caminho = '//div[@id=\"link-navegacao\"]/ul/li/a/@href'\n link_modulos = painel_de_compras.xpath(caminho)\n\n # Em cada um dos Modulo da pagina, pegar os links de cada metodo\n # Em cada pagina relativa ao modula ha dois tipos de metodo\n # Métodos de Consultas Básicas e Métodos de Informações Detalhadas\n # Nesse caso, ha diferenciacao explicita no dicionario criado\n\n # acessando a pagina de cada modulo\n for cada_modulo in link_modulos:\n \n # procurando as duas tabelas de metodos em cada modulo\n caminho = '//div[@id=\"conteudo_principal\"]/div/table'\n tabelas_metodo = xpath_arvore(url_compras+cada_modulo).xpath(caminho)\n\n # Modulo que o link eh referente (encontra-se no final do link, antes do .html)\n modulo = cada_modulo.split(\"/\")[-1].split(\"-\")[-1].split(\".\")[0]\n\n # Para cada tipo de metodo, pegar todos os metodos\n for cada_metodo in tabelas_metodo:\n\n # Pegar o tipo de metodo\n caminho = './caption/strong/text()'\n tipo_metodo = cada_metodo.xpath(caminho)[0]\n\n # Pegar todos os links dos metodos\n caminho = './tbody/tr/td/a/@href'\n links_metodo = cada_metodo.xpath(caminho)\n\n # Pegar o nome dos metodos\n caminho = './tbody/tr/td/a/text()'\n nomes_metodo = cada_metodo.xpath(caminho)\n\n # Os metodos devem ser iguais em numero aos seus nomes...\n if len(links_metodo) == len(nomes_metodo):\n\n # variavel para acompanhar nomes_metodo\n aux_nomes = 0\n \n # Armazenar todos os links no dicionario\n for cada_link in links_metodo:\n\n url_link_completa = 'http://compras.dados.gov.br/docs/'+cada_link\n dic = {'modulo' : modulo, 'tipo_metodo' : tipo_metodo, 'metodo' : nomes_metodo[aux_nomes], 'link' : url_link_completa} \n link_informacoes.append(dic)\n aux_nomes = aux_nomes + 1\n\n # Aqui ja se tem o dicionario link_informacoes construido\n\n # Pegando o link do formato json das informacoes\n # As informacoes realmente importantes aqui sao dos Metodos de Consultas Básicas\n\n # Construindo o arquivo com as informacoe dos Metodos de Consultas Basicas\n for cada_link in link_informacoes:\n\n # So precisamos das informacoes dos Metodos de Consulta Basicos\n # Eu exclui um dos links porque estava fazendo demorar muito\n if cada_link['tipo_metodo'] == 'Métodos de Consultas Básicas' and cada_link['metodo'] != 'uasgs':\n\n # Pegando o link exemplo e, entao, modificando para o formato json\n caminho = '//div[@id=\"conteudo_principal\"]/div/p/a/@href'\n link_html = xpath_arvore(cada_link['link']).xpath(caminho)[0]\n\n # Finalmente transformando cada link_html em link json\n url_json = link_html.split(\".html\")[0]+'.json'\n\n # Recebendo todos os dados da pagina em json e transformando em dicionario\n page_json = urllib.request.urlopen(url_json).read()\n dict_json = json.loads(page_json.decode('utf-8'))\n\n # Transformando dicionario em string json\n file_json = json.dumps(dict_json)\n \n # Armazenando o json em um arquivo com o nome do metodo\n file_path = directory+'/'+cada_link['metodo']+'.json'\n file = open(file_path, '+w')\n file.write(file_json)\n file.close()\n\n \n \n \n","sub_path":"jardiel_scraping.py","file_name":"jardiel_scraping.py","file_ext":"py","file_size_in_byte":5042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"351084906","text":"from tools import variables as var\nfrom tools import constants as con\nfrom tools import translate as tr\nimport datetime\nimport os\n\ndef logger(*output, logtype=\"\", type=\"normal\", display=True, write=True, splitter=\"\\n\", form=[], formo=[], formt=[]): # logs everything to file and/or screen. always use this\n output = get(output, splitter)\n timestamp = str(datetime.datetime.now())\n timestamp = \"[{0}] ({1}) \".format(timestamp[:10], timestamp[11:19])\n logall = None\n if form and not form == list(form):\n form = [form]\n toget = \"\"\n toform = []\n toforml = []\n forml = list(form)\n if \"\\n\" in output:\n indx = output.index(\"\\n\")\n toget = output[indx+1:]\n output = output[:indx]\n trout = output # not a fish\n\n if logtype:\n for typed in con.LOGGERS.keys():\n if con.LOGGERS[typed] == logtype:\n type = typed\n if not type:\n type = \"normal\"\n\n if output and output.isupper() and not output.islower() and not type in con.IGNORE_CHECK: # to fetch in translate, make sure it's not \"\" or non-words\n newout = getattr(tr, output)\n outlang = \"English\" if type in con.IGNORE_TRANSLATE else var.LANGUAGE\n trout = newout[outlang]\n output = newout[\"English\"]\n iter = 0\n foring = 0\n while True:\n if \"{\" + str(iter) + \"}\" in output: # output and trout should have the same amount of formats\n for writer in form:\n if writer.isupper(): # to translate as well\n forml[foring] = getattr(tr, writer)[var.LANGUAGE]\n form[foring] = getattr(tr, writer)[\"English\"]\n foring += 1\n foring = 0\n iter += 1\n else:\n if formo and formt:\n form = formo\n forml = formt\n trout = trout.format(*forml)\n output = output.format(*form)\n forml = forml[iter:]\n form = form[iter:]\n break\n toform = list(form)\n toforml = list(forml)\n\n if type in con.IGNORE_TIMESTAMP:\n timestamp = \"\"\n if var.LOG_EVERYTHING or var.DEV_LOG:\n logall = con.LOGGERS[\"all\"]\n if not logtype:\n if type not in con.LOGGERS.keys():\n type = \"normal\"\n logtype = con.LOGGERS[type]\n if var.DEBUG_MODE or var.DEV_LOG or var.WRITE_EVERYTHING: # if there's an error I'll want every possible information. that's the way to go\n write = True\n if var.DEBUG_MODE or var.DISPLAY_EVERYTHING:\n display = True\n logfile = getattr(var, logtype + \"_FILE\")\n log_ext = getattr(var, logtype + \"_EXT\")\n file = logfile + \".\" + log_ext\n\n newfile = not os.path.isfile(os.getcwd() + \"/\" + file)\n if display:\n print(trout)\n if write:\n f = open(os.getcwd() + \"/\" + file, \"w\" if newfile else \"r+\")\n f.seek(0, 2)\n if not var.LANGUAGE == \"English\" and type not in con.IGNORE_TRANSLATE:\n filel = con.LANGUAGES[var.LANGUAGE] + \"_\" + file\n newfilel = not os.path.isfile(os.getcwd() + \"/\" + filel)\n fl = open(os.getcwd() + \"/\" + filel, \"w\" if newfilel else \"r+\")\n fl.seek(0, 2)\n if type in con.IGNORE_NEWLINE:\n newfile = True\n newfilel = True\n if (not var.INITIALIZED or var.RETRY) and not newfile:\n f.write(\"\\n\\n\" + timestamp + output + \"\\n\")\n else:\n f.write(timestamp + output + \"\\n\")\n if logall:\n outputa = \"type.{0} - {1}\".format(type, output)\n filea = getattr(var, logall + \"_FILE\") + \".\" + getattr(var, logall + \"_EXT\")\n fa = open(os.getcwd() + \"/\" + filea, \"w\" if var.NEWFILE_ALL else \"r+\")\n var.NEWFILE_ALL = False\n fa.seek(0, 2)\n alines = list(con.LOGGERS)\n\n for lang in alines:\n if lang in con.IGNORE_MIXED:\n alines.remove(lang)\n if type in alines:\n if var.RETRY:\n fa.write(\"\\n\\n\" + timestamp + outputa + \"\\n\")\n else:\n fa.write(timestamp + outputa + \"\\n\")\n fa.close()\n if not var.LANGUAGE == \"English\" and type not in con.IGNORE_MIXED:\n trouta = \"type.{0} - {1}\".format(type, trout)\n filet = con.LANGUAGES[var.LANGUAGE] + \"_\" + filea\n ft = open(os.getcwd() + \"/\" + filet, \"w\" if var.NEWFILE_TRA else \"r+\")\n var.NEWFILE_TRA = False\n ft.seek(0, 2)\n if var.RETRY:\n ft.write(\"\\n\\n\" + timestamp + trouta + \"\\n\")\n else:\n ft.write(timestamp + trouta + \"\\n\")\n ft.close()\n if not var.LANGUAGE == \"English\" and type not in con.IGNORE_TRANSLATE:\n if (not var.INITIALIZED or var.RETRY) and not newfilel:\n fl.write(\"\\n\\n\" + timestamp + trout + \"\\n\")\n else:\n fl.write(timestamp + trout + \"\\n\")\n fl.close()\n f.close()\n if toget:\n logger(toget, logtype=logtype, display=display, write=write, formo=toform, formt=toforml) # don't iterate again if already translated\n\ndef multiple(*output, types=[], display=True, write=True, splitter=\"\\n\", form=[]):\n output = get(output, splitter)\n if \"all\" in types:\n log_it = []\n for logged in con.LOGGERS.keys():\n if logged in con.IGNORE_ALL:\n continue\n if con.LOGGERS[logged] not in log_it:\n log_it.append(con.LOGGERS[logged])\n for l in log_it:\n logger(output, logtype=l, display=display, write=write, splitter=splitter, form=form)\n elif types:\n for t in types:\n logger(output, type=t, display=display, write=write, splitter=splitter, form=form)\n else: # no type\n logger(output, display=display, write=write, splitter=splitter, form=form)\n\ndef help(*output, type=\"help\", write=False, display=True, splitter=\"\\n\", form=[]):\n output = get(output, splitter)\n logger(output, type=type, write=write, display=display, splitter=splitter, form=form)\n\ndef get(output, splitter):\n output = list(output)\n msg = None\n for line in output:\n if msg is None:\n msg = line\n else:\n msg += splitter + line\n return msg\n\ndef preset(): # makes a preset file with current settings\n userset = []\n _usrset = []\n bootset = []\n for setting in var.USER_SETTINGS.keys():\n value = getattr(var, setting)\n for set, prefix in con.USER_SETTINGS.items():\n if set == setting:\n userset.append(\"{2}{0}{1}\".format(prefix, value, con.USER_VAR))\n _usrset.append(\"{0}={1}\".format(prefix, value))\n break\n for setting in var.PATH_SETTINGS.keys():\n value = getattr(var, setting)\n for set, prefix in con.PATH_SETTINGS.items():\n if set == setting:\n userset.append(\"{2}{0}{1}\".format(prefix, value, con.PATH_VAR))\n _usrset.append(\"{0}={1}\".format(prefix, value))\n break\n for setting in var.BOOT_PACK_SETTINGS.keys():\n value = getattr(var, setting)\n for set in con.BOOT_PACK_SETTINGS.keys():\n if set == setting:\n bootset.append(value)\n break\n logger(\"SETTINGS: {0}\".format(\" \".join(userset)))\n logger(\"\")\n logger(\"{2} PACK: {0}{1}\".format(con.BOOT_PACK_VAR, \"\".join(bootset), con.PROGRAM_NAME.upper()))\n logger(\"\\n\".join(_usrset), con.BOOT_PACK_VAR + \"=\" + \"\".join(bootset), type=\"settings\", display=False, splitter=\"\\n\")","sub_path":"tools/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":7712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"636954515","text":"import pyblock\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import sem\n\n\ndef optimally_reblocked(data): # , cols):\n \"\"\"\n Uses pyblock to find optimal reblocking of input data. Takes in pandas\n DataFrame of raw data and selected columns to reblock, returns DataFrame\n of reblocked data.\n \"\"\"\n stats = pyblock.pd_utils.reblock(data)\n reblocked_data = pyblock.pd_utils.reblock_summary(stats[1]).squeeze()\n reblocks = pyblock.pd_utils.optimal_block(stats[1])\n reblocked_data[\"reblocks\"] = reblocks\n return reblocked_data\n\n\ndef test_reblocking():\n \"\"\"\n Tests reblocking against known distribution.\n \"\"\"\n\n def corr_data(N, L):\n \"\"\"\n Creates correlated data. Taken from \n https://pyblock.readthedocs.io/en/latest/tutorial.html.\n \"\"\"\n return np.convolve(np.random.randn(2 ** N), np.ones(2 ** L) / 10, \"same\")\n\n def reblock(data, reblocks, col):\n \"\"\"\n Reblocks data according to “Error estimates on averages of correlated data”,\n H. Flyvbjerg, H.G. Petersen, J. Chem. Phys. 91, 461 (1989).\n \"\"\"\n edat = data[col].values\n n = len(edat)\n for i in range(reblocks):\n edat_prime = []\n for j in range(1, int(len(edat) / 2 + 1)):\n edat_prime.append((edat[2 * j - 2] + edat[2 * j - 1]) / 2)\n edat = edat_prime\n return np.array(edat)\n\n n = 11\n cols = [\"test_data1\", \"test_data2\"]\n dat1 = corr_data(n, 4)\n dat2 = corr_data(n, 7)\n test_data = pd.DataFrame(data={cols[0]: dat1, cols[1]: dat2})\n reblocked_data = optimally_reblocked(test_data[cols])\n for c in cols:\n row = reblocked_data.loc[c]\n reblocks = reblocked_data[\"reblocks\"].values[0]\n std_err = sem(reblock(test_data, reblocks, c))\n std_err_err = std_err / np.sqrt(2 * (2 ** (n - reblocks) - 1))\n\n assert np.isclose(\n row[\"mean\"], np.mean(test_data[c]), 1e-10, 1e-12\n ), \"Means are not equal\"\n assert np.isclose(\n row[\"standard error\"], std_err, 1e-10, 1e-12\n ), \"Standard errors are not equal\"\n assert np.isclose(\n row[\"standard error error\"], std_err_err, 1e-10, 1e-12\n ), \"Standard error errors are not equal\"\n\n\nif __name__ == \"__main__\":\n test_reblocking()\n","sub_path":"pyqmc/reblock.py","file_name":"reblock.py","file_ext":"py","file_size_in_byte":2358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"455409044","text":"#!usr/bin/python\n\n\ndef max_increase_city_skyline(grid):\n\n max_left = list()\n max_top = list()\n\n temp_list = list()\n\n j = 0\n while j < len(grid[0]):\n for i in range(0, len(grid)):\n temp_list.append(grid[i][j])\n if j == len(grid)-1:\n max_left.append(max(grid[i]))\n j += 1\n max_top.append(max(temp_list))\n temp_list = list()\n\n sum = 0\n for i in range(len(grid[0])):\n for j in range(len(grid)):\n sum += (grid[i][j] - min(max_left[i], max_top[j]))\n\n return abs(sum)\n\n\nex_grid = [[3, 0, 8, 4],\n [2, 4, 5, 7],\n [9, 2, 6, 3],\n [0, 3, 1, 0]]\nprint(max_increase_city_skyline(ex_grid))\n","sub_path":"problem_solving/misc/max_increase_city_skyline.py","file_name":"max_increase_city_skyline.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"554320441","text":"def count_open_spots(board):\n count = 0\n for row in board:\n count += row.count(0)\n return count\n\ndef is_possible(board):\n return count_open_spots(board) % 3 == 0\n\ndef is_covered(board):\n return count_open_spots(board) == 0\n\n_blocks = [[(0, 0), (1, 0), (0, 1)],\n [(0, 0), (1, 0), (1, 1)],\n [(0, 0), (-1, 1), (0, 1)],\n [(0, 0), (0, 1), (1, 1)]]\n\ndef cover(board, pos, delta):\n result = True\n for p in pos:\n x = p[0]\n y = p[1]\n if y < 0 or y > len(board) or x < 0 or x > len(board[y]):\n result = False\n else:\n board[y][x] += delta\n if board[y][x] > 1:\n result = False\n return result\n\ndef next_pos(board, x, y):\n for col in range(x+1, len(board[y])):\n if board[y][col] == 0:\n return (col, y)\n for row in range(y+1, len(board)):\n for col in range(len(board[row])):\n if board[row][col] == 0:\n return (col, row)\n return (col, row)\n\ndef exhaustive_search(board, x, y):\n if not is_possible(board):\n return 0\n if is_covered(board):\n return 1\n if y == len(board)-1:\n return 0\n\n count = 0\n error = False\n for block in _blocks:\n pos = [(x+bx, y+by) for (bx, by) in block]\n if cover(board, pos, 1):\n count += exhaustive_search(board, *next_pos(board, x, y))\n cover(board, pos, -1)\n return count\n\nnum_cases = eval(input())\n\nfor _ in range(num_cases):\n hw = input().split()\n height = eval(hw[0])\n width = eval(hw[1])\n board = []\n for _ in range(height):\n board.append([eval(c)\n for c in input().replace('.', '0').replace('#', '1')])\n print(exhaustive_search(board, *next_pos(board, 0, 0)))\n","sub_path":"BOARDCOVER/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"5742047","text":"def intersects(line1, line2):\r\n def onSeg(p, q, r):\r\n if (q[0] <= max(p[0],r[0]) and q[0] >= min(p[0],r[0]) and\r\n q[0] <= max(p[1],r[1]) and q[1] >= min(p[1],r[1])):\r\n return True\r\n return False\r\n #0 -> colinear, 1 -> clockwise, 2 -> ccw \r\n def orientation(p,q,r):\r\n val = (q[1]-p[1]) * (r[0] - q[0]) - (q[0]-p[0]) * (r[1] - q[1])\r\n if val == 0:\r\n return 0\r\n if val > 0:\r\n return 1\r\n return 2\r\n p1 , q1 = line1\r\n p2 , q2 = line2\r\n o1 = orientation(p1, q1, p2)\r\n o2 = orientation(p1, q1, q2)\r\n o3 = orientation(p2, q2, p1)\r\n o4 = orientation(p2, q2, q1)\r\n\r\n x1, y1 = p1\r\n x2, y2 = q1\r\n x3, y3 = p2\r\n x4, y4 = q2\r\n \r\n if (o1 != o2 and o3 != o4): #general\r\n xd = (x1*y2 - y1*x2)*(x3-x4)-(x1-x2)*(x3*y4-y3*x4)\r\n xn = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4)\r\n yd = (x1*y2 - y1*x2)*(y3-y4)-(y1-y2)*(x3*y4-y3*x4)\r\n yn = (x1-x2)*(y3-y4) - (y1-y2)*(x3-x4)\r\n return (xd/xn , yd/yn)\r\n if (o1 == 0 and onSeg(p1,p2,q1)): #p2 lies on p1q1\r\n return p2\r\n if (o2 == 0 and onSeg(p1,q2,q1)): #q2 lies on p1q1\r\n return q2\r\n if (o3 == 0 and onSeg(p2,p1,q2)): #p1 lies on p2q2\r\n return p1\r\n if (o4 == 0 and onSeg(p2,q1,q2)): #q1 lies on p2q2\r\n return q1\r\n return (-1,-1)\r\n \r\n","sub_path":"insec.py","file_name":"insec.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"54575774","text":"import cv2\n\ncap = cv2.VideoCapture(0)\nret, frame = cap.read()\nframe = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) \nframe = cv2.equalizeHist(frame)\nret,frame=cv2.threshold(frame,80,255,cv2.THRESH_BINARY_INV)\nprint(len(frame))\nprint(len(frame[0]))\nprint(frame)\ncv2.imshow(\"display\", frame) # 显示\ncv2.waitKey(0)","sub_path":"pic_test.py","file_name":"pic_test.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"616155731","text":"from twisted.internet.protocol import DatagramProtocol\nfrom twisted.internet import reactor\n\nclass Echo(DatagramProtocol):\n\n def datagramReceived(self, data, address):\n print(\"received %r from %s\" % (data.decode('utf-8'), address))\n self.transport.write(data, address)\n\nreactor.listenUDP(1234, Echo())\nreactor.run()","sub_path":"connected_udp_server.py","file_name":"connected_udp_server.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"408752051","text":"\"\"\"Delay line linearity characterization\nFriedrich Schotte, Jul 22, 2015 - Jul 23, 2015\nSetup:\nRamsay-100B RF Generator, 351.93398 MHz +10 dBm -> FPGA RF IN\nFPGA 1: X-scope trig -> CH1, DC50, 500 mV/div\nFPGA 13: ps L oscill -> DC block -> 90-MHz low-pass -> CH2, DC50, 500 mV/div\nTimebase 5 ns/div\nMeasurement P1 CH2, time@level, Percent, 50%, Slope Pos, Gate Start 4.5 div, Stop 5.5 div\nFPGA Frequency: 41 Hz\n\"\"\"\n__version__ = \"2.1\"\nfrom instrumentation import timing_system,lxd,bcf,clksrc,lecroy_scope\nfrom scan import rscan,timescan as tscan\nfrom sleep import sleep\ndelay = lecroy_scope(\"pico21\").measurement(1)\ntmax = 5/bcf\n\ndef scan():\n lxd.value = 0\n data = rscan([lxd,delay.gate.start,delay.gate.stop],0,[tmax,-tmax,-tmax],\n 640,delay,averaging_time=60.0,logfile=\"logfiles/delay.log\")\n\ndef timescan():\n data = tscan(delay,averaging_time=4.0,logfile=\"logfiles/delay.log\")\n\ndef peridiocally_interrupt_clock():\n while True:\n try:\n clksrc.state = 'RJ45:1'\n sleep(4)\n clksrc.state = 'RF IN'\n sleep(60-4)\n except KeyboardInterrupt: break\n clksrc.state = 'RF IN'\n\nif __name__ == \"__main__\":\n print('timing_system.ip_address = %r' % timing_system.ip_address)\n print('scan()')\n print('peridiocally_interrupt_clock()')\n print('timescan()')\n","sub_path":"test/timing_system_test-2.1.py","file_name":"timing_system_test-2.1.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"106216020","text":"# This is my implementation of multiclass SVM.\n\nimport numpy as np\n\n# gradient of y wrt w given x, activations, and C\n# There are M training examples and N features (including bias)\n# x is MxN\n# y is Mx1\n# w is Nx1\n# activations is Mx1\n# C is scalar\n# Returns Nx1 column of gradients\ndef get_gradient(x, y, w, activations, C):\n values = -1 * x * y # MxN\n mask = (activations[:, 0] == 0.0)\n values[mask, :] = 0\n avg = np.mean(values, axis=0, keepdims=True)\n return C*avg.T + w\n\n# x is MxN\n# y is Mx1\n# w is Nx1\n# Returns Mx1 column of activations\ndef get_activations(x, y, w):\n #print \"x\", x.shape\n #print \"y\", y.shape\n #print \"w\", w.shape\n return np.maximum(0, 1 - (y * np.matmul(x, w)))\n\n# w is Nx1\n# activations is Mx1\n# C is scalar\n# Returns a scalar\ndef get_cost(w, activations, C):\n regularizer = 0.5 * np.sum(np.square(w))\n return C * np.mean(activations) + regularizer\n\n# x is MxN\n# y is Mx1\n# w is Nx1\n# C is scalar\n# Returns scalar cost and Nx1 column of gradients\ndef get_cost_and_gradient(x, y, w, C):\n activations = get_activations(x, y, w)\n cost = get_cost(w, activations, C)\n gradient = get_gradient(x, y, w, activations, C)\n return (cost, gradient)\n\n# x is MxN\n# y is Mx1\n# w is Nx1\n# C is scalar\n# Returns current cost, new w\ndef training_step(x, y, w, C, learning_rate):\n (cost, gradient) = get_cost_and_gradient(x, y, w, C)\n step = -1 * learning_rate * gradient\n return (cost, w + step)\n\n# x is MxN\n# ws is NxL (if there are L classes)\n# Returns a tuple:\n# 1) array of size M, where each entry is index of predicted class\n# 2) array of MxL, where each entry is the per-category SVM prediction\ndef get_multiclass_predictions(x, ws, add_ones=True):\n # Add 1s to x for bias term\n M = x.shape[0]\n if add_ones:\n x = np.append(x, np.ones((M, 1)), axis=1)\n values = np.matmul(x, ws)\n predictions = np.argmax(values, axis=1)\n per_class_predictions = np.where(values > 0, 1, -1)\n return (predictions, per_class_predictions)\n\n# We need to reimplement this here, since the implementation in\n# classifier_utils.py is based on TensorFlow, and we want to implement\n# from scratch.\ndef get_np_weighted_f1(predictions, actual, num_labels):\n try:\n f1s = []\n totals = []\n for label in xrange(num_labels):\n true_positives = np.count_nonzero(np.logical_and(np.equal(label, actual),\n np.equal(label, predictions)))\n positive_labels = np.count_nonzero(np.equal(label, actual))\n positive_predictions = np.count_nonzero(np.equal(label, predictions))\n recall = float(true_positives) / float(positive_labels)\n precision = float(true_positives) / float(positive_predictions)\n f1 = 2*precision*recall / (precision + recall)\n f1s.append(f1)\n totals.append(positive_labels)\n total_weight = float(sum(totals))\n weights = [w / total_weight for w in totals]\n f1_avg = sum([w * f1 for (w, f1) in zip(weights, f1s)])\n return f1_avg\n except ZeroDivisionError:\n return 0\n\n# Calculate statistics about our current model. Optinally print them,\n# and return a dictionary of our results.\ndef status_report(e, step, x, y, val_x, val_y, test_x, test_y, ws, C, quiet=False):\n training_losses = []\n validation_losses = []\n test_losses = []\n for label in xrange(len(ws)):\n w = ws[label]\n (train_loss, _) = get_cost_and_gradient(x, y[:, label:label+1], w, C)\n (val_loss, _) = get_cost_and_gradient(val_x, val_y[:, label:label+1], w, C)\n (test_loss, _) = get_cost_and_gradient(test_x, test_y[:, label:label+1], w, C)\n training_losses.append(train_loss)\n validation_losses.append(val_loss)\n test_losses.append(test_loss)\n (predictions, per_class_predictions) = get_multiclass_predictions(\n test_x, np.hstack(ws), add_ones=False)\n # Per-class accuracy\n per_class_accuracy = np.mean(np.equal(test_y, per_class_predictions).astype('float32'),\n axis=0)\n # Overall accuracy/f1 (test)\n actual = np.argmax(test_y, axis=1)\n correct = np.equal(predictions, actual)\n overall_test_accuracy = np.mean(correct.astype('float32'))\n test_weighted_f1 = get_np_weighted_f1(predictions, actual, len(ws))\n # Overall accuracy/f1 (validation)\n (predictions, _) = get_multiclass_predictions(val_x, np.hstack(ws), add_ones=False)\n actual = np.argmax(val_y, axis=1)\n correct = np.equal(predictions, actual)\n overall_val_accuracy = np.mean(correct.astype('float32'))\n val_weighted_f1 = get_np_weighted_f1(predictions, actual, len(ws))\n \n if not quiet:\n print (\"Epoch %s, step %s:\\n\\ttrain loss %s,\\n\\tvalidation loss %s,\" +\n \"\\n\\ttest loss %s,\\n\\tper-category test accuracy %s,\" +\n \"\\n\\toverall validation accuracy %f,\\n\\toverall test accuracy %f,\" +\n \"\\n\\tvalidation f1 %f,\\n\\ttest f1 %f\") % (\n e, step, training_losses, validation_losses, test_losses, per_class_accuracy,\n overall_val_accuracy, overall_test_accuracy, val_weighted_f1, test_weighted_f1)\n \n return {\n 'train_loss': training_losses,\n 'val_loss': validation_losses,\n 'test_loss': test_losses,\n 'val_accuracy': overall_val_accuracy,\n 'test_accuracy': overall_test_accuracy,\n 'val_f1': val_weighted_f1,\n 'test_f1': test_weighted_f1,\n }\n\n# Train a multiclass SVM. Computes statistics at each training step\n# and implements early stopping.\ndef multiclass_train_loop(x, y, val_x, val_y, test_x, test_y, C, learning_rate, batch_size, epochs,\n quiet=False, init_ws=None):\n # Make status variables global so that we can kill the training loop at any\n # time if it's taking too long, and as long as we keep the shell alive, we\n # will be able to inspect our current progress & results.\n global loss, val_loss, test_loss, params # should be accessed as variable[timestep][category]\n loss = []\n val_loss = []\n test_loss = []\n params = []\n \n global val_accuracy, test_accuracy, val_f1, test_f1 # should be accesses as variable[timestep]\n val_accuracy = []\n test_accuracy = []\n val_f1 = []\n test_f1 = []\n \n np.random.seed(31415) # repeatability\n\n # Sanity-check the input sizes\n L = y.shape[1]\n (M, N) = x.shape\n assert y.shape == (M, L)\n (M_val, N_val) = val_x.shape\n assert N_val == N\n assert val_y.shape == (M_val, L)\n (M_test, N_test) = test_x.shape\n assert N_test == N\n assert test_y.shape == (M_test, L)\n \n # Convert from one-hot labels to per-class -1/+1 labels\n y = y * 2 - 1\n val_y = val_y * 2 - 1\n test_y = test_y * 2 - 1\n\n # Intialize weights for each class\n ws = [np.random.normal(size=(N+1, 1)) for _ in xrange(L)] if init_ws is None \\\n else np.hsplit(init_ws, init_ws.shape[1])\n # Add 1s to x for bias term\n x = np.append(x, np.ones((M, 1)), axis=1)\n test_x = np.append(test_x, np.ones((M_test, 1)), axis=1)\n val_x = np.append(val_x, np.ones((M_val, 1)), axis=1)\n\n # Run training\n for e in xrange(epochs):\n for i in xrange(0, M, batch_size):\n for label in xrange(L):\n w = ws[label]\n (cost, new_w) = training_step(\n x[i:i+batch_size], y[i:i+batch_size, label:label+1], w, C, learning_rate)\n ws[label] = new_w\n step = i / batch_size\n if step % 50 == 0:\n status_dict = status_report(\n e, step, x[i:i+batch_size], y[i:i+batch_size],\n val_x, val_y, test_x, test_y, ws, C, quiet=quiet)\n loss.append(status_dict['train_loss'])\n val_loss.append(status_dict['val_loss'])\n test_loss.append(status_dict['test_loss'])\n val_accuracy.append(status_dict['val_accuracy'])\n test_accuracy.append(status_dict['test_accuracy'])\n val_f1.append(status_dict['val_f1'])\n test_f1.append(status_dict['test_f1'])\n params.append(list(ws))\n \n status_report(\"FINAL\", \"FINAL\", x[0:batch_size], y[0:batch_size],\n val_x, val_y, test_x, test_y, ws, C, quiet=False)\n\n # Return per-parameter weights\n return np.hstack(ws)\n","sub_path":"svm.py","file_name":"svm.py","file_ext":"py","file_size_in_byte":8420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"381641033","text":"\nimport logging \n \nfrom flask import current_app, Flask, redirect, url_for, jsonify, request, render_template, send_file\nimport json\nfrom hipsterflixter import alternate_alg\n#from alternate_alg import get_12_random, get_hipster_movies\n\ndef create_app(config, debug=False, testing=False, config_overrides=None):\n app = Flask(__name__)\n app.config.from_object(config)\n\n app.debug = debug\n app.testing = testing\n\n if config_overrides:\n app.config.update(config_overrides)\n\n # Configure logging\n if not app.testing:\n logging.basicConfig(level=logging.INFO)\n\n # Add a default root route.\n @app.route(\"/\", methods=['GET'])\n def test():\n return jsonify({'message' : 'hipster flixter' })\n\n @app.route(\"/hello\")\n def hello():\n return render_template('hello.html')\n\n @app.route(\"/poster-not-available.png\")\n def poster_pic():\n return send_file('images/poster-not-available.png', mimetype='image/png')\n\n @app.route(\"/Hipster_Poster\")\n def poster_pic_2():\n return send_file('images/hipster_poster.png', mimetype='image/png')\n\n @app.route(\"/random\", methods=['GET'])\n def random():\n message = alternate_alg.get_12_random()\n #pro conversion of dicts to a dict of arrays using DICT COMPREHENSION \n new_dict = {item['id']:item for item in message}\n return jsonify(new_dict)\n\n @app.route(\"/movies/\", methods=['GET'])\n def foo(jsdata):\n data = json.loads(jsdata)\n movie_ids = [x for x in data]\n message = alternate_alg.get_hipster_movies(movie_ids)\n return jsonify(message)\n\n # Add an error handler. This is useful for debugging the live application,\n # however, you should disable the output of the exception for production\n # applications.\n @app.errorhandler(500)\n def server_error(e):\n return \"\"\"\n An internal error occurred: 
    {}
    \n See logs for full stacktrace.\n \"\"\".format(e), 500\n\n return app\n\ndef get_model():\n model_backend = current_app.config['DATA_BACKEND']\n if model_backend == 'mongodb':\n from . import model_mongodb\n model = model_mongodb\n else:\n raise ValueError(\n \"No appropriate databackend configured. \"\n \"Please specify mongodb\")\n\n return model","sub_path":"hipster-flixter/hipsterflixter/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"456766737","text":"#!/usr/bin/env python3\n# Copyright (c) 2015\n#\n# All rights reserved.\n#\n# This file is distributed under the Clear BSD license.\n# The full text can be found in LICENSE in the root directory.\n# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4\n\nimport glob\nimport hashlib\nimport ipaddress\nimport logging\nimport os\nimport re\nimport tempfile\nfrom pathlib import Path\n\nimport boardfarm\nfrom aenum import Enum\nfrom boardfarm.exceptions import BftCommandNotFound, BootFail, CodeError\nfrom boardfarm.lib import SnmpHelper\nfrom boardfarm.lib.common import cmd_exists, keccak512_checksum, retry_on_exception\nfrom boardfarm.lib.DeviceManager import device_type\nfrom debtcollector import deprecate\n\nfrom boardfarm_docsis.exceptions import (\n CfgUnknownType,\n CMCfgEncodeFailed,\n IpAddrMismatch,\n MTACfgEncodeFailed,\n)\nfrom boardfarm_docsis.use_cases.cmts_interactions import is_bpi_privacy_disabled\n\nfrom .cfg_helper import CfgGenerator\n\ntry:\n # Python 2\n import Tkinter\nexcept Exception:\n # Python 3\n import tkinter as Tkinter\n\nlogger = logging.getLogger(\"bft\")\n\n\nclass cfg_type(Enum):\n UNKNOWN = 0\n CM = 1\n MTA = 2\n\n\nclass base_cfg:\n \"\"\"\n Name: docsis module\n Purpose: docsis operating.\n Input: Absolute path of text file\n Fuction:\n decode():\n return output file name(.txt)\n encode()\n return output file name(.cfg or .bin)\n \"\"\"\n\n mibs_path_arg = \"\"\n\n def __init__(self, file_or_obj, tmpdir=None, mibs_paths=None):\n # TODO: fix at some point, this tmpdir is already relative to the CM config you\n # are grabbing? Not ideal as that dir might not be writeable, or a tftp or http URL\n # at some point - need to use a real local tmpdir or maybe even results so we can\n # save the resulting artifacts in other tools\n\n self.init_copy(file_or_obj, tmpdir=tmpdir, mibs_paths=mibs_paths)\n\n def init_copy(self, file_or_obj, tmpdir=None, mibs_paths=None):\n if mibs_paths is None:\n mibs_paths = []\n\n if tmpdir is None:\n tmpdir = tempfile.mkdtemp()\n\n if mibs_paths != []:\n mibs_path_arg = \"-M \"\n for mibs_path in mibs_paths:\n mibs_path_arg = mibs_path_arg + \":\" + mibs_path\n\n self.mibs_path_arg = mibs_path_arg\n\n # TODO: this is all a bit wild here, need to clean up everything..\n if isinstance(file_or_obj, cm_cfg):\n self.cm_cfg = file_or_obj\n # TODO: this seems like the wrong place to store these but OK\n self.dir_path = os.path.join(os.path.split(__file__)[0], tmpdir)\n self.file = self.cm_cfg.original_fname\n self.file_path = os.path.join(self.dir_path, self.file)\n elif isinstance(file_or_obj, mta_cfg):\n self.mta_cfg = file_or_obj\n # TODO: this seems like the wrong place to store these but OK\n self.dir_path = os.path.join(os.path.split(__file__)[0], tmpdir)\n self.file = self.mta_cfg.original_fname\n self.file_path = os.path.join(self.dir_path, self.file)\n else:\n self.file_path = file_or_obj\n self.dir_path = os.path.join(os.path.split(file_or_obj)[0], tmpdir)\n self.file = os.path.split(file_or_obj)[1]\n\n # make target tmpdir if it does not exist\n try:\n os.makedirs(self.dir_path)\n except OSError as err:\n import errno\n\n # Reraise the error unless it's about an already existing directory\n if err.errno != errno.EEXIST or not os.path.isdir(self.dir_path):\n raise\n\n if isinstance(file_or_obj, cm_cfg):\n self.cm_cfg.save(self.file_path)\n if isinstance(file_or_obj, mta_cfg):\n self.mta_cfg.save(self.file_path)\n\n # Though the method requires an arg pylint throws some false positive error, hence added disable\n assert cmd_exists(\"docsis\") # pylint: disable=E1121\n assert cmd_exists(\"tclsh\") # pylint: disable=E1121\n tclsh = Tkinter.Tcl()\n assert tclsh.eval(\n \"package require sha1\"\n ), \"please run apt-get install tcllib first\"\n\n def get_cfg_type(self):\n with open(self.file_path) as cfg:\n # TODO: this is OK but could be better\n data = cfg.read()\n # BAD: this section needs cleaning up as parts are vendor specific\n if data.startswith(\"Main\"):\n return cfg_type.CM\n elif data.startswith(\"\\t.\"):\n return cfg_type.MTA\n else:\n return cfg_type.UNKNOWN\n\n def decode(self):\n if \".cfg\" in self.file:\n os.system(\n f\"docsis -d {self.file_path} > {self.file_path.replace('.cfg', '.txt')}\"\n )\n assert os.path.exists(self.file.replace(\".cfg\", \".txt\"))\n\n return self.file.replace(\".cfg\", \".txt\")\n\n # TODO: decode MTA?\n\n def _run_cmd(self, cmd):\n logger.debug(cmd)\n os.system(cmd)\n\n # this method can be overridden for vendor specific commands\n def encode_mta(self, mibs_path_arg, file_path, mtacfg_path):\n self._run_cmd(f\"docsis {mibs_path_arg} -p {file_path} {mtacfg_path}\")\n if not os.path.exists(mtacfg_path):\n raise MTACfgEncodeFailed()\n return mtacfg_path\n\n # this method can be overridden for vendor specific commands\n def encode_cm(self, mibs_path_arg, file_path, cmcfg_path, key_file=\"/dev/null\"):\n self._run_cmd(f\"docsis {mibs_path_arg} -e {file_path} {key_file} {cmcfg_path}\")\n if not os.path.exists(cmcfg_path):\n raise CMCfgEncodeFailed()\n return cmcfg_path\n\n def encode(self):\n def encode_mta():\n mtacfg_name = self.file.replace(\".txt\", \".bin\")\n mtacfg_path = os.path.join(self.dir_path, mtacfg_name)\n if os.path.isfile(mtacfg_path):\n os.remove(mtacfg_path)\n return self.encode_mta(self.mibs_path_arg, self.file_path, mtacfg_path)\n\n def encode_cm():\n cmcfg_name = self.file.replace(\".txt\", \".cfg\")\n cmcfg_path = os.path.join(self.dir_path, cmcfg_name)\n if os.path.isfile(cmcfg_path):\n os.remove(cmcfg_path)\n return self.encode_cm(self.mibs_path_arg, self.file_path, cmcfg_path)\n\n if self.get_cfg_type() == cfg_type.CM:\n return encode_cm()\n elif self.get_cfg_type() == cfg_type.MTA:\n return encode_mta()\n else:\n raise CfgUnknownType()\n\n def load(self, cfg_data, method=\"txt\"):\n \"\"\"Load cfg from txt file, for modification\"\"\"\n\n if method == \"txt\":\n self.txt = cfg_data\n if method == \"file\":\n with open(cfg_data) as txt:\n self.txt = txt.read()\n\n # this is old. This would go eventually.\n @staticmethod\n def configure_board(provisioner, board, **kwargs):\n cm_cfg = kwargs.pop(\"cm_cfg\", None)\n mta_cfg = kwargs.pop(\"mta_cfg\", None)\n\n board.update_docsis_config(cm_cfg=cm_cfg, mta_cfg=mta_cfg, **kwargs)\n\n override = kwargs.get(\"force\", False)\n if not override:\n # calculate and compare sha1 of board cfg file with one present in tftp here.\n pass\n\n # TODO: we need to have a common lib which marks services running in each device.\n # this needs to be removed at a later point.\n provisioner.tftp_device = board.tftp_dev\n provisioner.provision_board(board.config)\n\n # This method is old. Added a method on top to calculate sha3.\n @staticmethod\n def validate_modem_cfg_file(board, device):\n \"\"\"\n To check if the cfg file used in modem and wan container are same.\n This method is used to compare the sha on the cfg file used in the modem and the one on wan.\n Parameters: (object)board\n (object)wan\n\n Returns: (bool) True if sha matches else False.\n \"\"\"\n modem_cfg = board.get_modem_cfg_file(\n device.get_interface_ipaddr(device.iface_dut)\n )\n if modem_cfg:\n device.sendline(f\"sha1sum /tftpboot/tmp/{modem_cfg} /tftpboot/{modem_cfg}\")\n device.expect(device.prompt)\n return (\n device.before.split(\"\\n\")[1].split(\" \")[0]\n == device.before.split(\"\\n\")[2].split(\" \")[0]\n )\n else:\n return False\n\n @classmethod\n def copy_cmts_provisioning_files(cls, board_config, tftp_device, board):\n \"\"\"\n This method looks for board's config file in all overlays.\n The file is then encrypted using docsis and pushed to TFTP server.\n\n args:\n board_config (dict): requires tftp_cfg_files key in board config.\n \"\"\"\n # Look in all overlays as well, and PATH as a workaround for standalone\n paths = os.environ[\"PATH\"].split(os.pathsep)\n paths += [\n os.path.dirname(boardfarm.plugins[x].__file__) for x in boardfarm.plugins\n ]\n cfg_list = []\n\n if \"tftp_cfg_files\" in board_config:\n for cfg in board_config[\"tftp_cfg_files\"]:\n if isinstance(cfg, (cm_cfg, mta_cfg)):\n cfg_list.append(cfg)\n else:\n for path in paths:\n cfg_list += glob.glob(path + f\"/devices/cm-cfg/{cfg}\")\n else:\n # TODO: this needs to be removed\n for path in paths:\n cfg_list += glob.glob(path + \"/devices/cm-cfg/UNLIMITCASA.cfg\")\n cfg_set = set(cfg_list)\n\n # Copy binary files to tftp server\n for cfg in cfg_set:\n d = cls(cfg, mibs_paths=board.mibs_path)\n ret = d.encode()\n tftp_device.copy_file_to_server(ret)\n\n def shortname(self, num_digits=None):\n \"\"\"short name for displaying in summary\"\"\"\n h = hashlib.md5(self.txt.encode()).hexdigest()\n if num_digits:\n h = h[0:num_digits]\n return h\n\n\nclass cm_cfg(base_cfg):\n \"\"\"\n Class for generating CM cfg from nothing, or even importing from a file\n They later need to be encoded via a compiler\n \"\"\"\n\n # TODO: all these names will need to be made up once we don't have\n # an input file anymore\n original_fname = None\n original_file = None\n encoded_suffix = \".cfg\"\n encoded_fname = None\n\n # string representation of cm cfg\n # temporary for starting point\n txt = \"\"\n\n # plenty of tests reference a file name, and assume it's in a certain\n # place so let's allow for that for now\n legacy_search_path = None\n\n def __init__(self, start=None, fname=None, cfg_file_str=None, mibs_path=None):\n \"\"\"Creates a default basic CM cfg file for modification\"\"\"\n self.dslite = True\n\n self._start = start\n\n if cfg_file_str:\n # This is a config file in a long string format (multiline string)\n self.load_from_string(cm_str_txt=cfg_file_str)\n elif start is None:\n # create a default config file with bare minimum config,\n # no snmp objs, no CVCs, nothing vendor specific!\n # only CM RF minimal config\n # (i.e.: only the RF side configured, no client side, see Prasada)\n start = CfgGenerator()\n start.gen_dual_stack_cfg()\n self.txt = start.generate_cfg(fname)\n self.original_fname = fname\n self.encoded_fname = self.original_fname.replace(\n \".txt\", self.encoded_suffix\n )\n elif type(start) is str:\n # OLD fashined: this is a file name, load the contents from the file\n self.original_file = start\n self.original_fname = os.path.split(start)[1]\n self.encoded_fname = self.original_fname.replace(\n \".txt\", self.encoded_suffix\n )\n self.load(start)\n elif isinstance(start, CfgGenerator):\n # the dynamic configure class has created this config.... (ok not very OOD to\n # have a class type check in the base class....)\n if fname is None:\n # create a name and add some sha256 digits\n fname = \"cm-config-\" + self.shortname(10) + \".txt\"\n logger.info(f\"Config name created: {fname}\")\n self.txt = (\n start.generate_cfg()\n ) # the derived class already created the skeleton\n if \"DsLite\" in self.txt:\n self.dslite = True\n self.original_fname = fname\n self.encoded_fname = self.original_fname.replace(\n \".txt\", self.encoded_suffix\n )\n else:\n raise Exception(f\"Wrong type {type(start)} received\")\n super().init_copy(file_or_obj=self, mibs_paths=mibs_path)\n\n def encode(self):\n cfg_name = self.file.replace(\".txt\", \".cfg\")\n cfg_path = os.path.join(self.dir_path, cfg_name)\n if os.path.isfile(cfg_path):\n os.remove(cfg_path)\n return self.encode_cm(self.mibs_path_arg, self.file_path, cfg_path)\n\n def load(self, cm_txt):\n \"\"\"Load CM cfg from txt file, for modification\"\"\"\n\n if self.legacy_search_path is not None:\n cm_txt = os.path.join(self.legacy_search_path, cm_txt)\n\n with open(cm_txt) as txt:\n self.txt = txt.read()\n\n def load_from_string(self, cm_str_txt: str, name_prefix: str = \"\") -> None:\n \"\"\"Load CM cfg from text string (e.g. the file is stored in a multiline\n string).\n :parameter cm_str_txt: s string containing the config file\n :type cm_str_txt: string\n :parameter name: name of config file (optional)\n \"\"\"\n self.txt = cm_str_txt\n num = self.shortname(10)\n self.original_file = None\n if name_prefix:\n name_prefix += \"-\"\n else:\n name_prefix = \"cm-config-\"\n self.original_fname = name_prefix + num + \".txt\"\n self.encoded_fname = self.original_fname.replace(\".txt\", self.encoded_suffix)\n\n def __str__(self):\n \"\"\"String repr of CM txt\"\"\"\n return self.txt\n\n def save(self, full_path):\n with open(full_path, \"w\") as txt:\n txt.write(self.txt)\n\n def generic_re_sub(self, regex, sub):\n \"\"\"Crude function to replace strings in configs, should be replaced with subclasses\"\"\"\n saved_txt = self.txt\n\n self.txt = re.sub(regex, sub, self.txt)\n\n if saved_txt == self.txt:\n logger.error(\n f\"WARN: no regex sub was made for {regex}, to be replaced with {sub}\"\n )\n\n def _cm_configmode(self):\n \"\"\"function to check config mode in CM\"\"\"\n \"\"\"0-Disable/Bridge, 1-IPv4, 2-IPv6 (IPv6 | dslite), 3-IPv4 and IPv6(Dual)\"\"\"\n modeset = [\"0x010100\", \"0x010101\", \"0x010102\", \"0x010103\"]\n modestr = [\"disabled\", \"ipv4\", \"ipv6\", \"dual-stack\"]\n for mode in range(len(modeset)):\n tlv_check = \"GenericTLV TlvCode 202 TlvLength 3 TlvValue \" + modeset[mode]\n initmode_check = \"InitializationMode \" + str(mode)\n if (tlv_check in self.txt) or (initmode_check in self.txt):\n return modestr[mode]\n\n cm_configmode = property(_cm_configmode)\n\n\nclass mta_cfg(base_cfg):\n \"\"\"MTA specific class for cfgs\"\"\"\n\n encoded_suffix = \".bin\"\n txt = \"\"\n\n def __init__(self, start=None, fname=None, mta_file_str=None, mibs_path=None):\n \"\"\"\n Creates a default basic mta cfg file for modification\n \"\"\"\n\n if mta_file_str:\n self.load_from_string(mta_str_txt=mta_file_str)\n elif type(start) is str:\n # OLD fashined: this is a file name, load the contents from the file\n self.original_file = start\n self.original_fname = os.path.split(start)[1]\n self.encoded_fname = self.original_fname.replace(\n \".txt\", self.encoded_suffix\n )\n self.load(start)\n elif isinstance(start, CfgGenerator):\n if fname is None:\n # create a name and add some sha256 digits\n fname = \"mta-config-\" + self.shortname(10) + \".txt\"\n self.txt = (\n start.gen_mta_cfg()\n ) # the derived class already created the skeleton\n self.reformatted_txt = self.reformat(self.txt)\n logger.info(f\"Config name created: {fname}\")\n self.original_fname = fname\n self.encoded_fname = self.original_fname.replace(\n \".txt\", self.encoded_suffix\n )\n else:\n raise Exception(f\"Wrong type {type(start)} received\")\n super().init_copy(file_or_obj=self, mibs_paths=mibs_path)\n\n def encode(self):\n cfg_name = self.file.replace(\".txt\", \".bin\")\n cfg_path = os.path.join(self.dir_path, cfg_name)\n if os.path.isfile(cfg_path):\n os.remove(cfg_path)\n return self.encode_mta(self.mibs_path_arg, self.file_path, cfg_path)\n\n def load_from_string(self, mta_str_txt: str, name_prefix: str = \"\") -> None:\n \"\"\"Load CM cfg from text string (e.g. the file is stored in a multiline\n string).\n :parameter cm_str_txt: s string containing the config file\n :type cm_str_txt: string\n :parameter name: name of config file (optional)\n \"\"\"\n self.txt = mta_str_txt\n num = self.shortname(10)\n\n self.original_file = None\n name_prefix = name_prefix or \"mta-config\"\n\n self.reformatted_txt = self.reformat(self.txt)\n\n self.original_fname = f\"{name_prefix}-{num}.txt\"\n self.encoded_fname = self.original_fname.replace(\".txt\", self.encoded_suffix)\n\n def reformat(self, txt: str) -> str:\n \"\"\"In-case the generic MTA config text needs to be reformatted as per\n Vendor Specific requirements.\n\n :returns: string, reformatted text\n \"\"\"\n return txt\n\n def save(self, full_path: str) -> None:\n path = Path(full_path)\n txt = getattr(self, \"reformatted_txt\", \"\") or self.txt\n path.write_text(txt)\n\n\n# -----------------------------------Library Methods-----------------------------------\n\n\ndef check_board(board, cmts, cm_mac):\n assert board.is_online(), \"CM show not OPERATIONAL on console\"\n assert (\n cmts.is_cm_online(ignore_bpi=is_bpi_privacy_disabled(), ignore_partial=True)\n is True\n ), \"CM is not online\" # check cm online on CMTS\n \"\"\"\n Removing this assert for the time being.\n assert (\n sum(cmts.DUT_chnl_lock(cm_mac)) == cmts.channel_bonding\n ), \"CM is in partial service\"\n \"\"\"\n return True\n\n\ndef check_provisioning(board, mta=False):\n \"\"\"This function is used to validate the provisioning using sha3\n\n :param board : board device class to fetch different interfaces\n :type board : boardfarm_docsis.devices.Docsis\n :param mta : to check mta cfg\n :type mta : Boolean\n :return value: out\n :return type: Boolean\n \"\"\"\n\n # few cmts methods needs to be added before comparing sha3\n # TODO: need to do this\n def validate_cm_side():\n pass\n\n def _shortname(cfg):\n d = board.get_docsis(cfg)\n ret = d.encode()\n # Though the method requires an arg pylint throws some false positive error, hence added disable\n return keccak512_checksum(ret) # pylint: disable=E1121\n\n try:\n sha3_on_board = board.cfg_sha3()\n sha3_on_fw = _shortname(board.cm_cfg)\n logger.debug(sha3_on_board)\n logger.debug(sha3_on_fw)\n out = [sha3_on_board == sha3_on_fw]\n if mta:\n sha3_on_board = board.cfg_sha3(mta)\n sha3_on_fw = _shortname(board.mta_cfg)\n logger.debug(sha3_on_board)\n logger.debug(sha3_on_fw)\n out.append(sha3_on_board == sha3_on_fw)\n return all(out)\n except BftCommandNotFound:\n logger.error(\"NOTE: Ignoring provisioning check: sha3Sum command not found\")\n return True\n\n\ndef check_interface(board, ip, prov_mode=\"dual\", lan_devices=None):\n \"\"\"This function is used to validate IP addresses for CPEs\n\n Possible provisioning modes [\"none\",\"bridge\", \"ipv4\", \"dslite\", \"dual\"]\n Based on these modes validate IP v4/v6 address on e-router iface on board\n Based on these modes validate IP v4/v6 address of CPEs connected to board\n\n :param board : board device class to fetch different interfaces\n :type board : boardfarm_docsis.devices.Docsis\n :param ip : a dictionary of IP address for all devices calculated by a test\n :type ip : dict\n :param prov_mode : prov_mode against which CPEs are validated\n :type prov_mode : str\n :param lan_devices : list of CPEs connected to board\n :type lan_device : list\n\n :raises CodeError : if the IP addresses are not validated as per prov_mode\n \"\"\"\n if lan_devices is None:\n lan_devices = [\"lan\"]\n\n # This is only for erouter and CPE interfaces check.\n def _validate_ertr(iface, mode):\n \"\"\"This function validates if e-router needs to be considered for assertion\n\n If the prov_mode is none or bridge, do not expect an entry for erouter\n Else expect an entry for erouter\n This function called internally by check_interface\n\n :param iface : v6 and v4 details of a board's e-router iface\n :type iface : dict\n :param mode : can be IPv4 or IPv6\n :type mode : str\n\n :raises CodeError : if the IP addresses are not validated as per prov_mode\n \"\"\"\n version = {\"ipv4\": [\"ipv4\", \"dual\"], \"ipv6\": [\"dslite\", \"ipv6\", \"dual\"]}\n\n def check(x):\n if prov_mode in version[mode.lower()]:\n return x\n else:\n return not x\n\n assert check(\n iface.get(mode.lower(), None)\n ), f\"Failed to fetch E-Router {mode}, mode: {prov_mode}\"\n\n def _validate_cpe(mode):\n \"\"\"This function validates v4/v6 ip-addresses of CPEs based on prov_mode\n\n This function is called internally by check_interface\n\n :param mode : can be IPv4 or IPv6\n :type mode : str\n\n :raises CodeError : if the IP addresses are not validated as per prov_mode\n \"\"\"\n prov_info = [\n prov for prov in board.config[\"devices\"] if \"provisioner\" == prov[\"name\"]\n ]\n for dev in lan_devices:\n if prov_mode == \"disabled\" and mode.lower() == \"ipv4\":\n if ipaddress.ip_address(\n ip[dev].get(mode.lower())\n ) in ipaddress.ip_network(prov_info[0][\"open_network\"]):\n pass\n else:\n raise IpAddrMismatch(\n f\"LAN IP {ip[dev].get(mode.lower())} is not in public IP subnet\"\n )\n else:\n assert ip[dev].get(\n mode.lower(), None\n ), f\"Failed to fetch {dev} {mode}, mode: {prov_mode}\"\n\n # Validate IPv4 conditions\n _validate_ertr(ip[\"board\"][board.erouter_iface], \"IPv4\")\n _validate_cpe(\"IPv4\")\n\n # validate IPv6 conditions\n _validate_ertr(ip[\"board\"][board.erouter_iface], \"IPv6\")\n\n # since aftr iface does not have an IP address/mac address of it's own\n # just validate if the interface exists\n if prov_mode == \"ipv6\":\n # OFW-1150 - DSLite service taking up to 14 minutes to come up\n def _check_interface_exists(board, prov_mode):\n assert board.check_iface_exists(\n board.aftr_iface\n ), f\"{board.aftr_iface} interface didn't come up in prov mode : {prov_mode}\"\n\n retry_on_exception(\n _check_interface_exists, (board, prov_mode), retries=30, tout=30\n )\n if prov_mode != \"ipv4\":\n _validate_cpe(\"IPv6\") # validate ipv6 for CPEs\n\n\ndef generate_cfg_file(board, test_args, cfg_mode, filename=None, cfg_args=None):\n if not filename:\n filename = cfg_mode + \"_config.txt\"\n\n if cfg_args:\n extra_snmp_default_mibs = []\n for dict_name in cfg_args:\n if dict_name in board.cm_cfg.mib_list:\n extra_snmp_default_mibs += eval(\"board.cm_cfg.\" + dict_name)\n test_args[\"extra_snmp\"] = extra_snmp_default_mibs\n\n return board.generate_cfg(cfg_mode, fname=filename, kwargs=test_args)\n\n\ndef configure_board_v2(provisioner, board, test_args, test_data, **kwargs):\n prov_mode = getattr(test_data, \"prov_mode\", None)\n filename = getattr(test_data, \"filename\", None)\n cfg_args = getattr(test_data, \"cfg_args\", None)\n\n cm_cfg = kwargs.pop(\"cm_cfg\", None)\n mta_cfg = kwargs.pop(\"mta_cfg\", None)\n\n if not cm_cfg:\n cm_cfg = generate_cfg_file(board, test_args, prov_mode, filename, cfg_args)\n board.update_docsis_config(cm_cfg=cm_cfg, mta_cfg=mta_cfg, **kwargs)\n provisioner.tftp_device = board.tftp_dev\n provisioner.provision_board(board.config)\n\n\ndef check_cm_firmware_version(board, wan, env_helper):\n \"\"\"Compare CM firmware version with provided enviornment FM version\n checking all images ending with suffix .*\n eg CH7465LG-NCIP-6.12.18.26-3-GA-SH.p7\n\n :param board : board DUT device class\n :type board : device_type.DUT\n :param wan : wan is wan device type\n :type wan : device_type.wan\n :param env_helper : device class to fetch different devices\n :type env_helper : boardfarm_docsis.devices.Docsis\n :rtype: Bool\n :raise Assertion: Asserts when CM FM Mismatch or exception\n :return: returns bool True if FM Matches\n \"\"\"\n if env_helper.has_image():\n fm_ver = env_helper.get_image(mirror=False).rpartition(\".\")[0]\n\n if hasattr(board, \"check_fw_version\"):\n assert board.check_fw_version(fm_ver)\n return True # is this needed?\n\n # TODO: remove the following code once clean arch is used\n cm_ip = board.get_interface_ipaddr(board.wan_iface)\n # Though the right arguments passed to the method and the method has return pylint throws some false positive error, hence added disable\n result = retry_on_exception( # pylint: disable=E1111, E1121\n SnmpHelper.snmp_v2, [wan, cm_ip, \"docsDevSwCurrentVers\"], 2\n )\n # temporary fix, needs rework to being vendor independent\n assert (\n result in fm_ver\n ), f\"CM FM Version Mismatch current {result} not in requested {fm_ver}\"\n\n return True\n\n\ndef factoryreset(s, board, method=\"SNMP\"):\n \"\"\"Reset board to Factory Default\n\n :param s : object with log_to_file attribute for logging\n :type s : TestStep Obj with attribute log_to_file\n :param board : board DUT device class\n :type board : device_type.DUT\n :param method : (\"SNMP\", \"ACS\", \"CONSOLE\") Default to \"SNMP\"\n :type method : String value with values(\"SNMP\",\"ACS\",\"CONSOLE\")\n :rtype: Bool\n :raise Assertion: Asserts when FactoryReset failed or arg error\n :return: returns bool True if FactoryReset successful\n \"\"\"\n logger.debug(f\"=======Begin FactoryReset via {method}=======\")\n\n try:\n wan = board.dev.by_type(device_type.wan)\n wan_ip = board.get_interface_ipaddr(board.wan_iface)\n\n if method == \"SNMP\":\n if not hasattr(s, \"cm_wan_ip\"):\n s.cm_wan_ip = wan_ip\n\n # TODO Need to remove dependency on self\n board.reset_defaults_via_snmp(s, wan)\n board.reboot_modem_os_via_snmp(s, wan)\n\n elif method == \"ACS\":\n try:\n board.dev.acs_server.FactoryReset()\n except Exception as e:\n logger.error(\n \"Failed: FactoryReset through ACS '{}'\"\n \"\\n Restarting tr069 and retry Factory Reset again..\".format(str(e))\n )\n board.restart_tr069(wan, wan_ip)\n board.dev.acs_server.FactoryReset()\n\n elif method == \"CONSOLE\":\n if board.env_helper.has_image():\n cm_fm = board.env_helper.get_image(mirror=False)\n if \"nosh\" in cm_fm.lower():\n logger.error(\n \"Failed FactoryReset via CONSOLE on NOSH Image is not possible\"\n )\n raise CodeError(\n \"Failed FactoryReset via CONSOLE on NOSH Image is not possible\"\n )\n\n # TODO Need to remove dependency on self\n board.reset_defaults_via_os(s)\n\n else:\n raise Exception(\n \"\"\"WrongValue: Pass any value ['SNMP', 'ACS', 'CONSOLE'] for method arg\"\"\"\n )\n # Verify CM status and board ip address\n assert \"INTEGER: 12\" == board.get_cmStatus(\n wan, wan_ip, \"INTEGER: 12\"\n ), \"board cmstatus is down\"\n\n board.check_valid_docsis_ip_networking()\n\n logger.debug(f\"=======End FactoryReset via {method}=======\")\n return True\n\n except Exception as e:\n logger.error(f\"Failed Board FactoryReset using '{method}' \\n {str(e)}\")\n raise BootFail(f\"Failed Board FactoryReset: {str(e)}\")\n\n\ndef configure_cm_dhcp_server(board, mode=\"dual\", enable=True):\n \"\"\"Enable/disable board dhcp 4/6 server\n\n :param board : board DUT device class\n :type board : device_type.DUT\n :param mode : enable/disable board dhcp ipv4/ipv6/dual\n [\"dual\" = ipv4 & ipv6,\n \"ipv4\" = only ipv4,\n \"ipv6\" = only ipv6\n ]\n :type mode : string\n :param enable : enable/disable cm dhcp server\n :type enable : boolean\n :rtype: boolean\n :raise Assertion: Asserts when ACS RPC raise exception\n :return: returns bool True if enable/disable ACS RPC successful\n returns bool False if requested operation is already running\n \"\"\"\n if not board.get_cpeid():\n board.restart_tr069(board.dev.wan, board.get_interface_ipaddr(board.wan_iface))\n r_status = False\n\n if mode in [\"dual\", \"ipv4\"] and (\n enable != board.dev.acs_server.GPV(\"Device.DHCPv4.Server.Enable\")[0][\"value\"]\n ):\n r_status = (\n board.dev.acs_server.SPV({\"Device.DHCPv4.Server.Enable\": enable}) == 0\n )\n\n if mode in [\"dual\", \"ipv6\"] and (\n enable != board.dev.acs_server.GPV(\"Device.DHCPv6.Server.Enable\")[0][\"value\"]\n ):\n r_status = (\n board.dev.acs_server.SPV({\"Device.DHCPv6.Server.Enable\": enable}) == 0\n )\n\n return r_status\n\n\nclass docsis(base_cfg):\n \"\"\"\n Deprecated use base_cfg, eventually this will be removed.\n \"\"\"\n\n def __init__(self, *args, **kw):\n deprecate(\n \"Warning!\",\n message=\"Use base_cfg to encode/validate the Docsis related files\",\n category=UserWarning,\n )\n super().__init__(*args, **kw)\n\n\ndef reprovision_board(device_mgr, boot_file_txt=None, mta_file_txt=None):\n \"\"\"Full reprovisioning of the board with the given boot file passed as a string.\n The board is then rebooted and expected to come online (on the CMTS side).\n\n :param device_mgr: the device manager\n :type device_mgr: object\n :param boot_file_txt: a string containing the boot file (not the file name)\n :type boot_file_txt: string (multiline)\n :param mta_file_txt: a string containing the MTA boot file (not the file name)\n :type mta_file_txt: string (multiline)\n \"\"\"\n if boot_file_txt:\n device_mgr.board.cm_cfg.load_from_string(boot_file_txt)\n if mta_file_txt:\n device_mgr.board.mta_cfg.load_from_string(mta_file_txt)\n device_mgr.board.reprovision(device_mgr.provisioner)\n device_mgr.board.reset()\n device_mgr.cmts.clear_cm_reset(device_mgr.board.cm_mac)\n device_mgr.cmts.wait_for_cm_online(ignore_partial=True, iterations=50)\n","sub_path":"boardfarm_docsis/lib/docsis.py","file_name":"docsis.py","file_ext":"py","file_size_in_byte":31806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"561755988","text":"##Este archivo funciona como esquema para guardar datos en la base de datos a traves de POST\n##Basado en este archivo debe crearse una rutina en la rasperri\n##\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tTO DO: rutina en la rasperri (comunicaciones), se debe procesar la informacion antes de enviarla al servidor\n\nimport requests\t#http para humanos\nimport time\nimport random\nimport datetime\n\n\nip='robotgranjeroe3t.ueuo.com'\n##ip='10.14.52.135'\n# Valores aleatorios para llenar la base de datos, solo para pruebas\ni=1\nx=random.uniform(1,255)\nx2=random.uniform(1,255)\ny=random.randint(1,10)\nts=time.time()\nst = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\t#convierte el tiempo es un valor legible para humanos y con el formato de la base de datos\np = 1 ## numero de la planta\nprint(str(ts))\n\t##convencion\n\t\t##time tiempo formato texto '%Y-%m-%d %H:%M:%S'\n\t\t#temp valor del temp float\n #temp_amb valor del temp float\n\t\t#humidity valor humedad float\n #humidity_amb valor humedad ambiente float\n\t\t#grow valor de la cresimiento int\n #mellowing valor de la maduracion int \t\t\t\n\t\t#plant numero de la planta en la parcela int\n #img nombre d ela imagen de la imagen texto 'tom1.jpg'\nr = requests.post('http://'+ip+'/web/php/iot.php',data = {'time':st,'temp':round(x,2),'temp_amb':round(x,2),'humidity':round(x,2),'humidity_amb':round(x2,2),'grow':round(22),'mellowing':round(23),'photo':str(i)+'.jpg','plant':p})\nprint(r.status_code, r.reason)\t#revisa el estado de la transmision\ntime.sleep(1) #espera 1 segundo\n","sub_path":"date2f.py","file_name":"date2f.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"78675444","text":"from flask_inputs import Inputs\nfrom flask_inputs.validators import JsonSchema\nimport time\n\nfrom google.cloud import pubsub_v1\n\n# https://pythonhosted.org/Flask-Inputs/#module-flask_inputs\n# https://json-schema.org/understanding-json-schema/\n# noinspection SpellCheckingInspection\nmessage_payload = {\n \"type\": \"object\",\n \"properties\": {\n \"activities\": {\n \"type\": \"array\",\n \"items\": {\"$ref\": \"#/$defs/operations\"},\n \"minItems\": 1\n }\n },\n \"$defs\": {\n \"operations\": {\n \"type\": \"object\",\n \"required\": [\"operation\", \"table\", \"col_names\", \"col_types\", \"col_values\"],\n \"properties\": {\n \"operation\": {\"anyOf\": [{\"const\": \"insert\"},\n {\"const\": \"delete\"}]},\n \"table\": {\"type\": \"string\"},\n \"col_names\": {\n \"type\": \"array\",\n \"items\": {\"type\": \"string\"}\n },\n \"col_types\": {\"type\": \"array\",\n \"items\": {\"enum\": [ \"string\", \"bytes\", \"integer\",\n \"float\", \"numeric\", \"bignumeric\",\n \"boolean\", \"timestamp\", \"date\", \"time\",\n \"datetime\", \"geography\", \"record\"]}},\n \"col_values\": {\n \"type\": \"array\"\n }\n }\n }\n }\n}\n\nclass ActivitiesInput(Inputs):\n json = [JsonSchema(schema=message_payload)]\n\ninvalid_message = []\n\nproject_id = \"blank-space-312006\"\ntopic_id = \"api-gatekeeper\"\npublisher = pubsub_v1.PublisherClient()\ntopic_path = publisher.topic_path(project_id, topic_id)\n\ndef validate_message(request):\n inputs = ActivitiesInput(request)\n if inputs.validate():\n # push to pubsub cluster\n future = publisher.publish(topic_path, str(request.data).encode(\"utf-8\"))\n return print(future.result())\n else:\n invalid_message.append(inputs.errors[0])\n return f\"Invalid Message: {invalid_message}\"","sub_path":"app/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"274376960","text":"import logging\nimport threading\nimport time\n\nfrom chatrobot import DingtalkChatbot\nfrom huobi_swap_client import Huobi_Swap_Client\n\nlogger = logging.getLogger('root')\n\nHuobi_Access_Key = ''\nHuobi_Secret_Key = ''\n\n# dingding address info\ndingding_address = ''\n\nis_proxies = False\n\ncontract_code = 'BTC-USDT'\n\n# contract info\ncontract_decimal = 1\n\nclass MACD_strategy():\n\n def __init__(self):\n # strategy info\n self.strategy_account = 'test account'\n self.strategy_name = 'MACD strategy version 1'\n\n # dingding info\n self.xiaoding = DingtalkChatbot(dingding_address)\n\n # Swap client instance\n self.huobi_swap_client = Huobi_Swap_Client(Access_Key=Huobi_Access_Key, Secret_Key=Huobi_Secret_Key,\n is_proxies=is_proxies)\n\n # current account info\n self.max_leverage_rate = 10\n self.start_margin_balance = 0\n self.current_margin_balance = 0 # 账户权益\n self.current_margin_available = 0 # 可用保证金\n self.current_margin_frozen = 0 # 冻结保证金\n self.current_liquidation_price = 0 # 预估强平价格\n self.current_lever_rate = 0 # 杠杆倍数\n\n # current position info\n # buy position\n self.current_buy_volume = 0 # 当前多头总仓位\n self.current_buy_volume_available = 0 # 当前多头可用仓位\n self.current_buy_volume_frozen = 0 # 当前多头冻结仓位\n self.current_buy_cost_open = 0 # 多头开仓均价\n self.current_buy_position_margin = 0 # 多头持仓保证金\n # sell position\n self.current_sell_volume = 0 # 当前空头总仓位\n self.current_sell_volume_available = 0 # 当前空头可用仓位\n self.current_sell_volume_frozen = 0 # 当前空头冻结仓位\n self.current_sell_cost_open = 0 # 空头开仓均价\n self.current_sell_position_margin = 0 # 空头持仓保证金\n\n # quant model params\n self.period_short = 12\n self.period_long = 26\n self.period_dea = 9\n self.k_lines_count = 700\n\n # trade params\n self.trade_leverage = 1.5\n self.max_leverage = 10\n self.backup_stop_order_percent = 1.02\n\n self.trade_signal = 0\n self.trade_state = 'IDLE'\n\n self.trade_amount = 0\n\n self.current_working_day = None\n\n # market infomation\n self.current_market_price = float(self.huobi_swap_client.get_market_trade(contract_code=contract_code)['tick']['data'][0]['price'])\n\n '''quant model'''\n def get_MACD(self):\n k_lines = \\\n self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=self.k_lines_count)['data']\n while len(k_lines) != self.k_lines_count:\n time.sleep(1)\n k_lines = \\\n self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=self.k_lines_count)[\n 'data']\n ema_1 = []\n ema_2 = []\n dea = []\n macd = 0\n # bar = 0\n ema_1.append(k_lines[0]['close'])\n ema_2.append(k_lines[0]['close'])\n diff = ema_1[-1] - ema_2[-1]\n dea.append(diff)\n for i in range(1, self.k_lines_count):\n ema_1.append(ema_1[-1] * ((self.period_short - 1) / (self.period_short + 1)) + k_lines[i]['close'] * (\n 2 / (self.period_short + 1)))\n ema_2.append(ema_2[-1] * ((self.period_long - 1) / (self.period_long + 1)) + k_lines[i]['close'] * (\n 2 / (self.period_long + 1)))\n diff = ema_1[-1] - ema_2[-1]\n dea.append(dea[-1] * ((self.period_dea - 1) / (self.period_dea + 1)) + diff * (2 / (self.period_dea + 1)))\n macd = diff - dea[-1]\n bar = 2 * (diff - dea[-1])\n if diff > 0 and macd > 0:\n self.trade_signal = 1\n elif diff < 0 and macd < 0:\n self.trade_signal = -1\n else:\n self.trade_signal = 0\n message = 'macd is %s, diff is %s, Trade signal is %s.' % (macd, diff, self.trade_signal)\n self.dingding_notice(message)\n\n\n '''trade logic'''\n def in_idle(self):\n message = 'Status updated, Current status: IDLE'\n self.dingding_notice(message)\n logger.info(message)\n self.cancel_order_all()\n while True:\n self.check_position()\n if self.trade_state != 'IDLE':\n return\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n # print(new_klines)\n while len(new_klines) != 3:\n time.sleep(1)\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n if self.current_working_day != new_klines[-1]['id']:\n logger.info(\"current working hour: %s\" % self.current_working_day)\n logger.info('new k_line hour: %s' % new_klines[-1]['id'])\n self.get_MACD()\n self.get_trade_amount()\n if self.trade_signal > 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,\n volume=self.trade_amount,\n direction='buy',\n offset='open',\n lever_rate = 10,\n order_price_type='optimal_20')\n message = 'buy order, buy amount is %s' %(self.trade_amount)\n elif self.trade_signal < 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,\n volume=self.trade_amount,\n direction='sell',\n offset='open',\n lever_rate = 10,\n order_price_type='optimal_20')\n message = 'sell order, sell amount is %s' %(self.trade_amount)\n else:\n message = 'macd is 0'\n self.dingding_notice(message=message)\n time.sleep(15)\n self.current_working_day = new_klines[-1]['id']\n\n def in_long_position(self):\n self.cancel_order_all()\n time.sleep(1)\n self.check_position()\n message = 'Status updated, Current status: Long. Current long position: %s, Avg Price: %s' \\\n %(self.current_buy_volume, self.current_buy_cost_open)\n self.dingding_notice(message)\n\n stopPx = self.current_buy_cost_open / self.backup_stop_order_percent # in long position, stop loss\n self.get_current_account_position_info()\n time.sleep(1)\n # print(contract_code,'sell',int(self.current_buy_volume),self.format_price(stopPx),'optimal_20')\n stop_order = self.huobi_swap_client.create_tpsl_order(contract_code=contract_code,\n direction='sell',\n volume=int(self.current_buy_volume),\n sl_trigger_price= self.format_price(stopPx),\n sl_order_price_type='optimal_20'\n )\n time.sleep(1)\n message = 'Back-up stop orders settle, direction is sell, stop price is: %s.' % (self.format_price(stopPx))\n self.dingding_notice(message)\n\n while True:\n time.sleep(15)\n self.check_position()\n if self.trade_state != 'Long':\n return\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n while len(new_klines) != 3:\n time.sleep(1)\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n\n if self.current_working_day != new_klines[-1]['id']:\n logger.info(\"current working hour: %s\" % self.current_working_day)\n logger.info('new k_line hour: %s' % new_klines[-1]['id'])\n self.get_MACD()\n self.get_trade_amount()\n if self.trade_signal < 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,\n volume=int(self.current_buy_volume + self.trade_amount),\n direction='sell',\n offset='open',\n lever_rate = 10,\n order_price_type='optimal_20')\n\n message = 'direction: -> short. Current long position is %s, trade amount is %s' %(\n self.current_buy_volume, self.trade_amount\n )\n self.dingding_notice(message)\n self.current_working_day = new_klines[-1]['id']\n\n def in_short_position(self):\n self.cancel_order_all()\n time.sleep(1)\n self.check_position()\n message = 'Status updated, Current status: Short. Current short position: %s, Avg Price: %s' \\\n % (self.current_sell_volume, self.current_sell_cost_open)\n self.dingding_notice(message)\n\n stopPx = self.current_sell_cost_open * self.backup_stop_order_percent\n self.huobi_swap_client.create_tpsl_order(contract_code=contract_code,\n direction='buy',\n volume=int(self.current_sell_volume),\n sl_trigger_price= self.format_price(stopPx),\n sl_order_price_type='optimal_20'\n )\n\n time.sleep(1)\n message = 'Back-up stop orders settle, direction is buy, stop price is: %s.' % (self.format_price(stopPx))\n self.dingding_notice(message)\n\n while True:\n time.sleep(15)\n self.check_position()\n if self.trade_state != 'Short':\n return\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n while len(new_klines) != 3:\n time.sleep(1)\n new_klines = self.huobi_swap_client.get_k_lines(contract_code=contract_code, period='60min', size=3)['data']\n if self.current_working_day != new_klines[-1]['id']:\n logger.info(\"current working hour: %s\" % self.current_working_day)\n logger.info('new k_line hour: %s' % new_klines[-1]['id'])\n self.get_MACD()\n self.get_trade_amount()\n if self.trade_signal > 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,\n volume=int(self.current_sell_volume + self.trade_amount),\n direction='buy',\n offset='open',\n lever_rate = 10,\n order_price_type='optimal_20')\n message = 'direction: -> Long. Current short position is %s, trade amount is %s' %(\n self.current_sell_volume, self.trade_amount\n )\n self.dingding_notice(message)\n self.current_working_day = new_klines[-1]['id']\n\n def trade(self):\n self.get_trade_amount()\n time.sleep(1)\n self.get_current_account_position_info()\n self.start_margin_balance = self.current_margin_balance\n message = 'System initialization completed, beginning balance: %s \\n' \\\n 'Trading %s with basic trading amount %s.' \\\n %(self.start_margin_balance,contract_code, self.trade_amount)\n self.dingding_notice(message)\n last_balance = self.start_margin_balance\n\n while True:\n time.sleep(1)\n self.cancel_order_all()\n if self.current_lever_rate <= self.max_leverage_rate:\n if self.trade_state == 'IDLE':\n self.check_position()\n message = 'Single circulation completed, now balance: %s \\n' \\\n 'Profit in this circulation : %s, Profit from initialization :%s' \\\n %(self.current_margin_balance,\n self.current_margin_balance-last_balance,\n self.current_margin_balance-self.start_margin_balance)\n self.dingding_notice(message)\n\n last_balance = self.current_margin_balance\n self.in_idle()\n continue\n elif self.trade_state == 'Short':\n self.in_short_position()\n continue\n elif self.trade_state == 'Long':\n self.in_long_position()\n continue\n elif self.current_lever_rate > self.max_leverage_rate:\n message = 'Current leverage exceed maximum working leverage, initialization failed, please check'\n self.dingding_notice(message)\n if self.trade_state == 'IDLE':\n self.in_idle()\n continue\n elif self.trade_state == 'Short':\n self.in_short_position()\n continue\n elif self.trade_state == 'Long':\n self.in_long_position()\n continue\n time.sleep(15)\n\n '''account info'''\n def get_current_account_position_info(self):\n # current account info\n self.current_margin_balance = 0 # 账户权益\n self.current_margin_available = 0 # 可用保证金\n self.current_margin_frozen = 0 # 冻结保证金\n self.current_liquidation_price = 0 # 预估强平价格\n self.current_lever_rate = 0 # 杠杆倍数\n # current position info\n # buy position\n self.current_buy_volume = 0 # 当前多头总仓位\n self.current_buy_volume_available = 0 # 当前多头可用仓位\n self.current_buy_volume_frozen = 0 # 当前多头冻结仓位\n self.current_buy_cost_open = 0 # 多头开仓均价\n self.current_buy_position_margin = 0 # 多头持仓保证金\n # sell position\n self.current_sell_volume = 0 # 当前空头总仓位\n self.current_sell_volume_available = 0 # 当前空头可用仓位\n self.current_sell_volume_frozen = 0 # 当前空头冻结仓位\n self.current_sell_cost_open = 0 # 空头开仓均价\n self.current_sell_position_margin = 0 # 空头持仓保证金\n current_position = self.huobi_swap_client.get_swap_account_position_info(contract_code=contract_code)\n\n if current_position['status'] == 'ok':\n if len(current_position['data']) > 0:\n self.current_margin_balance = current_position['data'][0]['margin_balance'] # 账户权益\n self.current_margin_available = current_position['data'][0]['margin_available'] # 可用保证金\n self.current_margin_frozen = current_position['data'][0]['margin_frozen'] # 冻结保证金\n self.current_liquidation_price = current_position['data'][0]['liquidation_price'] # 预估强平价格\n self.current_lever_rate = current_position['data'][0]['lever_rate'] # 杠杆倍数\n message = 'Account info: \\n' \\\n 'margin balance is %s, margin available is %s, margin frozen is %s, liquidation price is ' \\\n '%s, lever rate is %s.\\n Current time: %s.\\n' \\\n % (self.current_margin_balance, self.current_margin_available,\n self.current_margin_frozen, self.current_liquidation_price, self.current_lever_rate,\n time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n # print(message)\n if len(current_position['data'][0]['positions']) > 0:\n for i in current_position['data'][0]['positions']:\n if i['direction'] == 'buy':\n self.current_buy_volume = i['volume'] # 当前多头总仓位\n self.current_buy_volume_available = i['available'] # 当前多头可用仓位\n self.current_buy_volume_frozen = i['frozen'] # 当前多头冻结仓位\n self.current_buy_cost_open = i['cost_open'] # 多头开仓均价\n self.current_buy_position_margin = i['position_margin'] # 多头持仓保证金\n if i['direction'] == 'sell':\n self.current_sell_volume = i['volume'] # 当前空头总仓位\n self.current_sell_volume_available = i['available'] # 当前空头可用仓位\n self.current_sell_volume_frozen = i['frozen'] # 当前空头冻结仓位\n self.current_sell_cost_open = i['cost_open'] # 空头开仓均价\n self.current_sell_position_margin = i['position_margin'] # 空头持仓保证金\n\n message = 'Buy position: \\n' \\\n 'buy volume is %s, buy volume available is %s, buy volume frozen is: %s, \\n' \\\n 'buy cost open is %s, buy position margin is %s. \\n \\n' \\\n 'Sell position: \\n' \\\n 'sell volume is %s, sell volume available is %s, sell volume frozen is: %s, \\n' \\\n 'sell cost open is %s, sell position margin is %s.\\n' \\\n 'Current time: %s.' \\\n % (self.current_buy_volume, self.current_buy_volume_available,\n self.current_buy_volume_frozen,\n self.current_buy_cost_open, self.current_buy_position_margin,\n self.current_sell_volume, self.current_sell_volume_available,\n self.current_sell_volume_frozen,\n self.current_sell_cost_open, self.current_sell_position_margin,\n time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n # print(message)\n else:\n message = 'No position info'\n # print(message)\n # logger.info(message)\n else:\n message = 'Cannot get current account and position infomation. (situation 2)'\n print(message)\n # logger.info(message)\n else:\n message = 'Cannot get current account and position infomation. (situation 1)'\n print(message)\n # logger.info(message)\n\n def check_position(self):\n self.get_current_account_position_info()\n time.sleep(1.5)\n if self.current_buy_volume == self.current_sell_volume:\n self.trade_state = 'IDLE'\n if self.current_buy_volume > 0 and self.current_sell_volume > 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_buy_volume),\n direction='sell',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_sell_volume),\n direction='buy',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n message = 'trade state is idle, but long and short position is not zero,'\\\n 'long position is %s, short position is %s' %(self.current_buy_volume,self.current_sell_volume)\n self.dingding_notice(message)\n else:\n pass\n\n elif self.current_buy_volume > self.current_sell_volume:\n self.trade_state = 'Long'\n if self.current_sell_volume > 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_sell_volume),\n direction='sell',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_sell_volume),\n direction='buy',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n message = 'trade state is long, but short position is not zero,'\\\n 'long position is %s, short position is %s' % (self.current_buy_volume,self.current_sell_volume)\n self.dingding_notice(message)\n else:\n pass\n else:\n self.trade_state = 'Short'\n if self.current_buy_volume > 0:\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_buy_volume),\n direction='sell',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n self.huobi_swap_client.create_order(contract_code=contract_code,volume=int(self.current_buy_volume),\n direction='buy',offset='close',lever_rate=10,\n order_price_type='optimal_5')\n time.sleep(1)\n message = 'trade state is short, but long position is not zero,'\\\n 'long position is %s, short position is %s' % (self.current_buy_volume,self.current_sell_volume)\n self.dingding_notice(message)\n else:\n pass\n # print('Trade status is: ',self.trade_state)\n\n def get_trade_amount(self):\n market_price_thread = threading.Thread(target=self.get_current_price)\n market_price_thread.start()\n while market_price_thread.is_alive() is True:\n time.sleep(0.2)\n # print('Current market price is: ',self.current_market_price)\n\n get_account_thread = threading.Thread(target=self.get_current_account_position_info)\n get_account_thread.start()\n while get_account_thread.is_alive() is True:\n time.sleep(0.2)\n # print('Current margin balance is ', self.current_margin_balance)\n\n self.trade_amount = int(self.current_margin_balance / (self.current_market_price * 0.001) * self.trade_leverage)\n print('trade amount is: ', self.trade_amount)\n\n '''market info'''\n def get_current_price(self):\n self.current_market_price = float(self.huobi_swap_client.get_market_trade(contract_code=contract_code)['tick']['data'][0]['price'])\n\n '''tools'''\n # dingding post\n def ding_thread(self, out):\n self.xiaoding.send_text(out, is_at_all=False)\n\n def dingding_notice(self, message=None):\n self.get_current_account_position_info()\n basic_info = '\\n--------------------------------\\n' \\\n 'Strategy name: %s \\n' \\\n 'Contract code: %s \\n' \\\n 'Current long position: %s \\n' \\\n 'Current short position: %s \\n' \\\n 'Local time: %s \\n ' \\\n '--------------------------------\\n' \\\n % (self.strategy_name,\n contract_code,\n self.current_buy_volume,\n self.current_sell_volume,\n time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n out = message + basic_info\n t = threading.Thread(target=self.ding_thread, args=(out,), )\n t.start()\n\n def format_price(self, num):\n price = round(num, contract_decimal)\n return price\n\n def cancel_order_all(self):\n self.huobi_swap_client.cancel_order_by_symbol(contract_code=contract_code)\n self.huobi_swap_client.cancel_tpsl_order_all(contract_code=contract_code)\n\ntest = MACD_strategy()\ntest.trade()\n\n# test.check_position()\n# print(test.huobi_swap_client.get_swap_account_position_info(contract_code=contract_code))\n# test.get_current_account_position_info()\n# test.xiaoding.send_text('strategy test message',is_at_all=False)\n# test.dingding_notice('test')\n# test.get_MACD()\n# test.in_idle()\n# aa = test.huobi_swap_client.cancel_order_by_symbol(contract_code=contract_code)\n# print(aa)\n\n# aa = test.huobi_swap_client.cancel_tpsl_order_all(contract_code=contract_code)\n# print(aa)\n# test.check_position()\n\n# test.get_trade_amount()\n\n# test.in_idle()\n\n# aa = test.huobi_swap_client.create_tpsl_order(contract_code=contract_code,direction='sell',volume=1,\n# tp_trigger_price=34500,tp_order_price_type='optimal_5')\n# print(aa)\n# aa = test.huobi_swap_client.create_tpsl_order(contract_code=contract_code,direction='sell',volume=2,\n# sl_trigger_price=36023.5,sl_order_price_type='optimal_20')\n# print(aa)\n\n# print(test.huobi_swap_client.cancel_tpsl_order(contract_code=contract_code,order_id=805871093956476929))","sub_path":"huobi_macd_v1.py","file_name":"huobi_macd_v1.py","file_ext":"py","file_size_in_byte":26489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"650884521","text":"# GetOldTweets3 ===> 2021/04/04 막힌듯함.. \r\n\r\nimport time\r\nimport datetime\r\nimport GetOldTweets3 as got\r\nimport logging\r\nimport logging.handlers\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nfrom multiprocessing import Pool\r\nimport pandas as pd\r\nimport os\r\n\r\n# 트윗 수집하는 함수 정의\r\n# def get_tweets(start_date, end_date, keyword, keyword2):\r\ndef get_tweets(start_date, end_date, keyword):\r\n \r\n # 범위 끝을 포함하게 만듬\r\n end_date = (datetime.datetime.strptime(end_date, \"%Y-%m-%d\") \r\n + datetime.timedelta(days=1)).strftime(\"%Y-%m-%d\")\r\n \r\n # 트윗 수집 기준 설정\r\n# tweetCriteria = got.manager.TweetCriteria().setQuerySearch('{}'.format(keyword,keyword2))\\\r\n tweetCriteria = got.manager.TweetCriteria().setQuerySearch('{}'.format(keyword))\\\r\n .setSince(start_date)\\\r\n .setUntil(end_date)\\\r\n .setMaxTweets(-1) # 모두 수집\r\n print(got.manager.TweetManager)\r\n print(\"==> Collecting data start..\")\r\n start_time = time.time()\r\n tweets = got.manager.TweetManager.getTweets(tweetCriteria)\r\n print(\"==> Collecting data end.. {0:0.2f} minutes\".format((time.time() - start_time)/60))\r\n print(\"=== Total number of tweets is {} ===\".format(len(tweets)))\r\n \r\n return tweets\r\n \r\n \r\n# 유저 리스트 반환하는 함수 정의\r\ndef get_users(tweets):\r\n \r\n user_list = []\r\n tweet_list = []\r\n\r\n for index in tweets:\r\n username = index.username\r\n content = index.text\r\n retweets = index.retweets\r\n favorites = index.favorites\r\n tweet_date = index.date.strftime(\"%Y-%m-%d\")\r\n \r\n info_list = [tweet_date, username, content, retweets, favorites]\r\n tweet_list.append(info_list)\r\n \r\n return tweet_list\r\n \r\n# logging 설정\r\ndef get_logger():\r\n logger = logging.getLogger(\"my\")\r\n \r\n if len(logger.handlers) > 0:\r\n return logger\r\n \r\n logger.setLevel(logging.INFO)\r\n stream_hander = logging.StreamHandler()\r\n logger.addHandler(stream_hander)\r\n \r\n return logger\r\n \r\ndef crawl_userdata(username):\r\n \r\n # setting\r\n url = 'https://twitter.com/{}'.format(username)\r\n# mylogger.info(\"{} 유저의 데이터 수집 시작\".format(username))\r\n HEADER = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'}\r\n response = requests.get(url, headers=HEADER)\r\n html = response.text\r\n\r\n # parsing\r\n soup = BeautifulSoup(html, \"lxml\")\r\n\r\n # parsing fail\r\n try:\r\n user_profile_header = soup.find(\"div\", {\"class\":'ProfileHeaderCard'})\r\n user_profile_canopy = soup.find(\"div\", {\"class\":'ProfileCanopy-nav'})\r\n\r\n # data collect\r\n user = user_profile_header.find('a', {'class':'ProfileHeaderCard-nameLink u-textInheritColor js-nav'})['href'].strip(\"/\") \r\n\r\n date_joined = user_profile_header.find('div', {'class':\"ProfileHeaderCard-joinDate\"}).find('span', {'class':'ProfileHeaderCard-joinDateText js-tooltip u-dir'})['title']\r\n date_joined = date_joined.split(\"-\")[1].strip()\r\n if date_joined is None:\r\n data_joined = \"Unknown\"\r\n\r\n tweets = user_profile_canopy.find('span', {'class':\"ProfileNav-value\"})['data-count']\r\n if tweets is None:\r\n tweets = 0\r\n \r\n except AttributeError:\r\n# mylogger.info(\"{} 유저의 데이터 수집 중 알수없는 오류가 발생했습니��.\".format(username))\r\n# mylogger.info(\"링크 : {}\".format(url))\r\n user, date_joined, tweets, following, followers = username, None, None, None, None\r\n \r\n # 블락 계정 특징 : 팔로워, 팔로잉 수가 안보임\r\n try:\r\n\r\n test_following = user_profile_canopy.find('li', {'class':\"ProfileNav-item ProfileNav-item--following\"})\r\n test_followers = user_profile_canopy.find('li', {'class':\"ProfileNav-item ProfileNav-item--followers\"})\r\n\r\n following = test_following.find('span', {'class':\"ProfileNav-value\"})['data-count']\r\n followers = test_followers.find('span', {'class':\"ProfileNav-value\"})['data-count']\r\n\r\n# mylogger.info(\"{} 유저의 데이터 수집 완료\".format(username))\r\n\r\n except AttributeError:\r\n# mylogger.info(\"{} 유저는 블락된 계정입니다.\".format(username))\r\n following = \"Block\"\r\n followers = \"Block\"\r\n \r\n os.system('clear')\r\n\r\n result = [user, date_joined, tweets, following, followers]\r\n \r\n return result\r\n \r\n# 파일 저장\r\ndef save_file(tweet_list):\r\n twitter_df = pd.DataFrame(tweet_list, columns = [\"tweet_date\",\"username\",\"content\",\"retweets\",\"favorites\"])\r\n\r\n # csv 파일 생성\r\n twitter_df.to_csv(\"{}_{}_to_{}.csv\".format(keyword, s_date, e_date), index=False)\r\n print(\"=== {} tweets are successfully saved ===\".format(len(user_info)))\r\n\r\n# # 파일 확인\r\n# df_tweet = pd.read_csv('{}_{}_to_{}.csv'.format(keyword, start_date, end_date))\r\n# df_tweet.head(10) # 위에서 10개만 출력\r\n\r\n# 유저 정보 Multiprocessing\r\nglobal user_info\r\nuser_info = []\r\n\r\nkeyword = \"from:3mindia\"\r\n# keyword2 = \"samsung elec\"\r\ns_date = \"2020-04-01\"\r\ne_date = \"2020-04-30\"\r\n\r\ndef main():\r\n # 유저 리스트 수집하기\r\n# tweets = get_tweets(s_date, e_date, keyword,keyword2)\r\n tweets = get_tweets(s_date, e_date, keyword)\r\n tweet_list = get_users(tweets)\r\n \r\n# user_list = users\r\n pool_size = len(tweet_list)\r\n \r\n if pool_size < 8:\r\n pool = Pool(pool_size)\r\n \r\n else:\r\n pool = Pool(8)\r\n \r\n for user in pool.map(crawl_userdata, tweet_list):\r\n user_info.append(user)\r\n \r\n save_file(tweet_list)\r\n \r\nif __name__ == '__main__':\r\n \r\n start_time = time.time()\r\n \r\n mylogger = get_logger()\r\n mylogger.info(\"유저 정보 수집 시작\")\r\n \r\n main()\r\n \r\n end_time = (time.time() - start_time)/60\r\n mylogger.info(\"유저 정보 수집 종료.. {0:0.2f} 분 소요\".format(end_time))\r\n mylogger.info(\"총 수집된 유저 정보는 {} 개 입니다.\".format(len(user_info)))\r\n ","sub_path":"crawling/twi/practice_GetOldTweet2.py","file_name":"practice_GetOldTweet2.py","file_ext":"py","file_size_in_byte":6258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"53179583","text":"from bs4 import BeautifulSoup\nfrom urllib.request import urlopen\n\nclass Akipress:\n url = \"https://akipress.org\"\n\n def open_page(url):\n \twith urlopen(url) as page:\n \t\thtml = page.read().decode()\n \t\treturn html\n\n def search_tag(tag):\n \tsoup = BeautifulSoup(open_page(url), 'html.parser')\n \tlinks = soup.find_all(tag)\n \treturn links\n\n def get_data(cls, tagTitle, tagText):\n \t\tarticle_soup = BeautifulSoup(open_page(url), 'html.parser')\n \t\ttitle = article_soup.find(tagTitle).get_text()\n \t\ttext = article_soup.find('div', class_ = cls).find_all(tagText)\n \t\tarticle_text = [x.get_text() for x in text]\n \t\tdata = {\n \t\t\t'title': title,\n \t\t\t'text-parser': text\n \t\t\t}\n \t\treturn data\n\n for link in search_tag('a'):\n \tif 'tazabek.kg/news' in link.get('href'):\n \t\turl = 'https://' + link.get('href').replace('http://', '').replace('//', '')\n\n \t\twith open('tazabek.txt', 'a', encoding = 'utf-8') as file:\n \t\t\tfile.write(get_data('lenta-row', 'h2', 'p')['title'] + '\\n')\n\n \t\t\tfor x in get_data('lenta-row', 'h2', 'p')['text-parser']:\n \t\t\t\tfile.write(x.get_text() +'\\n')\n\n \telif 'turmush.kg/ru/news' in link.get('href'):\n \t\turl = 'https://' + link.get('href').replace('http://', '').replace('//', '')\n\n \t\twith open('turmush.txt', 'a', encoding = 'utf-8') as file:\n \t\t\tfile.write(get_data('colored-link-text', 'h1', 'p')['title'] + '\\n')\n\n \t\t\tfor x in get_data('colored-link-text', 'h1', 'p')['text-parser']:\n \t\t\t\tfile.write(x.get_text() +'\\n')\n","sub_path":"akipress.py","file_name":"akipress.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"562310900","text":"import pandas\nimport math\nimport argparse\nimport numpy as np\n\nfrom collections import defaultdict\nfrom itertools import combinations\n\ndef distance(det1, det2):\n return math.hypot(det1.x.iloc[0] - det2.x.iloc[0], det1.y.iloc[0] - det2.y.iloc[0])\n\ndef speed(det, df, n):\n # go back n frames to calc speed\n curr_frameID = det.frameID.iloc[0]\n curr_pedID = det.pedID.iloc[0]\n\n # check previous frames and that they exist\n prev_dets = df.loc[(df.pedID == curr_pedID) & (df.frameID < curr_frameID)]\n if len(prev_dets) == 0:\n return 0\n \n # go back n frames (or as far back as possible) to calc distance\n prev_frame = min(n, len(prev_dets))\n prev_det = None\n while prev_frame >= 0:\n prev_det = df.loc[(df.pedID == curr_pedID) & (df.frameID == curr_frameID - prev_frame)]\n if len(prev_det) == 0:\n prev_frame -= 1\n else:\n break\n if len(prev_det) == 0:\n return 0\n\n dist = distance(det, prev_det)\n return dist / n\n\ndef delta_speed(det1, det2, df, n):\n speed1 = speed(det1, df, n)\n speed2 = speed(det2, df, n)\n return speed1 - speed2\n\ndef delta_angle(det1, det2):\n vector1 = [det1.x.iloc[0], det1.y.iloc[0]]\n vector2 = [det2.x.iloc[0], det2.y.iloc[0]]\n\n unit_vector1 = vector1 / np.linalg.norm(vector1)\n unit_vector2 = vector2 / np.linalg.norm(vector2)\n dot_product = np.dot(unit_vector1, unit_vector2)\n angle = np.arccos(dot_product)\n return angle\n\ndef output_data(filename, data):\n with open(filename, \"w\") as outfile:\n outfile.write(\"frameID,pedID1,pedID2,dist,speed,angle,group_label\\n\")\n for d in data:\n line = ','.join([str(x) for x in d])\n outfile.write(line + \"\\n\")\n\n# Output format: \n# Diff b/w two pedestrians in same frame: (distance, speed, angle) -> (1/0)\n# 1 = in same group\n# 0 = not in same group\ndef collect_data(infile, outfile, n):\n df = pandas.read_csv(infile)\n #df = df.loc[df.dataset == \"train\"]\n \n ped_pairs = defaultdict(list)\n for frameID in df.frameID.unique():\n p = list(combinations(df.loc[df.frameID == frameID].pedID, 2))\n ped_pairs[frameID] += p\n\n data = []\n count = 0\n for frameID in ped_pairs:\n for pair in ped_pairs[frameID]:\n pedID1, pedID2 = pair\n det1 = df.loc[(df.frameID == frameID) & (df.pedID == pedID1)]\n det2 = df.loc[(df.frameID == frameID) & (df.pedID == pedID2)]\n\n # calc dist\n dist = distance(det1, det2)\n # calc speed\n speed = delta_speed(det1, det2, df, n)\n # calc angle\n angle = delta_angle(det1, det2)\n # in same group\n if det1.groupID.iloc[0] == det2.groupID.iloc[0] and \\\n det1.groupID.iloc[0] != -1 and det2.groupID.iloc[0] != -1:\n group_label = 1\n else:\n group_label = -1\n \n # frameID,pedID1,pedID2,dist,speed,angle,group_label\n data.append([frameID, det1.pedID.iloc[0], det2.pedID.iloc[0], dist, speed, angle, group_label])\n count += 1\n if count % 5000 == 0:\n print(count)\n \n\n output_data(outfile, data)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Deep SORT\")\n parser.add_argument(\"--input_file\", default=None, required=True)\n parser.add_argument(\"--output_file\", default=None, required=True)\n parser.add_argument(\"--n\", default=1, required=True, type=int)\n return parser.parse_args()\n\nif __name__ == \"__main__\":\n args = parse_args()\n print(args.input_file, args.output_file, args.n)\n collect_data(args.input_file, args.output_file, args.n)\n\n\n","sub_path":"social_relations_model/collect_data.py","file_name":"collect_data.py","file_ext":"py","file_size_in_byte":3705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"296055639","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport copy\nfrom aip import AipNlp\nimport numpy as np\nimport pandas as pd\n\nAPP_ID = '9331282'\nAPI_KEY = 'BGP9B6buzMQ0kL5AbQYoSdGk'\nSECRET_KEY = 'Vx4M4TGL5fjFSSFY8qMQBUnLAsZ2YDTy'\n\n\nclass BaiduSim:\n def __init__(self, most_k=10, csv='./sim.csv'):\n self.similarity = None\n self.aip = None\n self.csv = csv\n self.api_cnt = 0\n self.drop_cnt = 0\n self.most_k = most_k\n self.df = None\n self.load()\n\n def load(self):\n if self.csv and os.path.exists(self.csv):\n self.df = pd.read_csv(self.csv, index_col=0)\n else:\n self.df = pd.DataFrame(index=[], data=[], columns=list(self.cols()))\n\n def cols(self):\n for i in range(self.most_k):\n for attr in 'ns':\n yield '{attr}{index}'.format(attr=attr, index=i + 1)\n\n def dummy_vals(self):\n for i in range(self.most_k):\n for v in ('null', 0.0):\n yield v\n\n def get_aip(self):\n if self.aip is None:\n self.aip = AipNlp(APP_ID, API_KEY, SECRET_KEY)\n return self.aip\n\n def update(self, index, name, sim):\n r = self.df.xs(index)\n ss = [(r['s{}'.format(i)], r['n{}'.format(i)]) for i in range(1, self.most_k + 1)]\n ss.append((sim, name))\n ss = sorted(ss)\n ss.reverse()\n for i, t in enumerate(ss[:self.most_k]):\n (s, n) = t\n self.df.set_value(index, 'n{}'.format(i + 1), n)\n self.df.set_value(index, 's{}'.format(i + 1), s)\n\n def join(self, product):\n if product in self.df.index:\n return\n\n ps = set(self.df.index)\n new_df = pd.DataFrame(index=[product], data=[list(self.dummy_vals())], columns=list(self.cols()))\n self.df = self.df.append(new_df.iloc[0])\n\n api_cnt = 0\n drop_cnt = 0\n r = None\n while ps:\n p = ps.pop()\n try:\n r = self.get_aip().simnet(product, p)\n # print('\\t\\tcall({}):{}'.format(self.api_cnt, p))\n self.api_cnt += 1\n api_cnt += 1\n if 'error_code' in r:\n ps.add(p)\n else:\n sim = r['output']['score']\n if sim > 0.5:\n self.update(p, product, sim)\n self.update(product, p, sim)\n elif sim < 0.3:\n for s in self.sim_of(p):\n if s in ps:\n # print('\\t\\t\\t\\tdrop({}):{}'.format(self.drop_cnt, s))\n self.drop_cnt += 1\n drop_cnt += 1\n ps.remove(s)\n except Exception as e:\n pass\n print('calc sim of {},{}-{}'.format(product, api_cnt, drop_cnt))\n self.dump()\n\n def sim_of(self, product):\n if product in self.df.index:\n r = self.df.xs(product)\n for i in range(self.most_k):\n nk = r['n{}'.format(i + 1)]\n if nk != 'null':\n yield nk\n\n def join_categories(self, cats):\n for c in cats:\n cate_dir = '/Users/junix/products/{cate}'.format(cate=c)\n for _, _, files in os.walk(cate_dir):\n for f in files:\n self.join(f)\n self.dump()\n\n def run(self):\n with open('fs', 'r') as f:\n for c in f.readlines():\n self.join_categories(c.split())\n\n def dump(self):\n if self.df is None:\n return\n if self.csv is None:\n return\n self.df.to_csv(self.csv)\n","sub_path":"baidu.py","file_name":"baidu.py","file_ext":"py","file_size_in_byte":3741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"198537561","text":"from multiprocessing import Process\nimport os\nfrom signal import *\nfrom time import sleep\n\ndef sal_handler(sig,frame):\n if sig == SIGINT:\n os.kill(os.getppid(),SIGUSR1)\n elif sig == SIGQUIT:\n os.kill(os.getppid(),SIGUSR2)\n elif sig == SIGUSR1:\n print(\"到站下车\")\n os._exit()\n\ndef driver_handler(sig,frame):\n if sig == SIGUSR1:\n print(\"老司机，发车！\")\n elif sig == SIGUSR2:\n print(\"车速过快 \")\n elif sig == SIGTSTP:\n os.kill(p.pid,SIGUSR1)\n\n#子进程代表售票员\ndef saler():\n signal(SIGINT,sal_handler)\n signal(SIGQUIT,sal_handler)\n signal(SIGUSR1,sal_handler)\n signal(SIGTSTP,SIG_IGN)\n while True:\n sleep(1)\n print(\"好好学习\")\n\nif __name__ == '__main__':\n\n p = Process(target=saler)\n p.start()\n #父进程\n signal(SIGINT, SIG_IGN)\n signal(SIGQUIT, SIG_IGN)\n signal(SIGUSR1,driver_handler)\n signal(SIGUSR2,driver_handler)\n signal(SIGTSTP,driver_handler)\n p.join()","sub_path":"DuoXianCheng/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"320910042","text":"class SearchMatrix(object):\n '''\n Solution:\n 1. Maintain one pointer starting from either top right or bottom left and search the matrix by modifying row or\n column indices of the pointer to move in a direction towards the target.\n 2. Splitting into 3 cases, if the target is found, return true.\n 3. Otherwise move top or left if target is lower and move bottom or right if target is higher and return false\n if not found.\n\n --- Passed all testcases successfully on leetcode.\n O(max(m, n)) Time Complexity | O(1) Space Complexity\n '''\n\n def searchMatrix(self, matrix, target):\n \"\"\"\n :type matrix: List[List[int]]\n :type target: int\n :rtype: bool\n \"\"\"\n\n if (matrix == None or len(matrix) == 0):\n return False\n\n rows = len(matrix);\n cols = len(matrix[0])\n currRow = rows - 1;\n currCol = 0\n\n while (currRow >= 0 and currCol < cols):\n if (matrix[currRow][currCol] == target): # case 1 == return true\n return True\n elif (matrix[currRow][currCol] < target): # case 2 == move right\n currCol += 1\n else: # case 3 == move top\n currRow -= 1\n\n return False","sub_path":"SearchMatrix.py","file_name":"SearchMatrix.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"507482222","text":"import numpy as np\nimport cv2 as cv\n\n\nimage = cv.imread('c:\\\\Users\\\\kingjy79\\\\Documents\\\\rlawhdduq1\\\\VSCODE\\\\python_conv_test\\\\jenny.jpg', 0)\n\nkernel_get =[]\nf = open(\"c:\\\\Users\\\\kingjy79\\\\Documents\\\\rlawhdduq1\\\\VSCODE\\\\python_conv_test\\\\w_in_1s.dat\", 'r')\nfor i in range(150):\n line = int(f.readline())\n kernel_get.append(line)\n\nf.close()\n\n'''\n#값이 제대로 들어갔는지 확인//확인 완료\nfor i in range(150):\n print(kernel_get[i])\n'''\n\n\n'''\ninput_get =[]\nf = open(\"c:\\\\Users\\\\kingjy79\\\\Documents\\\\rlawhdduq1\\\\VSCODE\\\\python_conv_test\\\\x_in_1s.dat\", 'r')\nfor i in range(1024):\n line = f.readline()\n input_get.append(line)\n\nf.close()\n'''\n'''#값이 제대로 들어갔는지 확인//확인 완료\nfor i in range(1024):\n print(input_get[i])\n'''\n'''\n\nkernel = np.array([[1, 0, -1],\n [1, 0, -1],\n [1, 0, -1]])\n\nfiltered = cv2.filter2D(src=image, kernel=kernel, ddepth=-1)\ncv2.imshow('horizontal edges', filtered)\n'''\n\n\n\nkernel_matrix=[]\nfor k in range(6): # k= depth\n kernel_line = [] # 빈 리스트 생성\n for i in range(5): #height\n line = [] # 안쪽 리스트로 사용할 빈 리스트 생성\n for j in range(5): #width\n line.append(kernel_get[k*25+5*i+j]) \n kernel_line.append(line) # 전체 리스트에 안쪽 리스트를 추가\n kernel_matrix.append(kernel_line)\n\nkernel_matrix_np=np.array(kernel_matrix)\nfor k in range(6):\n print(kernel_matrix_np[k])\n\n#image2=cv.copyMakeBorder(image, top=2, bottom=2, left=2, right=2, borderType= cv.BORDER_CONSTANT )\n\nf = open('jenny_p.dat','w')\nfor k in range(6):\n filtered = cv.filter2D(src=image, kernel=kernel_matrix_np[k], ddepth=-1,borderType= cv.BORDER_CONSTANT )\n for i in range(48):\n for j in range(48):\n f.write(str(filtered[i][j])+'\\n') #10진수 표현(hex->str)\nf.close()\n","sub_path":"VScode/python_conv_test/conv_test.py","file_name":"conv_test.py","file_ext":"py","file_size_in_byte":1890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"405765029","text":"# requests #(do pobierania)\n# bs4 #(do parsowania)\n# import json\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\n\nSUTTA_LINKS = 'http://sasana.pl/alfabetycznie'\n\nTRANRSLATORS = {\n 'vil': 'Piotr Jagodziński',\n 'var': 'Varapanyo Bhikkhu',\n 'sir': 'Siristru',\n 'agr': 'Agrios',\n 'kow': 'Hubert Kowalewski',\n 'ltw': \"Lo'tsa'wa (Dobromił Dowbór)\",\n 'krz': 'Janusz Krzyżowski',\n }\n\nCOLLECTIONS = {\n 'DN': 'Dīgha Nikāya',\n 'MN': 'Majjhima Nikāya',\n 'SN': 'Saṃyutta Nikāya',\n 'AN': 'Aṅguttara Nikāya',\n 'KN': 'Khuddaka Nikāya',\n}\n\n\ndef get_link_list(url): # => [link1, link2]\n \"\"\"\n (net) pobranie glownej strony (alfabetycznie)\n \"\"\"\n # 1. pobranie tresci\n # response = requests.get(url)\n\n # 2. parsowanie tresci (wydobywanie linkow)\n # return []\n suttas_links_list = []\n response = requests.get(url)\n html_doc = response.text\n soup = BeautifulSoup(html_doc, 'html.parser')\n table_of_contents = soup.find('div', class_='yui-content')\n for a in table_of_contents.findAll('a', href=True):\n suttas_links_list.append(a[\"href\"])\n # suttas_links_list.append('http://sasana.pl{}'.format(a['href']))\n return suttas_links_list\n\n\ndef get_sutta_data_list(link_list):\n \"\"\"Returns list of resultss\"\"\"\n sutta_text_list = []\n for link in link_list:\n sutta_text = get_sutta_data(link)\n sutta_text_list.append(sutta_text)\n return sutta_text_list\n\n\ndef get_sutta_data(link):\n \"\"\"\n Results:\n results: {\n title: str,\n paragraph_list: [str...],\n link: str,\n author: str,\n collection: str,\n sutta_nr: int\n }\n \"\"\"\n # Korzystamy z metody results.update, poniewaz przepisuje\n # ona dane z jednego slownika do drugiego\n # a to oznacza, ze kod mozemy podzielic na 2 czesci\n # (jedna dla url, druga dla html) i scalic wynik w jedno.\n\n data = {}\n\n # 1. url\n data.update(get_sutta_data_from_url(link))\n\n # 2. html\n data.update(get_sutta_data_from_html(link))\n\n return data\n\n\ndef get_sutta_data_from_url(link):\n \"\"\"\n Results:\n results: {\n link: str,\n author: str,\n collection: str,\n sutta_nr: int\n }\n \"\"\"\n\n results = {'link': f'http://sasana.pl{link}'}\n\n author = TRANRSLATORS.get(parse_author_code(link))\n if author is not None:\n results['author'] = author\n\n collection = COLLECTIONS.get(parse_collection_code(link))\n if collection is not None:\n results['collection'] = collection\n\n results['sutta_nr'] = parse_sutta_nr(link)\n return results\n\n\ndef parse_sutta_nr(link):\n sutta_code = re.search(r'(?<=-)\\d+', link)\n if sutta_code is None:\n return link\n else:\n return sutta_code.group()\n\n\ndef parse_author_code(link):\n match_auth = re.search(r'[a-z]{3}$', link)\n return match_auth.group()\n\n\ndef parse_collection_code(link):\n match_coll = re.search(r'(?<=/)\\w+', link)\n return match_coll.group()\n\n\ndef get_sutta_data_from_html(link):\n \"\"\"\n Results:\n results: {\n title: str,\n paragraph_list: [str...],\n }\n \"\"\"\n # 1. pobieramy tresc strony\n # 2. parsujemy strone\n # 3. pamietaj zeby przepuszczac tylko polskie paragrafy\n\n results = {}\n response = requests.get(f'http://sasana.pl{link}')\n html_doc = response.text\n soup = BeautifulSoup(html_doc, 'html.parser')\n\n results['title'] = parse_sutta_title(soup)\n\n results['paragraph_list'] = parse_all_paragraphs(soup)\n\n return results\n\n\ndef parse_all_paragraphs(html_parser):\n if check_page_kind(html_parser) == 'table':\n return parse_paragraph_table(html_parser)\n else:\n return parse_paragraph_list(html_parser)\n\n\ndef check_page_kind(html_parser):\n if html_parser.find('table') is None:\n return\n # print('No table: ', html_parser)\n else:\n paragraph_data_list = html_parser.select('table p')\n for paragraph in paragraph_data_list:\n if len(str(paragraph)) > 400:\n return 'table'\n\n\ndef parse_paragraph_table(html_parser):\n paragraph_list = []\n sutta_content = html_parser.select('table p')\n # print(sutta_content)\n for paragraph in sutta_content:\n if len(str(paragraph)) > 200:\n # print(paragraph)\n pl_paragraph = chcek_lang(paragraph)\n if pl_paragraph is True:\n # print(pl_paragraph)\n paragraph_list.append(paragraph)\n\n return paragraph_list\n\n\ndef parse_paragraph_list(html_parser):\n paragraph_list = []\n sutta_content = html_parser.find('div', class_='drop')\n if sutta_content is not None:\n for paragraph in sutta_content.findAll('p', style=False):\n paragraph_list.append(paragraph)\n return paragraph_list\n\n\ndef chcek_lang(paragraph):\n special_pl_letters = 'ąężźśćłó'\n paragraph_str = (str(paragraph.string)).lower()\n return bool(set(special_pl_letters) & set(paragraph_str))\n\n\ndef parse_sutta_title(html_parser):\n title_content_data = html_parser.find('div', class_='page-title')\n title_content = title_content_data.find('span')\n title_content_string = title_content.string\n title_match = re.search(r'(?<=-.|–.).+\\w', title_content_string)\n if title_match is None:\n print(\"I can't find title: \", title_content)\n return title_match\n else:\n title = title_match.group()\n return title\n\n\n# get_sutta_data_from_url(SUTTA_LINKS)\n\ndef run_it():\n # print(get_link_list(SUTTA_LINKS))\n # print(get_link_list(SUTTA_LINKS)[0])\n # print(parse_sutta_nr(get_link_list(SUTTA_LINKS)[0]))\n print(get_sutta_data_list(get_link_list(SUTTA_LINKS)))\n\n\nrun_it()\n","sub_path":"parser_html.py","file_name":"parser_html.py","file_ext":"py","file_size_in_byte":5815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128886569","text":"# Write a program that accepts sequence of lines as input and prints the lines after making all characters in the sentence capitalized.\n#\n# Suppose the following input is supplied to the program:\n#\n# Hello world\n# Practice makes perfect\n# Then, the output should be:\n#\n# HELLO WORLD\n# PRACTICE MAKES PERFECT\n\ninputToCapitalize=[]\nline=input()\nwhile line:\n inputToCapitalize.append(line)\n line=input()\n\ntoCapitalize= \"\\n\".join(inputToCapitalize)\n\ntoCapitalize= toCapitalize.upper()\n\nprint(toCapitalize)\n\n\n\n\n","sub_path":"Q9.py","file_name":"Q9.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"607499537","text":"# Prompt: https://leetcode.com/problems/water-bottles/\n# Runtime: 20 ms, faster than 48.33% of Python online submissions for Water Bottles.\n# Memory Usage: 13.3 MB, less than 72.92% of Python online submissions for Water Bottles.\n\n\nclass Solution(object):\n def numWaterBottles(self, numBottles, numExchange):\n \"\"\"\n :type numBottles: int\n :type numExchange: int\n :rtype: int\n \"\"\"\n \n numFull = numBottles\n numEmpty = 0\n drank = 0\n \n while numFull > 0:\n # drink full waterbottles\n drank += numFull\n numEmpty += numFull\n # refill\n numFull = numEmpty // numExchange\n numEmpty = numEmpty % numExchange\n \n return drank\n","sub_path":"0. Easy/1518. Water Bottles/water_bottles.py","file_name":"water_bottles.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"640619034","text":"# MenuTitle: Copy to Background, Decompose, Remove Overlaps, Correct Path Direction\nfrom GlyphsApp import Glyphs\n\nfor layer in Glyphs.font.selectedLayers:\n g = layer.parent\n for layer in g.layers:\n layer.background = layer.copy()\n layer.decomposeComponents()\n layer.removeOverlap()\n layer.correctPathDirection()\n","sub_path":"Glyphs/DecRO.py","file_name":"DecRO.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"184943588","text":"import SimpleITK as sitk\n\nimport os\nimport uuid\nimport zipfile\nfrom django.conf import settings\n\n\ndef get_path_to_file(file):\n path, is_dir = handle_uploaded_file(file)\n return path, is_dir\n\n\ndef handle_uploaded_file(f):\n is_dir = False\n uuid_str = str(uuid.uuid1())\n temp_name = uuid_str + \"-\" + f.name\n path = os.path.join(settings.BASE_DIR, 'temp', temp_name)\n\n with open(path, 'wb+') as destination:\n for chunk in f.chunks():\n destination.write(chunk)\n\n if f.name.endswith('.zip'):\n temp_dir = os.path.join(settings.BASE_DIR, 'temp', temp_name.replace(\".zip\", \"\"))\n print('temp_dir = ', temp_dir)\n with zipfile.ZipFile(path, 'r') as zip_ref:\n zip_ref.extractall(os.path.join(settings.BASE_DIR, 'temp', temp_dir))\n is_dir = True\n os.remove(path)\n path = temp_dir\n\n return path, is_dir\n\n\ndef load_image(filename):\n image = sitk.ReadImage(filename)\n ct_scan = sitk.GetArrayFromImage(image)\n\n return image, ct_scan","sub_path":"backend/diplom_server/diplom_backend/service/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"562580074","text":"#!/usr/bin/env python3\n# encoding=utf8\n\n# process text (from raw)\nimport codecs\nimport jieba\n\ndef strip_punctuation(text):\n return re.sub(r'[^\\w\\s]',' ',text)\n\n# mapping from 0, 1, ..., 9 to chinese character\ndigits={'0':'零', '1':'一', '2':'二', '3':'三', '4':'四', '5':'五', '6':'六', '7':'七', '8':'八', '9':'九'}\n# tenth\ntenths={'1':'十', '2':'二十','3':'三十', '4':'四十', '5':'五十', '6':'六十', '7':'七十', '8':'八十', '9':'九十'}\n\n# mapping from 10, 10^2, 10^3, 10^4, 10^8 to chinese characters\ntens=[u'', u'\\u5341', u'\\u767e', u'\\u5343']\nthousds=[u'', u'\\u4e07', u'\\u4ebf', u'\\u5146']\n\n# Chinese coded English letters and numbers\nchnalpupper=[u'\\uff21',u'\\uff22', u'\\uff23', u'\\uff24', u'\\uff25', u'\\uff26', u'\\uff27',u'\\uff28', u'\\uff29', u'\\uff2a', u'\\uff2b', u'\\uff2c', u'\\uff2d',u'\\uff2e', u'\\uff2f', u'\\uff30', u'\\uff31', u'\\uff32', u'\\uff33', u'\\uff34', u'\\uff35', u'\\uff36', u'\\uff37', u'\\uff38', u'\\uff39', u'\\uff3a']\nchnalplower=[u'\\uff41',u'\\uff42', u'\\uff43', u'\\uff44', u'\\uff45', u'\\uff46', u'\\uff47',u'\\uff48', u'\\uff49', u'\\uff4a', u'\\uff4b', u'\\uff4c', u'\\uff4d',u'\\uff4e', u'\\uff4f', u'\\uff50', u'\\uff51', u'\\uff52', u'\\uff53', u'\\uff54', u'\\uff55', u'\\uff56', u'\\uff57', u'\\uff58', u'\\uff59', u'\\uff5a']\nchnnumber=[u'\\uff10', u'\\uff11', u'\\uff12', u'\\uff13', u'\\uff14', u'\\uff15', u'\\uff16', u'\\uff17', u'\\uff18', u'\\uff19']\n\n# look for 19XX年, 2XXX年\nimport re\npat_year=re.compile('[1|2]\\d{3}年')\npat_time=re.compile('\\d{2}：\\d{2}')\npat_monthdate=re.compile('(?P[0-9]|1[012])月(?P[1-9])日')\npat_number=re.compile('(?P\\d+)(?P\\D)')\npat_emojy=re.compile('\\[(?P\\w+)\\]')\npat_twitter=re.compile('@(?P\\w*)')\npat_dot=re.compile('(?P\\d+)\\.(?P\\d+)')\npat_numberstring=re.compile('(?P\\d+)')\npat_percent=re.compile('(?P\\d+)%')\n\ndef conver_number(text):\n\n\tdigit=int(text)\n\n\tif (digit>999):\n\t\treturn conver_digitstring(text)\n\n\treturn convert_hundred(digit)\n\n# this should process up to 99999 (万)\ndef convert_tenthousand(digit):\n\n\tif (digit < 10000):\n\t\treturn convert_thousand(digit)\n\telse:\n\t\ttenthousand, modulo = divmod(digit, 10000)\n\n\tnumbers = []\n\t# append thousands\n\tnumbers.append(digits[str(tenthousand)] + '万')\n\tif (modulo == 0):\n\t\treturn ''.join(numbers)\n\n\t#if str(digit)[2] == '0':\n\t#\tnumbers.append('零')\n\n\tnumbers.append(convert_thousand(modulo))\n\treturn ''.join(numbers)\n\n# this should process up to 9999\ndef convert_thousand(digit):\n\n\tif (digit<1000):\n\t\treturn convert_hundred(digit)\n\telse:\n\t\tthousand, modulo=divmod(digit,1000)\n\tnumbers=[]\n\t# append thousands\n\tnumbers.append(digits[str(thousand)] + '千')\n\tif (modulo==0):\n\t\treturn ''.join(numbers)\n\n\t#if str(digit)[2] == '0':\n\t#numbers.append('零')\n\n\tnumbers.append(convert_hundred(modulo))\n\treturn ''.join(numbers)\n\n# this should process up to 999\ndef convert_hundred (digit):\n\n\tif (digit<100):\n\t\treturn convert_tenth(digit)\n\telse:\n\t\thundreds, modulo = divmod(digit, 100)\n\tnumbers=[]\n\t# append hundres\n\tnumbers.append(digits[str(hundreds)] + '百')\n\tif (modulo == 0):\n\t\treturn ''.join(numbers)\n\t#if str(digit)[2] == '0':\n\t#\tnumbers.append('零')\n\n\tnumbers.append(convert_tenth(modulo))\n\treturn ''.join(numbers)\n\n# this should process up to 99\ndef convert_tenth (digit):\n\n\tif (digit<10):\n\t\treturn digits[str(digit)]\n\ttenth, modulo=divmod (digit,10)\n\tif (modulo ==0):\n\t\treturn tenths[str(tenth)]\n\telse:\n\t\treturn tenths[str(tenth)]+digits[str(modulo)]\n\ndef process_tenth(match):\n\tm = int(match.group('month'))\n\td = int(match.group('date'))\n\treturn convert_tenth(m)+'月'+convert_tenth(d)+'日'\n\n\n# this will spell out matching digits string\ndef spell_out(match):\n\tstr=[]\n\tfor m in match.group():\n\t\tif(m in digits):\n\t\t\tstr.append(digits[m])\n\t\telse:\n\t\t\tstr.append(m)\n\treturn ''.join(str)\n\n# incoming is a string\ndef conver_digitstring(digit):\n\tstr=[]\n\tfor s in digit:\n\t\tstr.append(digits[s])\n\treturn ''.join(str)\n\ndef processYear(text):\n\treturn pat_year.sub(spell_out,text)\n\ndef processTime(text):\n\treturn pat_time.sub(spell_out, text)\n\ndef processMonthDate(text):\n\treturn pat_monthdate.sub(process_tenth, text)\n\n\n\ndef remove_httpsuffix(text):\n\treturn text.split('http://')[0]\n\ndef process_emojy(match):\n\tm=match.group('emojy')\n\tif len(m) > 0: print (m)\n\ndef processEmojy(text):\n\treturn pat_emojy.sub(process_emojy,text)\n\ndef process_hashtag(match):\n\tm= match.group('username')\n\tif len(m)>0: print (m)\n\ndef processHashTag(text):\n\treturn pat_twitter.sub(process_hashtag, text)\n\nimport random\ndef process_dotnumeric(match):\n\tdecimal=match.group('decimal')\n\tfraction=match.group('fraction')\n\t# we don't really care about the actual number so output a random\n\t# number string ...\n\treturn convert_hundred(random.randint(1, 100))+'点'+ \\\n\t\t\tconvert_hundred(random.randint(1, 100))\n\ndef processDotNumeric(text):\n\treturn pat_dot.sub(process_dotnumeric, text)\n\n\ndef processNumberStringSwipe(text):\n\treturn pat_numberstring.sub(spell_out, text)\n\n### detect the string has chinese chracters\ndef anyChineseCharacters(text):\n\treturn re.findall('[\\u4e00-\\u9fff]+', text)\n\n\ndef process_number(match):\n\treturn conver_number(match.group('number'))\ndef processNumberString(text):\n\treturn pat_numberstring.sub(process_number, text)\n\n\ndef process_percent(match):\n\treturn '百分之'+conver_number(match.group('number'))\ndef processPercentString(text):\n\treturn pat_percent.sub(process_percent,text)\n\n## this will do the multiple replacements\nrepls={'《':' ', '》':' ', '(':' ', ')':' ', '（':' ','）':' ', \\\n\t '‘':'', '’':'','、':' ', \\\n\t '０':'0','１':'1','２':'2','３':'3','４':'4','５':'5','６':'6','７':'7','８':'8','９':'9', \\\n 'Ａ':'A','Ｂ':'B', 'Ｃ':'C','Ｄ':'D','Ｅ':'E','Ｆ':'F','Ｇ':'G','Ｈ':'H','Ｉ':'I','Ｊ':'J','Ｋ':'K','Ｌ':'L',\\\n\t 'Ｍ':'M','Ｎ':'N', 'Ｏ':'O','Ｐ':'P','Ｑ':'Q','Ｒ':'R','Ｓ':'S','Ｔ':'T','Ｕ':'U','Ｖ':'V','Ｗ':'W','Ｘ':'X',\\\n\t 'Ｙ':'Y','Ｚ':'Z', \\\n 'ａ':'a','ｂ':'b', 'ｃ':'c','ｄ':'d','ｅ':'e','ｆ':'f','ｇ':'g','ｈ':'h','ｉ':'i','ｊ':'j','ｋ':'k','ｌ':'l',\\\n\t 'ｍ':'m','ｎ':'n', 'ｏ':'o','ｐ':'p','ｑ':'q','ｒ':'r','ｓ':'s','ｔ':'t','ｕ':'u','ｖ':'v','ｗ':'w','ｘ':'x',\\\n\t 'ｙ':'y','ｚ':'z', \\\n\t '①':' ', '②':' ', '③':' ',\\\n\t '％':'%', 'ldquo':' ','rdquo':' '\n\t}\ndef multiple_replace(string):\n\trep_dict=repls\n\tpattern = re.compile(\"|\".join([re.escape(k) for k in rep_dict.keys()]), re.M)\n\treturn pattern.sub(lambda x: rep_dict[x.group(0)], string)\n\ndef allEnglish (text):\n\tmatch = re.search('[a-zA-Z]+',text)\n\tif match and len(text) == match.end()-match.start():\n\t\treturn True\n\treturn False\n\nimport uuid\ndef write_out(out_dir, sent_list):\n\toutfile=out_dir+'/'+str(uuid.uuid4())+'.txt'\n\tprint('write to {}:{} entries'.format(outfile, len(sent_list)))\n\twith codecs.open(outfile,'w','utf-8') as fp:\n\t\tfp.write('\\n'.join(sent_list))\n\tdel sent_list[:]\n\nimport argparse\n# all arguments parsing\nparser = argparse.ArgumentParser(description='prepare text for LM training')\nparser.add_argument('src_text')\nparser.add_argument('tgt_dir')\nparser.add_argument('nitems')\n\nargs = parser.parse_args()\n\n# not running debugging\ndebug=False\n\nfrom zh_text import setup_logging\nlogger=setup_logging('prepare_news', 'logs/prepare_news.log')\n\n\nwith codecs.open(args.src_text, 'r', 'gb18030') as fp:\n\n\tsent_list=[]\n\tN=int(args.nitems)\n\n\t# chop and write out to file very N items\n\tfor line in fp:\n\t\ttext=re.sub('[\\s+]','',line.strip())\n\t\tif debug: print('**raw**:{}'.format(text))\n\t\t# break the sentences with these puncutation marks\n\t\tsentences= re.split('[! |？|。|“|”|！]',text)\n\t\tfor sentence in sentences:\n\t\t\tif (len(sentence)>0):\n\t\t\t\tskip=False\n\n\t\t\t\tif debug: print ('**sen**:{}'.format(sentence))\n\t\t\t\t# replace all puntuations\n\t\t\t\ttext= multiple_replace(sentence)\n\t\t\t\tif debug: print ('**text**:{}'.format(text))\n\t\t\t\ttext=processPercentString(text)\n\t\t\t\tif debug: print ('**percent**:{}'.format(text))\n\t\t\t\ttext = strip_punctuation(text)\n\t\t\t\tif debug: print ('**punc**:{}'.format(text))\n\t\t\t\t# normalize number\n\t\t\t\ttext=processNumberString(text)\n\t\t\t\tif debug: print ('**number**:{}'.format(text))\n\t\t\t\t# remove all white space\n\t\t\t\t# text=text.replace(\" \",\"\")\n\t\t\t\t# need chinese characters and maybe a-z/A-Z and number\n\t\t\t\tmatch=re.findall('[a-zA-Z0-9]*[\\u4e00-\\u9fff]+', text)\n\t\t\t\tif not match:\n\t\t\t\t\tlogger.info ('skipping {}'.format(text))\n\t\t\t\telse:\n\t\t\t\t\t# add to the list\n\t\t\t\t\tsent_list.append(' '.join(match))\n\t\t\t\t#text=remove_httpsuffix(sentence)\n\t\t\t\t#if debug: print ('**http**:{}'.format(text))\n\t\t\t\t#text=processEmojy(text)\n\t\t\t\t#if debug: print('__emojy:{}'.format(text))\n\t\t\t\t#text=processHashTag(text)\n\t\t\t\t#if debug: print('__hashtag:{}'.format(text))\n\t\t\t\t#text=processYear(text)\n\t\t\t\t#if debug: print ('**year**:{}'.format(text))\n\t\t\t\t#text=processTime(text)\n\t\t\t\t#if debug: print ('**time**:{}'.format(text))\n\t\t\t\t#text=processMonthDate(text)\n\t\t\t\t#if debug: print ('**monthdate**:{}'.format(text))\n\t\t\t\t# itemized matching\n\t\t\t\t#text = multiple_replace(text, repls)\n\t\t\t\t#if debug: print ('**repls**:{}'.format(text))\n\t\t\t\t#text= processDotNumeric(text)\n\t\t\t\t#if debug: print ('**dot-numeric**:{}'.format(text))\n\t\t\t\t# this will be largely matching for number string\n\t\t\t\t#text = processNumberString(text)\n\t\t\t\t#if debug: print ('**numbers**:{}'.format(text))\n\t\t\t\t#text= processNumberStringSwipe(text)\n\t\t\t\t#if debug: print ('**numswipe**:{}'.format(text))\n\t\t\t\t#text=strip_punctuation(text)\n\t\t\t\t#if debug: print ('**punc**:{}'.format(text))\n\t\t\t\t#print('norm:{}-{}'.format(text, len(text)))\n\t\t\t\t#text=''.join(anyChineseCharacters(text))\n\t\t\t\t#if debug: print ('**join**:{}'.format(text))\n\t\t\t\t#if len(text) > 0:\n\t\t\t\t#\tsent_list.append(' '.join(jieba.cut(text)))\n\t\tif len(sent_list) > N:\n\t\t\twrite_out(args.tgt_dir, sent_list)\n\n# the incoming text is chunked into paragraph as line\n\n","sub_path":"local/lm/prepare_news.py","file_name":"prepare_news.py","file_ext":"py","file_size_in_byte":9778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"527045711","text":"\"\"\"\n Info UA\n 'Djangonize It!'\n Однофайлова утиліта для спрощення процесів роботи із зображеннями для Django розробників. Для використання\n потрібно розмістити файл програми в папку із зображеннями (images) static папки django-проекту. Дане рішення\n одночасно служить підтримкою для рекомендованої структури папок в django-проектах та дозволяє програмі\n та корисувачу уникнути додаткових дій по налаштуванню місця збереження зображень. Також варто мати\n встановлене розширення PyQt4 (якщо встановлена інша версія PyQt, можна спробувати замінити значення в import).\n Дана програма призначена для виконання наступних операцій:\n 1. Пошук і заміна посилань зображень в фронтенд-файлах (CSS,HTML) на django-посинання. Пошук здійснюється\n на основі регулярних виразів. В програмі присутні базові регулярні вирази для CSS і HTML, які є\n доступними для редагування користувачем (для випадків, коли назва папки із зображеннями у проекті не images).\n При здійсненні заміни, файл не замінюється, а створюється його копія (у папці із оригіналом) \n на випадок неточного регулярного виразу. Ім'я копії формується як \"[0-9]старе ім'я\", що дозволяє \n простіше знаходити його у папці (він буде зверху, або знизу). Крім того, реалізовано можливість \n відкриття папки із редагованими файлами напряму із програми, відразу після пошуку.\n 2. Завантаження зображення із інтернету за посиланням (у папку images) та надання користувачу валідного\n django-посилання для його проекту. Всі згенеровані посилання архівуються (\"db.txt\" в папці\n розміщення програми). Також реалізовано класс, для зручної роботи із даним архівом із самої програми,\n який підтримує сортування по імені/django-посиланні/даті. Пошук по архіву можна здійснювати як\n за допомогою звичайних записів, так і регулярних виразів.\n Програма написана в об'єкто-орієнтованому стилі. Кожен клас реалізований графічно через розширення PyQt4\n (в програмі використано як і абсолютне позіционування елементів так і позиціонування через layout-и).\n Структура наслідування є наступною:\n\n PyQT Parent | QtGui.QWidget QtGui.QSortFilterProxyModel\n Parent | DjangoImages SortFilterHistory (використовується як екземпляр History)\n 1st Child | DjangoFiles History (не наслідує конструктор)\n 2nd Child | MainMenu\n\n Програма розроблена і протестована в середовищі Windows, але повинна працювати в Unix-based (не перевірено).\n Також реалізовано валідатори для запобігання найчастіщих помилок і допомоги користувачам які не читають info в\n файлі. Усі налаштування за замовчуванням винесені в Parent клас (DjangoImages).\n\"\"\"\n\"\"\"\n Info EN\n 'Djangonize It!' (A single-file application)\n The purpose of this application is to simplify work with images for Django developers.\n For successful using, you should to install the app into \"images\" folder inside \"static\" folder of your\n django project (../static/../images/). This solution simultaneously supporting the recommended file\n structure of django projects and allows to avoid additional user and program activities related with path setting\n for image download. The installed PyQt4 is, also, needed.\n The application allows to perform the next operations:\n 1. Search and replacement image links on django-links at frontend (HTML, CSS) files. Search is RegEx driven.\n The application have default regular expressions for CSS and HTML files, which can be changed by user (this\n is necessary for cases when name of folder with images isn't \"images\").\n When you run the djangonization process for the file, it isn't replaced. The app creates a copy of file\n at folder with original. Copy's name is forming as a \"[0-9]old name\", which allow to simplify it searching at\n folder (it was at the top or bottom). Also, created file can be opened from the program by os explorer.\n 2. Download images from Internet by link (in images folder) and return valid link for user's django project.\n All results of operation are archiving (in \"db.txt\" at folder with program). Also, the class to simplify\n work with logs is realized. It supports sorting by name/django link/date. Also, searching can be performed\n by normal and RegEx strings.\n The application is created with object-oriented style. Every class is realized graphically by PyQt4 extension\n (both absolute and layout positioning are used).\n The inheritance structure is the following:\n\n PyQT Parent | QtGui.QWidget QtGui.QSortFilterProxyModel\n Parent | DjangoImages SortFilterHistory (used as an example for the History)\n 1st Child | DjangoFiles History (closed __init__)\n 2nd Child | MainMenu\n\n The program is developed and testing in Windows environment, but it should work at Unix-based systems\n (isn't checked). Also validators for most frequently errors and user helping are realised.\n All default settings are placed at the Parent class (DjangoImages).\n\n\"\"\"\n\nimport os\nimport sys\nimport re\nfrom urllib.request import URLopener\nfrom datetime import datetime\nfrom random import randint\nfrom PyQt4 import QtGui, QtCore\n\n\nclass DjangoImages(QtGui.QWidget):\n ''' Parent class of application (contains elements constructors and default variables for other classes).\n Download images from Web, return django-links, log the result.\n\n '''\n NOW = datetime.now() # Constant for logs\n PATH = os.getcwd() # Constant for link generation and file management\n FILE = \"djangonizeit.pyw\" # Constant for internal restart (should be modified with app filename)\n size = 450, 340 # Default size of application windows (width, height)\n position = 450, 150 # Default position of application windows (horizontal,vertical)\n bFontSize = 10 # Default font size for buttons\n bStyle = \"color: rgb(0, 85, 255);\" # Default stylesheet for buttons\n lFontSize = 10 # Default font size for labels\n lStyle = \"color: rgb(0, 85, 255);\" # Default stylesheet for labels\n database = \"db.txt\" # Default name of database (for logs)\n defaultCSS = r'\\.\\..*/images/' # Default pattern for re.sub function (DjangoFiles().djangonize())(lns 453/459)\n defaultHTML = r'src=\\\".*images/(.*\\.[a-z]{3})\\\"' # Default pattern for re.sub function (lines 470/476)\n\n def __init__(self):\n super(DjangoImages, self).__init__()\n # Buttons\n self.djangonizeButton = self.create_button(\"Djangonize It!\", self.djangonize,\n tooltip=\"Download image,return django link, log the result\")\n self.quitButton = self.create_button('Quit', self.quit_app(), tooltip=\"Close All Windows\")\n self.openButton = self.create_button('History', self.open_next(), tooltip=\"Open window with logs\")\n\n # Lines\n self.linkText = self.create_text_edit()\n self.nameLine = self.create_line_edit()\n self.djangoLine = self.create_line_edit()\n\n def __call__(self):\n # Give the ability to be opened outside. Contains information about positioning of elements on windows\n linkLayout = QtGui.QGridLayout()\n linkLayout.addWidget(self.linkText, 0, 0)\n nameLayout = QtGui.QGridLayout()\n nameLayout.addWidget(self.nameLine, 1, 0)\n\n buttonsLayout = QtGui.QGridLayout()\n buttonsLayout.addWidget(self.djangonizeButton, 0, 3)\n buttonsLayout.addWidget(self.quitButton, 2, 5)\n buttonsLayout.addWidget(self.openButton, 2, 0)\n buttonsLayout.addWidget(self.djangoLine, 1, 3)\n\n linkGroupBox = QtGui.QGroupBox(\"URL:\")\n linkGroupBox.setLayout(linkLayout)\n nameGroupBox = QtGui.QGroupBox(\"Filename:\")\n nameGroupBox.setLayout(nameLayout)\n buttonsGroupBox = QtGui.QGroupBox(\"Djangonizetion and Control:\")\n buttonsGroupBox.setLayout(buttonsLayout)\n\n mainLayout = QtGui.QVBoxLayout()\n mainLayout.addWidget(linkGroupBox)\n mainLayout.addWidget(nameGroupBox)\n mainLayout.addWidget(buttonsGroupBox)\n\n self.setLayout(mainLayout)\n\n self.setWindowTitle(\"Djangonize image from Web\")\n self.resize(*self.size)\n self.move(*self.position)\n self.show()\n\n # Element constructors\n def create_button(self, text, activity, tooltip=None, fontsize=int(bFontSize), style=bStyle):\n button = QtGui.QPushButton(text, self)\n button.clicked.connect(activity)\n button.setToolTip(tooltip)\n font = QtGui.QFont()\n font.setPointSize(fontsize)\n font.setBold(True)\n font.setWeight(75)\n button.setFont(font)\n button.setStyleSheet(style)\n return button\n\n def create_label(self, text, fontsize=int(lFontSize), style=str(lStyle)):\n label = QtGui.QLabel(text, self)\n font = QtGui.QFont()\n font.setPointSize(fontsize)\n font.setBold(True)\n font.setWeight(75)\n label.setFont(font)\n label.setStyleSheet(style)\n return label\n\n def create_text_edit(self):\n textEdit = QtGui.QTextEdit()\n return textEdit\n\n def create_line_edit(self):\n lineEdit = QtGui.QLineEdit()\n return lineEdit\n\n def dir_list(self):\n # Method which returns list of folders before static folder (in django structure). Optimized for Linux\n if os.name == 'nt':\n pathList = re.findall(r'static\\\\(.+).*$', self.PATH) # path is saved as a list with one element\n try:\n dirList = pathList[0].split('\\\\')\n except IndexError: # Error when list is empty (for cases when the application isn't installed)\n QtGui.QMessageBox.warning(self, \"InstallError\",\n \"Please, move the program inside your django project \"\n \"(..\\static\\..\\images)to solve the Error!\")\n raise\n return dirList\n else:\n pathList = re.findall(r'static/(.+).*$', self.PATH)\n try:\n dirList = pathList[0].split('/')\n except IndexError:\n QtGui.QMessageBox.warning(self, \"InstallError\",\n \"Please, move the program inside your django project \"\n \"(../static/../images)to solve the Error!\")\n raise\n return dirList\n\n def djangonize(self):\n # Download image and return its django-link (Overridable)\n url = str(self.linkText.toPlainText())\n if re.search(r'\\.[a-z]{3}$', url): # Validate link by type of file\n filename = str(self.nameLine.displayText())\n\n if len(filename) == 0: # If the filename line is empty save the image with its basename\n newFilename = os.path.basename(url)\n else: # Else, save it with entered name and original fileformat\n newFilename = filename + os.path.basename(url)[-4:]\n\n dirList = self.dir_list()\n\n # If with dir_list() all is ok, download image to django directory\n # If statement don't used here, because any Exception in dir_list() stop execution of the method\n URLopener().retrieve(str(url), newFilename)\n\n # Return the link to user interface\n djangoView = \"{% static '\" + '/'.join(dirList) + '/' + newFilename + \" '%}\"\n self.djangoLine.setText(djangoView)\n\n # Log the result\n with open(self.database, \"a\") as f:\n historyNote = ','.join([newFilename, djangoView, str(self.NOW.year), str(self.NOW.month),\n str(self.NOW.day), str(self.NOW.hour), str(self.NOW.minute)])+'\\n'\n f.write(historyNote)\n else:\n QtGui.QMessageBox.warning(self, \"Link\", \"You enter a wrong link!\")\n\n def open_next(self):\n # Overridable method for transitions\n return History()\n\n def quit_app(self):\n # Overridable method\n return QtCore.QCoreApplication.instance().quit\n\n\nclass History(DjangoImages):\n ''' Class for comfortable work with logs. Ih inherits methods only because design of this class is so different.\n Sort and filter djangonized images (RegEx supports). All info in table can be copied.\n\n '''\n def __init__(self):\n super(DjangoImages, self).__init__() # Closed from parent constructor attributes\n self.proxyModel = SortFilterHistory(self) # Technical class (PyQt template)\n self.proxyModel.setDynamicSortFilter(True)\n\n # Buttons\n self.openButton = self.create_button('Folder', self.open_folder, tooltip=\"Open folder with djangonized images\")\n self.quitButton = self.create_button('Quit', self.quit_app(), tooltip=\"Close All Windows\")\n self.emptyLabel = QtGui.QLabel() # Filler for GridLayout with buttons\n\n self.filterPatternLineEdit =self.create_line_edit() # Search line\n self.filterPatternLabel = QtGui.QLabel(\"Filter pattern:\")\n self.filterPatternLabel.setBuddy(self.filterPatternLineEdit)\n\n self.filterSyntaxComboBox = QtGui.QComboBox() # Search modes\n self.filterSyntaxComboBox.addItem(\"Normal\", QtCore.QRegExp.FixedString) # 1st (Default)\n self.filterSyntaxComboBox.addItem(\"RegEx\", QtCore.QRegExp.RegExp) # 2nd (Switch with Normal to make it default)\n self.filterSyntaxComboBox.setToolTip(\"Search mode\")\n\n self.fromDateEdit = QtGui.QDateEdit()\n self.fromDateEdit.setDate(QtCore.QDate(2016, 1, 1))\n self.fromDateEdit.setCalendarPopup(True) # True calendar\n self.fromLabel = QtGui.QLabel(\"From:\")\n self.fromLabel.setBuddy(self.fromDateEdit)\n\n self.toDateEdit = QtGui.QDateEdit()\n self.toDateEdit.setDate(QtCore.QDate(2026, 1, 1))\n self.toDateEdit.setCalendarPopup(True)\n self.toLabel = QtGui.QLabel(\"To:\")\n self.toLabel.setBuddy(self.toDateEdit)\n\n self.filterPatternLineEdit.textChanged.connect(self.text_filter_changed)\n self.filterSyntaxComboBox.currentIndexChanged.connect(self.text_filter_changed)\n self.fromDateEdit.dateChanged.connect(self.date_filter_changed)\n self.toDateEdit.dateChanged.connect(self.date_filter_changed)\n\n def __call__(self):\n self.proxyView = QtGui.QTreeView() # Table view\n self.proxyView.setRootIsDecorated(False)\n self.proxyView.setAlternatingRowColors(True)\n self.proxyView.setModel(self.proxyModel) # Setting of table for the view\n self.proxyView.setSortingEnabled(True)\n self.proxyView.sortByColumn(1, QtCore.Qt.AscendingOrder)\n self.proxyModel.setSourceModel(self.create_log_table()) # Setting of table for the window\n\n self.proxyView.setColumnWidth(0, 75)\n self.proxyView.setColumnWidth(1, 240)\n\n self.text_filter_changed()\n self.date_filter_changed()\n\n proxyLayout = QtGui.QGridLayout()\n proxyLayout.addWidget(self.proxyView, 0, 0, 1, 3)\n proxyLayout.addWidget(self.filterPatternLabel, 1, 0)\n proxyLayout.addWidget(self.filterPatternLineEdit, 1, 1)\n proxyLayout.addWidget(self.filterSyntaxComboBox, 1, 2)\n proxyLayout.addWidget(self.fromLabel, 3, 0)\n proxyLayout.addWidget(self.fromDateEdit, 3, 1, 1, 2)\n proxyLayout.addWidget(self.toLabel, 4, 0)\n proxyLayout.addWidget(self.toDateEdit, 4, 1, 1, 2)\n proxyGroupBox = QtGui.QGroupBox(\"Sort/Filter Links\")\n proxyGroupBox.setLayout(proxyLayout)\n\n buttonsLayout = QtGui.QHBoxLayout()\n buttonsLayout.addWidget(self.openButton, 1)\n buttonsLayout.addWidget(self.emptyLabel, 3)\n buttonsLayout.addWidget(self.quitButton, 1)\n buttonsGroupBox = QtGui.QGroupBox(\"Control buttons\")\n buttonsGroupBox.setLayout(buttonsLayout)\n\n mainLayout = QtGui.QVBoxLayout()\n mainLayout.addWidget(proxyGroupBox)\n mainLayout.addWidget(buttonsGroupBox)\n\n self.setLayout(mainLayout)\n self.setWindowTitle(\"History of djangonized images\")\n self.resize(*self.size)\n self.move(*self.position)\n self.show()\n\n def text_filter_changed(self):\n # Filtering by filter patterns (Normal, RegEx)\n syntax = QtCore.QRegExp.PatternSyntax(\n self.filterSyntaxComboBox.itemData(\n self.filterSyntaxComboBox.currentIndex()))\n\n regExp = QtCore.QRegExp(self.filterPatternLineEdit.text(), True, syntax)\n self.proxyModel.setFilterRegExp(regExp)\n\n def date_filter_changed(self):\n # Filtering by dates\n self.proxyModel.set_filter_minimum_date(self.fromDateEdit.date())\n self.proxyModel.set_filter_maximum_date(self.toDateEdit.date())\n\n def add_log(self,table, name, link, date):\n # Fill row of the table\n table.insertRow(0)\n table.setData(table.index(0, 0), name)\n table.setData(table.index(0, 1), link)\n table.setData(table.index(0, 2), date)\n\n def create_log_table(self):\n # Create table and fill it by log data\n table = QtGui.QStandardItemModel(0, 3, self)\n\n table.setHeaderData(0, QtCore.Qt.Horizontal, \"Name\")\n table.setHeaderData(1, QtCore.Qt.Horizontal, \"Djangonized link\")\n table.setHeaderData(2, QtCore.Qt.Horizontal, \"Date\")\n\n # Fill the table\n try:\n with open(self.database) as f:\n lines = f.readlines()\n for line in lines:\n line = line.split(',')\n self.add_log(table, line[0], line[1], #name,link\n QtCore.QDateTime(QtCore.QDate(int(line[2]), int(line[3]), int(line[4])), # Date\n QtCore.QTime(int(line[5]), int(line[6])))) # Time\n except FileNotFoundError:\n QtGui.QMessageBox.information(self, \"HistoryError\",\n \"You haven't any history\")\n raise\n return table\n\n def open_folder(self):\n # Open folder with djangonized images\n return os.system(QtGui.QFileDialog().getOpenFileName(self,'Open Dj-folder', self.PATH))\n\n\nclass DjangoFiles(DjangoImages):\n ''' Class simplify work with website templates for django programmers.\n Djangonize links in CSS and HTML files, save copy of djangonized files in format [0-9]oldname, return name to user.\n\n '''\n def __init__(self):\n super(DjangoFiles, self).__init__()\n # Buttons (djangonizeButton, openButton and quitButton are inherited)\n self.djangonizeButton.setToolTip(\"Make a copy where pattern is replaced by django links, return name of copy\")\n self.browseButton = self.create_button(\"Browse...\", self.browse)\n self.openButton.setText('Open It!')\n self.openButton.setToolTip(\"Open djangonized file by default program\")\n\n # Lines\n self.regexLine = self.create_line_edit()\n self.regexLine.setText(\"Choose CSS or HTML file!\")\n self.djangonizeLine = self.create_line_edit() # Line for djangonized filename returning\n self.fileComboBox = self.create_combo_box(QtCore.QDir.currentPath())\n\n def __call__(self):\n fileLayout = QtGui.QHBoxLayout()\n fileLayout.addWidget(self.fileComboBox,4)\n fileLayout.addWidget(self.browseButton,1)\n fileGroupBox = QtGui.QGroupBox(\"Browse file:\")\n fileGroupBox.setLayout(fileLayout)\n\n regexLayout = QtGui.QVBoxLayout()\n regexLayout.addWidget(self.regexLine)\n regexGroupBox = QtGui.QGroupBox(\"Pattern for replacement:\")\n regexGroupBox.setLayout(regexLayout)\n\n buttonsLayout = QtGui.QGridLayout()\n buttonsLayout.addWidget(self.djangonizeButton, 0, 2, 1, 3)\n buttonsLayout.addWidget(self.djangonizeLine, 1, 2, 1, 3)\n buttonsLayout.addWidget(self.openButton, 2, 0)\n buttonsLayout.addWidget(self.quitButton, 2, 5)\n buttonsGroupBox = QtGui.QGroupBox(\"Djangonization and Control buttons:\")\n buttonsGroupBox.setLayout(buttonsLayout)\n\n mainLayout = QtGui.QVBoxLayout()\n mainLayout.addWidget(fileGroupBox)\n mainLayout.addWidget(regexGroupBox)\n mainLayout.addWidget(buttonsGroupBox)\n self.setLayout(mainLayout)\n\n self.setWindowTitle(\"Djangonize CSS or HTML file\")\n self.resize(*self.size)\n self.move(*self.position)\n self.show()\n\n def create_combo_box(self, text=\"\"):\n # Additional element constructor\n comboBox = QtGui.QComboBox()\n comboBox.setEditable(True)\n comboBox.addItem(text)\n return comboBox\n\n def browse(self):\n # Browse file and choose a default RegEx according to file extension\n openedFile = QtGui.QFileDialog.getOpenFileName(self, \"Find CSS or HTML\", QtCore.QDir.currentPath())\n\n if openedFile:\n if self.fileComboBox.findText(openedFile) == -1:\n self.fileComboBox.addItem(openedFile)\n\n self.fileComboBox.setCurrentIndex(self.fileComboBox.findText(openedFile))\n\n filePath = str(self.fileComboBox.currentText())\n\n if filePath[-3:] == \"css\":\n self.regexLine.setText(self.defaultCSS)\n elif filePath[-3:] == \"tml\":\n self.regexLine.setText(self.defaultHTML)\n else:\n self.regexLine.setText(\"Wrong file. Choose CSS or HTML!\")\n\n def djangonize(self):\n # Method which make files more djangonized! (Overridden method for djangonizeButton)\n filePath = str(self.fileComboBox.currentText()) # Path to file from fileComboBox\n dirList = self.dir_list() # DjangonizeImage inherited method which returns folders before static folder\n newFilename = str(randint(0, 9)) + os.path.basename(filePath) # Filename where changes will be saved\n\n if filePath[-3:] == \"css\":\n djPath = '../' + '/'.join(dirList) + '/' # Path to images in django project\n nonDjPath = str(self.regexLine.displayText()) # Path to images in CSS for replacement\n\n if os.name == 'nt':\n with open(os.path.dirname(filePath) + '/' + newFilename, 'w') as f: # open new\n content = open(filePath).read() # copy data from old\n f.write('{% load staticfiles %}\\n') # connect static files to CSS\n f.write(re.sub(nonDjPath, djPath, content)) # replace line and write in new\n self.djangonizeLine.setText(\"New filename: \" + newFilename) # return name of new\n\n else:\n with open(os.path.dirname(filePath) + '\\\\' + newFilename, 'w') as f:\n content = open(filePath).read()\n f.write('{% load staticfiles %}\\n')\n f.write(re.sub(nonDjPath, djPath, content))\n self.djangonizeLine.setText(\"New filename: \" + newFilename)\n\n elif filePath[-3:] == \"tml\":\n\n djPath = \"src=\\\"{% static '\" + '/'.join(dirList) + '/' + \"\\g<1>\" + \"' %}\\\"\" # Path to django-project images\n nonDjPath = str(self.regexLine.displayText()) # Path in CSS or HTML for replace\n\n if os.name == 'nt':\n with open(os.path.dirname(filePath) + '/' + newFilename, 'w') as f: # Create a new file [0-9]oldname\n content = open(filePath).read() # Open old file and read it content\n f.write(re.sub(nonDjPath, djPath, content)) # Replacement is here\n self.djangonizeLine.setText(\"New filename: \" + newFilename) # Info for user\n\n else:\n with open(os.path.dirname(filePath) + '\\\\' + newFilename, 'w') as f:\n content = open(filePath).read()\n f.write(re.sub(nonDjPath, djPath, content))\n self.djangonizeLine.setText(\"New filename: \" + newFilename)\n else:\n self.djangonizeLine.setText(\"Wrong file. Choose CSS or HTML!\") # When not CSS or not HTML file is browsed\n\n def open_file(self):\n # This method open the folder with djangonized files in os explorer and allow choose file for opening\n # It should be opened as object not as function\n if re.match(r'New\\sfilename', self.djangonizeLine.displayText()):\n os.system(QtGui.QFileDialog().getOpenFileName(self, 'Open Dj-file', self.fileComboBox.currentText()))\n else:\n QtGui.QMessageBox.warning(self,'OpenError', 'Djangonize file before opening')\n\n def open_next(self):\n # Overridden method for openButton\n return self.open_file\n\n\nclass MainMenu(DjangoFiles):\n \"\"\" Start window of the application\n Call the components of the application when buttons is clicking.\n\n \"\"\"\n def __init__(self):\n super(MainMenu, self).__init__()\n # Buttons (djangonizeButton, openButton, browseButton and quitButton are inherited)\n self.djangonizeButton.setText('Djangonize image from Web')\n self.djangonizeButton.clicked.connect(self.djangonize())\n self.djangonizeButton.setToolTip(None)\n\n self.openButton.setText('History of djangonized images')\n self.openButton.setToolTip(None)\n\n self.browseButton.setText('Djangonize CSS or HTML file')\n self.browseButton.clicked.connect(self.browse())\n\n self.restartButton = self.create_button('R', self.restart,\n tooltip=\"Temporary solution for issue with repainting of GroupBoxes\")\n\n\n # Label\n self.mainLabel = self.create_label(\"Main menu\", 16)\n\n # Location of components (Absolute). Here the problem with window resizing is solved.\n self.mainLabel.setGeometry(QtCore.QRect(self.size[0]*0.43, self.size[1]*0.03, # Location (horizontal,vertical)\n self.size[0]*0.3, self.size[1]*0.15)) # Size of element (width, height)\n self.djangonizeButton.setGeometry(QtCore.QRect(self.size[0]*0.1, self.size[1]*0.20,\n self.size[0]*0.48, self.size[1]*0.17))\n self.openButton.setGeometry(QtCore.QRect(self.size[0] * 0.1, self.size[1] * 0.4,\n self.size[0] * 0.48, self.size[1] * 0.17))\n self.browseButton.setGeometry(QtCore.QRect(self.size[0]*0.1, self.size[1]*0.6,\n self.size[0]*0.48, self.size[1]*0.17))\n self.quitButton.setGeometry(QtCore.QRect(self.size[0]*0.75, self.size[1]*0.85,\n self.size[0]*0.2, self.size[1]*0.1))\n self.restartButton.setGeometry(QtCore.QRect(self.size[0]*0.01, self.size[1]*0.01,\n self.size[0]*0.05, self.size[1]*0.05))\n\n self.setWindowTitle('DjangonizeIt! - Main menu')\n self.resize(*self.size)\n self.move(*self.position)\n\n # Overridden methods\n def djangonize(self):\n return DjangoImages()\n\n def browse(self):\n return DjangoFiles()\n\n def open_next(self):\n return History()\n\n def restart(self):\n # start new, sleep old\n return os.system(self.FILE)\n\nclass SortFilterHistory(QtGui.QSortFilterProxyModel):\n ''' Technical class for the History class. (is taken from pyQt templates)\n Example of the class is used for representing (filtering) of table content by date range.\n\n '''\n def __init__(self, parent=None):\n super(SortFilterHistory, self).__init__(parent)\n\n self.minDate = QtCore.QDate()\n self.maxDate = QtCore.QDate()\n\n def set_filter_minimum_date(self, date):\n self.minDate = date\n self.invalidateFilter()\n\n def filter_minimum_date(self):\n return self.minDate\n\n def set_filter_maximum_date(self, date):\n self.maxDate = date\n self.invalidateFilter()\n\n def filter_maximum_date(self):\n return self.maxDate\n\n def filter_accepts_row(self, sourceRow, sourceParent):\n index0 = self.sourceModel().index(sourceRow, 0, sourceParent)\n index1 = self.sourceModel().index(sourceRow, 1, sourceParent)\n index2 = self.sourceModel().index(sourceRow, 2, sourceParent)\n\n return ((self.filterRegExp().indexIn(self.sourceModel().data(index0)) >= 0\n or self.filterRegExp().indexIn(self.sourceModel().data(index1)) >= 0)\n and self.date_in_range(self.sourceModel().data(index2)))\n\n def date_in_range(self, date):\n if isinstance(date, QtCore.QDateTime):\n date = date.date()\n\n return ((not self.minDate.isValid() or date >= self.minDate)\n and (not self.maxDate.isValid() or date <= self.maxDate))\n\n\nif __name__ == '__main__':\n\n app = QtGui.QApplication(sys.argv)\n menu = MainMenu()\n menu.show()\n sys.exit(app.exec_())","sub_path":"djangonizeit.pyw","file_name":"djangonizeit.pyw","file_ext":"pyw","file_size_in_byte":31464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"366821097","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nfrom sklearn.covariance import EmpiricalCovariance, MinCovDet\n\nfrom fredapi import Fred\nfred = Fred(api_key='98d7e668ce51c2997660ab73367c689a')\n\nfrom utils.misc import get_fred_asof_history, get_fred_asof\n\n# # 0. Theoretical background\n\n# $$r_t^{eqy} = E[r_t^{eqy}] + \\beta_1^{eqy} INFL_t+\\beta_2^{eqy} GR_t+ \\beta_3^{eqy} FS_t + \\alpha_t^{eqy}$$\n# $$r_t^{ust} = E[r_t^{ust}] + \\beta_1^{ust} INFL_t+\\beta_2^{ust} GR_t+ \\beta_3^{ust} FS_t + \\alpha_t^{ust}$$\n# $$\\vdots$$\n# $$r_t^{fxcs} = E[r_t^{fxcs}] + \\beta_1^{fxcs} INFL_t+\\beta_2^{fxcs} GR_t+ \\beta_3^{fxcs} FS_t + \\alpha_t^{fxcs}$$\n\n# , where\n# - $INFL_t$ stands for an *inflation* macro-factor return at time t. Likewise, $GR$ for *growth* and $FS$ for *finantial stress* factors.\n# - $r_t^{eqy}$ is an *excess* return of global equity markets at time t as one of the base assets. T-Bill 1M is used for the excess return calculation. A risk free return such as $r_f$ is omiited for simplicity.\n# - $ust$ stands for U.S 10Yr Treasury, and the rest of notation should be the same as that of $eqy$. The same naming rule applies for the rest of base assets in denoting $r_t^{asset}$.\n# - A full list of base assets used in this model : Equities($eqy$), Treasuries($ust$), Credit($cre$), Inflation-Linked Bonds($ilb$), Gold(gold), Industrial Metals($inm$), Energy commodity($eng$), U.S. Dollar($dxy$), Commodity vs safe haven currencies ($fxcs$). Abbreviations in ().\n# - $E[\\cdot]$ is an expected excess return.\n# - $\\beta_{\\#}^{instrument}$ is a factor beta, or factor loading, for that $instrument$. This value is the **same across all periods** of time being modeled. Therefore, there is no subscript $t$. Instead, we have a digit subscript 1 for $INFL$ beta, 2 for $GR$ beta and 3 for $FS$ beta.\n# - e.g. $\\beta_1^{eqy}$ is a sensitivy of Equities to $INFL$ factor.\n# - $E[\\alpha^{asset}] = 0$ for all periods of time being modeled.\n\n# # 1. Load datasets\n\n# #### Set data frequency\n\n# In[2]:\n\n\n_freq = 'W'\n\n\n# ## 1) Bloomberg (bbg)\n# - For data of more recent years.\n\n# In[3]:\n\n\nif _freq == 'M':\n bbg_filename = '../data/raw/bbg_M.log'\nelif _freq == 'W':\n bbg_filename = '../data/raw/bbg_W.log'\n\ndf_bbg = pd.read_csv(bbg_filename, header=2, parse_dates=['date'])\ndf_bbg = df_bbg.set_index(['date'], drop=True)\n\n\n# In[4]:\n\n\nprint('Data loaded from {}: {} rows, {} columns.'.format(bbg_filename, df_bbg.shape[0], df_bbg.shape[1]))\n\n\n# In[5]:\n\n\nbbg_daily_filename = '../data/raw/bbg_D.log'\ndf_bbg_d = pd.read_csv(bbg_daily_filename,\n header=2,\n index_col='date',\n parse_dates=['date'])\n\nprint('Data loaded from {}: {} rows, {} columns.'.format(bbg_daily_filename, df_bbg_d.shape[0], df_bbg_d.shape[1]))\n\n\n# Some data are only downloadable at either monthly or daily frequency using BDH in Excel, not weekly. So we do:\n# - If `_freq` is 'W', feed `CLI_USA` and others in `df_bbg_d` into df_bbg with a proper frequency conversion.\n\n# In[6]:\n\n\nif _freq == 'W':\n df_d2w = df_bbg_d[df_bbg_d.columns.difference(['FF_rate', 'UST10YR_yld'])]\n df_d2w = df_d2w.set_index(df_d2w.index.to_period('D'))\n df_d2w = df_d2w.resample('W-FRI', kind='period').last()\n df_d2w = df_d2w.set_index(df_d2w.index.to_timestamp(how='E').strftime('%Y-%m-%d'))\n df_bbg.drop(df_bbg_d.columns.difference(['FF_rate', 'UST10YR_yld']), axis=1, inplace=True)\n df_bbg = df_bbg.merge(df_d2w, how='left', left_index=True, right_index=True)\n\n\n# ## 2) Global Financial Data (gfd)\n# - For data of older years; since 1850.\n\n# In[7]:\n\n\nif _freq == 'M':\n gfd_filename = '../data/raw/gfd_M.log'\nelif _freq == 'W':\n gfd_filename = '../data/raw/gfd_W.log'\n\ndf_gfd = pd.read_csv(gfd_filename, header=0, parse_dates=['Date'])\ndf_gfd = df_gfd.rename(columns={'CHFUSD': 'CHFUSD_rate', 'GBPUSD':'GBPUSD_rate'})\n\n\n# In[8]:\n\n\n# df_gfd.columns\n\n\n# In[9]:\n\n\n# df_gfd.shape\n\nprint('Data loaded from {}: {} rows, {} columns.'.format(gfd_filename, df_gfd.shape[0], df_gfd.shape[1]))\n\n\n# We pivot this table.\n\n# In[10]:\n\n\ndf_gfd = pd.pivot_table(df_gfd, index=['Date'], columns=['Ticker'])\n\n# Drop one of the top multi-index column, namely, \"Close\"\ndf_gfd.columns = df_gfd.columns.droplevel(0)\n\n\n# Add a prefix `_rate` to each FX rate column name.\n\n# In[11]:\n\n\ndf_gfd.columns = [col + \"_rate\" if col.find(\"USD\") >= 0 else col for col in df_gfd.columns]\n\n\n# In[12]:\n\n\ndf_gfd.shape\n\n\n# In[13]:\n\n\ndf_gfd\n\n\n# In[14]:\n\n\n# gfd_weekly_filename = '../../data/raw/gfd_ust10.log'\n# df_gfd_w = pd.read_csv(gfd_filename, header=0, parse_dates=['Date'])\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# ## 3) From Chernov(2012), real yield data (ry)\n# - http://personal.lse.ac.uk/muellerp/RealYieldAOT5.xls\n\n# In[15]:\n\n\nry_filename = '../data/raw/base_assets_realyield_aot5.log'\n\ndf_ry = pd.read_csv(ry_filename, header=2)\n\n\n# In[16]:\n\n\nsplit_df = df_ry[\"Dates (qrts)\"].astype(str).str.split('.', expand=True)\nsplit_df.columns = [f\"date_{id_}\" for id_ in range(len(split_df.columns))]\ndf_ry = pd.merge(df_ry, split_df, how=\"left\", left_index=True, right_index=True)\n\n# Create `date` index\ndf_ry['date'] = pd.to_datetime({\n 'year': df_ry['date_0'],\n 'month': df_ry['date_1'].astype(int, errors='raise')*3,\n 'day': 1\n})\n\ndf_ry = df_ry.set_index('date', drop=True)\ndf_ry = df_ry.drop(['Dates (qrts)', 'date_0', 'date_1', '3m', '6m', '1y', '2y', '3y', '5y'], axis=1)\n\n\n# In[17]:\n\n\n# df_ry\nprint('Data loaded from {}: {} rows, {} columns.'.format(ry_filename, df_ry.shape[0], df_ry.shape[1]))\n\n\n# Change column names.\n\n# In[18]:\n\n\ndf_ry.columns = ['REAL7Y_yld', 'REAL10Y_yld']\n\n\n# ## 4) Winan Real Estate Index (WIREI)\n# - No longer have access to this data. Don't lose it.\n# - `WIREI`: The WIREI (Patent Pending 11/670,914) tracks new U.S. home prices, starting from the year 1830. Its unique approach involved rescaling and combining several well-known government studies of U.S. new home prices into a continuous data set without the “gapping” and time lag problems found in other studies. The WIREI has several sub-indices: 1. Sales since 1962, 2. Inflation since 1932, 3. Home sizes (i.e., average square feet) since 1973 and 4. Geographic regions (Northeast, Midwest, South, West) since 1975.\n\n# In[19]:\n\n\nwirei_filename = '../data/raw/base_assets_wirei_gfd.log'\n\ndf_wirei = pd.read_csv(wirei_filename, header=0, parse_dates=['date'])\n\n\n# In[20]:\n\n\ndf_wirei = df_wirei.drop(['WIREI-open', 'WIREI-vol'], axis=1)\ndf_wirei = df_wirei.set_index('date', drop=True)\ndf_wirei.columns = ['WIREI']\n\n\n# In[21]:\n\n# np.log(df_wirei/df_wirei.shift(1)).plot()\nprint('Data loaded from {}: {} rows, {} columns.'.format(wirei_filename, df_wirei.shape[0], df_wirei.shape[1]))\n\n\n# In[ ]:\n\n\n\nprint('Data loading has ended. Preprocessing has started.')\n\n# # 2. Preprocessing\n\n# ## 1) Merge datasets\n# - `df_gfd`, `df_bbg`, `df_ry`, `df_wirei`\n# - Changing `date` formats to properly join\n\n# #### Change each index type to `PeriodIndex` with `_freq` frequency\n\n# In[22]:\n\n\nif _freq == 'M':\n gfd_index = pd.to_datetime(df_gfd.index).to_period(_freq)\n bbg_index = pd.to_datetime(df_bbg.index).to_period(_freq)\n ry_index = pd.to_datetime(df_ry.index).to_period(_freq)\n wirei_index = pd.to_datetime(df_wirei.index).to_period(_freq)\nelif _freq == 'W':\n gfd_index = pd.to_datetime(df_gfd.index - pd.Timedelta('1 days')).to_period('W-FRI')\n bbg_index = pd.to_datetime(df_bbg.index).to_period('W-FRI') \n ry_index = pd.to_datetime(df_ry.index).to_period('M').asfreq('W-FRI', how='E')\n wirei_index = pd.to_datetime(df_wirei.index).to_period('M').asfreq('W-FRI', how='E')\n\n\n# In[23]:\n\n\ndf_bbg = df_bbg.set_index(bbg_index, drop=True)\ndf_gfd = df_gfd.set_index(gfd_index, drop=True)\ndf_ry = df_ry.set_index(ry_index, drop=True)\ndf_wirei = df_wirei.set_index(wirei_index, drop=True)\n\n\n# #### Joining all data sets to create `df`\n\n# In[24]:\n\n\ndf = pd.merge(df_bbg, df_gfd, how='outer', left_index=True, right_index=True, suffixes=('_bbg', '_gfd'))\ndf = pd.merge(df, df_ry, how='left', left_index=True, right_index=True, suffixes=('', '_ry'))\ndf = pd.merge(df, df_wirei, how='left', left_index=True, right_index=True, suffixes=('', '_wirei'))\n\n\n# - `Riskfree_rate`:\n# - July 1954 to present: Federal funds rates (df_bbg.**FF_rate**, daily)\n# - February 22, 1934 to June 1954: USA Government 3-Month T-bills Auction Rate (**ITUSAA3W**, weekly)\n# - January 1920 to February 1934: USA Government 3-Month T-Bills Constant Maturity Yield (**ITUSA3CMD**, monthly)\n\n# In[25]:\n\ndf['Rf_rate'] = df.FF_rate\ndf.Rf_rate.fillna(df.ITUSAA3W, inplace=True)\ndf.Rf_rate.fillna(df.ITUSA3CMD, inplace=True)\n\n\n# Data range is:\n\n# In[26]:\n\nprint('We have data from {} to {}'.format(df.index.min(), df.index.max()))\n\n\n# To rename the index name, we set *inplace=True* in order for the data frame to retain all its properties.\n# - https://stackoverflow.com/questions/19851005/rename-pandas-dataframe-index\n\n# In[27]:\n\n\ndf.index.rename('date', inplace=True)\n\n\n# In[28]:\n\n\n# df.columns\n\n\n# In[29]:\n\n\ndf.TBILL3M_yld.dropna()\n\n\n# #### Drop rows where their data are invalid due to erroneous forward-fill.\n\n# In[30]:\n\n\nif _freq == 'M':\n this_month = pd.Timestamp.today().to_period(_freq)\n df = df.loc[~(df.index==this_month), :]\nelif _freq == 'W':\n this_week = pd.Timestamp.today().to_period('W-FRI')\n df = df.loc[~(df.index==this_week), :]\n\n\n# #### For each column, we fill nan rows with the lastest observation from the first valid row to the last valid row.\n# - We leave nan as it is for the rest of rows.\n\n# In[31]:\n\n\ndf = df.apply(lambda col: col.loc[col.first_valid_index():col.last_valid_index()].fillna(method='ffill'))\n\n\n# In[32]:\n\n\n# df\nprint('Asset prices have been all cleaned and merged. A few last data points are: ')\nprint(df.tail())\n\n\n# In[ ]:\n\n\n\n\n\n# # 3. Generate `Base Asset` indices\n# - Data from **1921** to present. `FXCS` is the shortest data series and it starts from 1921.\n\n# #### `bf` is a DataFrame of base asset indices where its columns are base asset indices.\n\n# In[33]:\n\n\nbf = pd.DataFrame(index = df.index)\nprint('Calculating base asset prices...')\n\n\n# #### Index names in `gray boxes`\n\n# ### 1) Equities\n\n# `DMEQ` (Equities): Log returns of an equity index.\n# - Since January 1970, **DMEQ_idx**: MSCI World in USD.\n# - Before that date, **_SPXD**: S&P (500)\n\n# #### Take log returns\n\n# In[34]:\n\n\n# DMEQ_etf (USRT US) changed its tracking index to the desired one on November 3, 2016.\ndf['DMEQ_recent'] = np.log(df.DMEQ_etf/df.DMEQ_etf.shift(1))\ndf['DMEQ_older'] = np.log(df.DMEQ_idx/df.DMEQ_idx.shift(1))\ndf['DMEQ_oldest'] = np.log(df._SPXD/df._SPXD.shift(1))\n\n\n# #### Merge two columns to create `DMEQ`\n\n# In[35]:\n\n\nbf['DMEQ'] = df.DMEQ_recent.fillna(df.DMEQ_older).fillna(df.DMEQ_oldest)\n\n\n# `Rf` is a series of `_freq` risk-free log returns.\n# - January 1993 to present **TBILL_idx**: Bloomberg Barclays US T-Bills 1-3 Months TR Index Value Unhedged\n# - Before that date, **TRUSABID**: GFD Indices USA Total Return Daily T-Bill Index\n\n# In[36]:\n\n\ndf['Rf_recent'] = np.log(df.TBILL_idx/df.TBILL_idx.shift(1))\ndf['Rf_older'] = np.log(df.TRUSABID/df.TRUSABID.shift(1))\n\n\n# In[37]:\n\n\ndf['Rf_ret'] = df.Rf_recent.fillna(df.Rf_older)\n\n\n# In[38]:\n\n\nbf['DMEQ'] = bf['DMEQ'] - df.Rf_ret\n\n\n# ### 2) Treasuries\n\n# `UST` (Treasuries): Log returns of a U.S. government bond index.\n# - Since January 2005, **UST_idx**: ICE U.S. Treasury 7-10 Year TR Index.\n# - We choose this index instead of Blomberg Barclays US Treasury Total Return Unhedged USD Index (LUATTRUU) because **IEF US** tracks the index.\n# - YAS Modified duration of **UST_idx** is 7.547. Matury is 8.46 as of April 28, 2019.\n# - Before that date, **TRUSG10M**: GFD Indices USA 10-year Government Bond Total Return Index.\n\n# In[39]:\n\n\ndf['UST_recent'] = np.log(df.UST_etf/df.UST_etf.shift(1))\ndf['UST_older'] = np.log(df.UST_idx/df.UST_idx.shift(1))\ndf['UST_oldest'] = np.log(df.TRUSG10M/df.TRUSG10M.shift(1))\nbf['UST'] = df.UST_recent.fillna(df.UST_older).fillna(df.UST_oldest) - df.Rf_ret\n\n\n# ### 3) Credit\n\n# `CRE` (Credit): Log returns of Baa-rating index - log returns of Aaa-rating index.\n# - February 1973 to present: **CRE_Baa_idx**: Bloomberg Barclays U.S. Credit Baa index vs. **CRE_Aaa_idx**: Bloomberg Barclays U.S. Credit Aaa index.\n# - Before this date: **_DJCBPD**: Dow Jones Corporate Bond Price Index (new) vs. **TRUSACOM**: GFD Indices USA Total Return AAA Corporate Bond Index.\n# - In contrast to its name, **_DJCBPD** is a total return index.\n\n# In[40]:\n\n\ndf['CRE_recent'] = np.log(df.CRE_Baa_idx/df.CRE_Baa_idx.shift(1)) - np.log(df.CRE_Aaa_idx/df.CRE_Aaa_idx.shift(1))\ndf['CRE_older'] = np.log(df._DJCBPD/df._DJCBPD.shift(1)) - np.log(df.TRUSACOM/df.TRUSACOM.shift(1))\nbf['CRE'] = df.CRE_recent.fillna(df.CRE_older) - df.Rf_ret\n\n\n# ### 4) Inlfation-Linked Bond\n\n# `ILB` (Inflation-Linked Bonds): Real log returns. - Nominal log returns. That is, `ILB` is a series of outperformances of TIPS against their U.S. Treasury counterparts on the same maturity.\n# - df.`ILB_recent`: January 2003 to present. **TIPS_idx**: Log returns of Bloomberg Barclays U.S. TIPS (\"*real return*\") - **UST_idx** Bloomberg Barclays U.S. Treasury Total Return Unhedged USD (\"*nominal return*\")\n# - We don't use TIPS returns for a period of 1997 to 2002, when TIPS existed, because their trading volumes are low, lowering their accruracy as real returns.\n# - df.`ILB_older`: Before that date. `REALBOND8Y_logreturn`: *Derived* real log returns - **UST_idx** Bloomberg Barclays U.S. Treasury Total Return Unhedged USD.\n\n# In[ ]:\n\n\n\n\n\n# `ILB` (Inflation-Linked Bonds): Log returns of a U.S. government bonx index `UST` - log returns of TIPS on the same maturity **TIPS_idx**. Conceptually it's returns derived from *breakeven inflation rates*. Since a nominal return can be decomposed as:\n# $$\n# \\begin{align}\n# r^{Nominal} &= r^{Real} + r^{Expected\\ inflation} + r^{Inflation-risk\\ premium} + r^{Liquidity-risk\\ premium} \\\\\n# &= r^{Real} + r^{Breakeven\\ inflation} \\\\\n# \\Leftrightarrow r^{Breakeven\\ inflation} &= r^{Nominal} - r^{Real}\n# \\end{align}\n# $$\n# , where $r^{Nominal}$ is a 10-year Treasury yield and $r^{Real}$ is a 10-year TIPS yield. If this equation holds, owning 10-year Treasury and 10-year TIPS would be indifferent assuming an average of CPI over the next 10 years equals to $r^{Breakeven\\ inflation}$.\n# \n# By defining `ILB` as above, we can say that the higher `ILB`, the greater a realized CPI than the current $r^{Breakeven\\ inflation}$ over 10 years. Keep in mind that `ILB` is a series of ***return*** differences, not rate differences.\n# - A breakeven inflation rate (**BEI**) is believed to be a leading indicator of CPI. Since we will get an `ILB` exposure through TIPS, we are assuming: $$\\textrm{ILB} \\propto \\textrm{BEI}.$$\n# Therefore, we define, again: `ILB` = rate of returns of nominal bonds - rate of returns of TIPS on the same maturity (8 years in this case because we choose to match it to an ETF duration).\n# - Since January 2003, **UST_idx** Bloomberg Barclays U.S. Treasury Total Return Unhedged USD (\"*nominal return*\") vs. **TIPS_idx**: Log returns of Bloomberg Barclays U.S. TIPS (\"*real return*\").\n# - We don't use TIPS returns for before this date because their trading volumes are low, lowering their accruracy as real returns.\n# - From January 1972 to December 2002, **UST_idx** Bloomberg Barclays U.S. Treasury Total Return Unhedged USD (\"*nominal return*\") vs. **derived** real bond returns `REALBOND8Y_logreturn`. We derive real bond returns using historical real yield stored in `df_ry`.\n# - Before that date, we use a centered moving average of CPI to approximate inflation forecasts, which in turns will be used as $r^{Expected\\ inflation}$, which is assumed to be equivalent to $r^{Breakeven\\ inflation}$, as discussed in Swinkels (2018). Now we can get real yields simply by taking the difference; $r^{Nominal} - r^{Breakeven\\ inflation}$. Finally, we calculate `ILB` from these replicated real yields as we do above.\n# - $r^{Breakeven\\ inflation}$ for this period is log returns of $$\\frac{1}{3}(CPI_{t-1}+CPI_{t}+CPI_{t+1})$$, where $CPI_t$ is CPUSAM/CPUSAM.shift(12)-1, **CPUSAM**: United States BLS Consumer Price *Index* Inflation Rate NSA. CPUSAM.shift(12): one-year ago; t: present; CPUSAM.shift(-12): one-year ahed.\n\n# #### (1) January 2002 to present\n\n# In[41]:\n\n\ndf['ILB_recent_etf'] = np.log(df.TIPS_etf/df.TIPS_etf.shift(1)) - bf.UST\ndf['ILB_recent_idx'] = np.log(df.TIPS_idx/df.TIPS_idx.shift(1)) - bf.UST\ndf['ILB_recent'] = df.ILB_recent_etf.fillna(df.ILB_recent_idx)\n\n\n# As discussed above, `ILB_recent` is defined since 2003. So, remove all data points before this date.\n\n# In[42]:\n\n\ndf.loc[df.index.year < 2002, 'ILB_recent'] = np.nan\n\n\n# #### (2) December 1852 ~ December 2002\n\n# i) Get **real yields** for this pre-2003 period first.\n# - We get a proxy of breakeven inflation rates (BEI) through a centered moving average of CPIs. Then we transform it to a series of real rates in log term.\n# - Real rates `real_yld` = Nominal bond rates - BEI = **_TNXD** CBOE 10-year US Government Bond Yield Index - `breakeven`\n# - Real rates in log term = ln(1 + `real_yld`)\n# \n# We will call those real rates `REAL8Y_logyld`. Its maturity is not really 8 years, but we choose to call it 8-year because we need 8-year real rates to match the maturity of TIPS ETF, an instrument which we end up with having in a portfolio.\n\n# In[43]:\n\n\nif _freq == 'M':\n one_yr = 12\n two_yrs = 24\nelif _freq == 'W':\n one_yr = 52\n two_yrs = 104\n\n\n# In[44]:\n\n\ndf['breakeven'] =((df.CPUSAM.shift(one_yr)/df.CPUSAM.shift(two_yrs)-1) + (df.CPUSAM/df.CPUSAM.shift(one_yr)-1) + (df.CPUSAM.shift(-one_yr)/df.CPUSAM-1))/3\ndf['real_yld'] = df._TNXD/100 - df.breakeven\n\n\n# In[45]:\n\n\n# df['real_yld'].plot()\n\n\n# ii) Real yields in log term for December 1852 to December 1971\n\n# In[46]:\n\n\ndf['REAL8Y_logyld'] = np.log(1+df.loc[df.index.year < 1972, 'real_yld'])\ndf['REAL7Y_logyld'] = np.log(1+df.loc[df.index.year < 1972, 'real_yld'])\n\n\n# In[47]:\n\n\n# df.loc[:, ['REAL8Y_logyld', 'REAL7Y_logyld']].plot()\n\n\n# iii) Real yields for a period of 1972 to 2002.\n# - We have already loaded 7-year and 10-year real yields in `REAL7Y_yld` and `REAL10Y_yld` from 1972 to 2002 on a quarterly basis.\n# - We want to generate 8-year real yields\n\n# `avg_diff_in_logyld` is for making 7-year real yield from 8-year one. It's arbitrary and no strong ground.\n\n# In[48]:\n\n\nreal7y_logyld = np.log(1+df.REAL7Y_yld)\nreal8y_logyld = np.log(1 + (2*df.REAL7Y_yld + df.REAL10Y_yld)/3)\navg_diff_in_logyld = np.average((real8y_logyld-real7y_logyld).dropna(how='any'))\n\n\n# In[49]:\n\n\nreal8y_logyld.dropna()\n\n\n# - Merge them into df.`REAL7Y_logyld` and df.`REAL8Y_logyld`.\n\n# In[50]:\n\n\ndf['REAL8Y_logyld'] = df.REAL8Y_logyld.fillna(real8y_logyld)\ndf['REAL7Y_logyld'] = df.REAL7Y_logyld.fillna(real7y_logyld)\n\n\n# For the pre-1972 period `REAL7Y_logyld`, we define it as the `real_yld` - an average difference between 8-year log yields and 7-year log yields both of which were from `df_ry`.\n\n# In[51]:\n\n\ndf['REAL7Y_logyld'] = df['REAL7Y_logyld'] - avg_diff_in_logyld\n\n\n# In[52]:\n\n\ndf.loc[:, ['REAL7Y_logyld', 'REAL8Y_logyld']].dropna()\n\n\n# iv) Derive **real bond returns**.\n# - This follows Swinkels (2018), \"Simulating historical inflation-linked bond returns.\"\n# \n\n# We set a duration `D` = 8 for all this calculation process to be in line with the modified duration of **TIP US** of 8.06 as of April 28, 2020. \n\n# In[53]:\n\n\nD = 8\ndf['REALBOND8Y_logreturn'] = (D*df.REAL8Y_logyld - (D-1)*df.REAL7Y_logyld.shift(-12))/12\n\n\n# #### *LIMITATION*: Derived real bond returns in blue show *much smaller volatility* than TIPS returns in orange.\n# - Check out how similar derived real bond returns and TIPS returns are.\n# - Derived returns are obviously less volatile, but they seem to well replicate TIPS returns.\n\n# In[54]:\n\n\ntips_return = np.log(df.TIPS_idx/df.TIPS_idx.shift(1))\ncompare_rr = pd.merge(df['REALBOND8Y_logreturn'], tips_return, how='inner', on='date')\n# compare_rr.loc[np.logical_and(compare_rr.index.year>1996, compare_rr.index.year<2002), :].plot()\n\n\n# Older derived real bond returns seem to be more volatile, though.\n\n# In[55]:\n\n\n#df.loc[df.index.year<1997, 'REALBOND8Y_logreturn'].plot()\n\n\n# In[56]:\n\n\ndf['ILB_older'] = df.REALBOND8Y_logreturn - bf.UST\n\n\n# In[57]:\n\n\nbf['ILB'] = df.ILB_recent.fillna(df.ILB_older) - df.Rf_ret\n\n\n# In[58]:\n\n\n#bf.ILB.plot()\n\n\n# ### 5) U.S. Dollar\n\n# `DXY` (Dollar Index): Log returns of the U.S. Dollar Index.\n# - Since February 1967, **DXY_idx**: U.S. Dollar Index\n# - Before that date, we use a *derived* DXY index by taking an equally-weighted basket of major currencies in that period except of USDEM due to the hyper-inflation in Germany. It's not G-10, but I'll call it G-10 anyway.\n\n# #### (1) February 1967 to present\n\n# In[59]:\n\ndf['DXY_recent_etf'] = np.log(df.DXY_etf/df.DXY_etf.shift(1))\ndf['DXY_recent_idx'] = np.log(df.DXY_idx/df.DXY_idx.shift(1))\ndf['DXY_recent'] = df.DXY_recent_etf.fillna(df.DXY_recent_idx)\n\n\n# #### (2) February 1850 to January 1967\n\n# In[60]:\n\n\ng10 = ['USDBEF_rate', 'USDCAD_rate_gfd', 'USDFRF_rate', 'USDITL_rate', 'USDJPY_rate_gfd', 'USDNLG_rate', 'USDSEK_rate', 'USDCHF_rate_gfd', 'GBPUSD_rate' ]\ndf_g10 = df[g10]\n\n\n# Take a reciprocal of `GBPUSD_rate` to match its direction to that of the rest currencies.\n\n# In[61]:\n\n\ndf_g10['USDGBP_rate'] = np.reciprocal(df_g10.GBPUSD_rate)\ndf_g10 = df_g10.drop(['GBPUSD_rate'], axis=1)\n\n\n# - Recent European rates are distorted due to Euro introduction in 1999.\n# - Doesn't matter. We don't use them since 1967 in this model.\n\n# In[62]:\n\n\n#df_g10.plot()\n\n\n# Compute the mean for the monthly log returns of all currencies.\n\n# In[63]:\n\n\ndf_g10 = df_g10.apply(lambda col: np.log(col/col.shift(1)))\n\n\n# #### `USDFRF_rate` normailzation\n# - French Frac was revalued in January 1960 with each new franc being worth 100 old francs, and it resulted in *discontinuity* in raw data.\n# - We normalize this particular month.\n\n# In[64]:\n\n\ndf_g10.loc['1960-01', 'USDFRF_rate'] = np.log(df.loc['1960-01', 'USDFRF_rate']*100/df.loc['1959-12', 'USDFRF_rate'])\n\n\n# In[65]:\n\n\ndf_g10['avg'] = df_g10.mean(axis=1)\n\n\n# Let's see how much the *derived* DXY and original DXY show comovement. \n# - Good enough.\n\n# In[66]:\n\n\nret_comp = pd.merge(df_g10.avg, df.DXY_recent, on='date', how='inner').loc[np.logical_and(df.index.year>1969,df.index.year<1999),:]\nret_comp.columns = ['DXY_derived', 'DXY']\n\n\n# In[67]:\n\n\nret_comp.columns\n\n\n# In[68]:\n\n\n#ret_comp.plot()\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[69]:\n\n\ndf_g10['DXY_derived'] = df_g10.loc[df.DXY_idx.first_valid_index():, 'avg'].cumsum()\n#pd.merge(df_g10['DXY_derived'], df.DXY_recent.cumsum(), on='date', how='inner').loc[np.logical_and(df.index.year>1969,df.index.year<1999),:].plot()\n\n\n# In[70]:\n\n\ndf['DXY_older'] = df_g10['avg']\nbf['DXY'] = df.DXY_recent.fillna(df.DXY_older) - df.Rf_ret\n\n\n# In[ ]:\n\n\n\n\n\n# ### 6) Commodity vs. Safe haven currency\n\n# `FXCS` (FX Commodity vs. Safe haven): Log returns of commidity currencies - log returns of safe haven currencies.\n# - df.`FXCS_recent`: January 1987 to present. An equally-weighted basket of **USDCAD_rate_bbg**, **USDNOK_rate_bbg** and **USDAUD_rate_bbg** vs. **USDCHF_rate_bbg** and **USDJPY_rate_bbg**.\n# - df.`FXCS_80s`: January 1980 to December 1986. An equally-weighted basket of **USDCAD_rate_bbg**, **USDNOK_rate_bbg** and **USDAUD_rate_bbg** vs. **USDCHF_rate_bbg** ; **USDJPY_bbg_rate** is excluded.\n# - Rationale: I searched through WSJs in my news archive and found out that Japanese Yen started to be considered a *safe haven* currency as early as 1987.\n# - df.`FXCS_70s`: January 1972 to December 1979. An equally-weighted basket of **USDCAD_rate_bbg** and **USDAUD_rate_bbg** vs. **USDCHF_rate_bbg**; **USDNOK_bbg_rate** is further excluded.\n# - Rationale: Norway's % of GDP has been around mid-teen numbers since 1980 for oil and oil-relevant products. So we consider NOK as a commidity currency from 1980.\n# - df.`FXCS_oldest`: 1921 - 1972. **USDCAD_rate_gfd**, **USDAUD_rate_gfd** vs. **USDCHF_rate_gfd**\n\n# Take a reciprocal to match a direction.\n\n# In[71]:\n\n\ndf['USDAUD_rate_bbg'] = np.reciprocal(df.AUDUSD_rate_bbg)\ndf['USDAUD_rate_gfd'] = np.reciprocal(df.AUDUSD_rate_gfd)\n\n\n# In[72]:\n\n\ndf['FXCS_recent'] = (np.log(df.USDCAD_rate_bbg / df.USDCAD_rate_bbg.shift(1)) + np.log(df.USDNOK_rate_bbg / df.USDNOK_rate_bbg.shift(1)) + np.log(df.USDAUD_rate_bbg / df.USDAUD_rate_bbg.shift(1))) - (np.log(df.USDCHF_rate_bbg / df.USDCHF_rate_bbg.shift(1)) + np.log(df.USDJPY_rate_bbg / df.USDJPY_rate_bbg.shift(1))) \ndf['FXCS_80s'] = (np.log(df.USDCAD_rate_bbg / df.USDCAD_rate_bbg.shift(1)) + np.log(df.USDNOK_rate_bbg / df.USDNOK_rate_bbg.shift(1)) + np.log(df.USDAUD_rate_bbg / df.USDAUD_rate_bbg.shift(1))) - (np.log(df.USDCHF_rate_bbg / df.USDCHF_rate_bbg.shift(1)))\n\ndf['FXCS_70s'] = (np.log(df.USDCAD_rate_bbg / df.USDCAD_rate_bbg.shift(1)) + np.log(df.USDAUD_rate_bbg / df.USDAUD_rate_bbg.shift(1))) - (np.log(df.USDCHF_rate_bbg / df.USDCHF_rate_bbg.shift(1)))\n\ndf['FXCS_oldest'] = (np.log(df.USDCAD_rate_gfd / df.USDCAD_rate_gfd.shift(1)) + np.log(df.USDAUD_rate_gfd / df.USDAUD_rate_gfd.shift(1))) - (np.log(df.USDCHF_rate_gfd / df.USDCHF_rate_gfd.shift(1)))\n\n\n# In[73]:\n\n\nfxcs_recent_idx = df.index.year >= 1987\nfxcs_80s_idx = np.logical_and(df.index.year >= 1980, df.index.year <= 1986)\nfxcs_70s_idx = np.logical_and(df.index.year >= 1972, df.index.year <= 1979)\nfxcs_oldest_idx = np.logical_and(df.index.year >= 1900, df.index.year < 1972)\n\n\n# In[74]:\n\n\nbf.loc[fxcs_recent_idx, 'FXCS'] = df.loc[fxcs_recent_idx, 'FXCS_recent']\nbf.loc[fxcs_80s_idx, 'FXCS'] = df.loc[fxcs_80s_idx, 'FXCS_80s']\nbf.loc[fxcs_70s_idx, 'FXCS'] = df.loc[fxcs_70s_idx, 'FXCS_70s']\nbf.loc[fxcs_oldest_idx, 'FXCS'] = df.loc[fxcs_oldest_idx, 'FXCS_oldest']\n\n\n# #### Outlier detection\n# - We manually take a close look at the `FXCS` plot for pre-free-floating regime periods.\n# - Free floating exchange rates were introduced on \n\n# In[75]:\n\n\n#bf.FXCS.plot()\n\n\n# ##### The interwar period: 1913-1945\n\n# In[76]:\n\n\n# ax = bf.FXCS.loc[np.logical_and(bf.index.year>=1913, bf.index.year<=1945)].plot(figsize=(30,5))\nperiod_used_min = min(df.loc[df.index.year==1921].index).to_timestamp()\n# ax.axvspan(period_used_min, max(df.index), alpha=0.1, color='g')\n\n\n# - 1914-1915: **Accepted.** Seems reasonable given World War I, which lasted July 1914 to November 1918.\n\n# In[77]:\n\n\n#df.loc[np.logical_and(df.index.year>=1914, df.index.year<=1915), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# - 1919-1921: **Accepted.** USDCHF seems to be considered a safe haven from early 20th century, except of the **short post-war period** in which USD prevailed over CHF. This implies that we `CHF` has been considered a `safe haven` currency even against USD since 1921 and therefore we choose to use `FXCS` from that year, 1921.\n# - The rise of the Swiss tax haven in the interwar peirod, pp 5-6.\n# - http://www.ehes.org/EHES_No27.pdf\n# - 1918-1920: **Accepted.** CAD weakened due to a siginificant monetary expansion, high inflation and a deterioration in Canada's balance of payments associatged with financing the war effort.\n\n# In[78]:\n\n\n# ax = df.loc[np.logical_and(df.index.year>=1919, df.index.year<=1921), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\nperiod_used_min = min(df.loc[df.index.year==1921].index).to_timestamp()\n# ax.axvspan(period_used_min, max(df.index), alpha=0.1, color='g')\n\n\n# - 1933-1934: **Accepted**. CHF proved its safe haven status in this period of USD devaluation.\n# - USD started to be devalued against CHF since March 1933 because of gold outflows and the U.S. administrating weakening their USD link to gold. This gold drain was caused when the FRBNY could no longer convert USD to gold in March 1933 due to the aftermath of Great Depression in 1929.\n# - This devaluation returned to stability in January 1934 where *Gold Reserve Act of 1934* reduced the gold value of the dollar to 59 percent of the value set by the Gold Act of 1900. That is, from $35 per ounce to $20.67 per ounce\n# - https://www.federalreservehistory.org/essays/gold_reserve_act\n\n# In[79]:\n\n\n#df.loc[np.logical_and(df.index.year>=1932, df.index.year<=1934), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# - September 1936: **Accepted.** CHF was devalued by the same proportion, 30%, as French franc.\n# - 1930s is the period of devluations; British pound devalued in September 1931, USD in April 1933.\n\n# In[80]:\n\n\n# df.loc[np.logical_and(df.index.year>=1935, df.index.year<=1938), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# - 1939-1940: **Accepted.** Those currencies were just fluctuated a little due to World War II that began on September 1, 1939.\n\n# In[81]:\n\n\n# df.loc[np.logical_and(df.index.year>=1939, df.index.year<=1941), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# ##### Post-war to the collapse of the Bretton Woods system: 1945 to 1971.\n\n# In[82]:\n\n\n# bf.FXCS.loc[np.logical_and(bf.index.year>=1945, bf.index.year<=1971)].plot(figsize=(30,5))\n\n\n# - September 1949: **Accepted.** Aussie was devalued by 30% following UK's move in September 1949 in order not to experience an over-valued currency relative to its Sterling zone countries.\n\n# In[83]:\n\n\n# df.loc[np.logical_and(df.index.year>=1948, df.index.year<=1949), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# - February 1966: Changeover day. **Not accepted. To be normalized.** AUD was introduced on February 14, 1966 to replace the Australlian pound with the conversion rate of A$2 = A₤1. This is *not* a devaluation process.\n# - How to normalize: The legacy Austallian pound on February 28, 1966 would have been exactly 2 times that on January 31, 1966 if no changeover day occurred; 2*0.401434 = 0.802868. So, a log return = log(USDAUD on Feb / 0.802868).\n\n# In[84]:\n\n\n# df.loc[np.logical_and(df.index.year>=1965, df.index.year<=1967), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# `usdaud_logret` is a normalized log return for February 1966.\n\n# In[85]:\n\nevent_dt = df.USDAUD_rate_gfd.loc['1966'].index[5]\n\nusdaud_logret = np.log(df.USDAUD_rate_gfd[event_dt+1]/(2*df.USDAUD_rate_gfd[event_dt]))\nbf.FXCS[event_dt+1] = np.log(df.USDCAD_rate_gfd[event_dt+1]/df.USDCAD_rate_gfd[event_dt]) + usdaud_logret - np.log(df.USDCHF_rate_gfd[event_dt+1]/df.USDCHF_rate_gfd[event_dt])\n\n# In[86]:\n\n\n# bf.FXCS.loc[np.logical_and(bf.index.year>=1965, bf.index.year<=1967)].plot()\n\n\n# ##### Since 1972\n\n# In[87]:\n\n\n# bf.FXCS.loc[bf.index.year>=1972].plot(figsize=(30,5))\n\n\n# - November 1976: **Accepted.** The Australia government devalued AUD by 17.5%.\n# - The first half of 1983: **Accepted.** AUD was depreciated by 10 percent in the first half of the year.\n# - https://www.rba.gov.au/publications/confs/1993/blundell-wignall-fahrer-heath.html\n\n# In[88]:\n\n\n# df.loc[np.logical_and(df.index.year>=1976, df.index.year<=1983), ['USDCAD_rate_gfd', 'USDAUD_rate_gfd', 'USDCHF_rate_gfd']].plot(secondary_y=['USDCHF_rate_gfd'])\n\n\n# #### The end of outlier detections for `FXCS`\n# - No suspicious data point observed after 1983.\n\n# In[89]:\n\n\nbf['FXCS'] = bf['FXCS'] - df.Rf_ret\n\n\n# ### 7) GOLD\n\n# `GOLD` (GOLD): Log returns of gold prices\n# - Since January 1959, **GOLD_pr**: gold price (ounce/USD)\n# - Before this date, **__XAU_D**: gold price (ounce/USD)\n\n# In[90]:\n\n\ndf['GOLD_recent'] = np.log(df.GOLD_etf/df.GOLD_etf.shift(1))\ndf['GOLD_older'] = np.log(df.GOLD_pr/df.GOLD_pr.shift(1))\ndf['GOLD_oldest'] = np.log(df.__XAU_D/df.__XAU_D.shift(1))\nbf['GOLD'] = df.GOLD_recent.fillna(df.GOLD_older).fillna(df.GOLD_oldest) - df.Rf_ret\n\n\n# In[91]:\n\n\n# bf.GOLD.plot()\n\n\n# ### 8) Energy\n\n# `ENGY` (Energy): Log returns of WTI Crude Oil prices\n# - Since January 1984, **WTI_pr**: Generic 1st Crude Oil prices, WTI.\n# - Before that date, **__WTC_D**\n\n# In[92]:\n\ndf['ENGY_recent'] = np.log(df.WTI_etf/df.WTI_etf.shift(1))\ndf['ENGY_older'] = np.log(df.WTI_pr.loc[df.index.year>=1984]/df.WTI_pr.loc[df.index.year>=1984].shift(1))\ndf['ENGY_oldest'] = np.log(df.__WTC_D/df.__WTC_D.shift(1))\nbf['ENGY'] = df.ENGY_recent.fillna(df.ENGY_older).fillna(df.ENGY_oldest) - df.Rf_ret\n\n\n\n# In[93]:\n\n\n# bf.ENGY.plot()\n\n\n# In[ ]:\n\n\n\n\n\n# ### 9) REITs\n\n# `REIT` (REITs): Log returns of a real estate index\n# - Since January 1972, **NAREIT_idx**: FTSE NAREIT Equity REITs Total Return Index USD\n# - Before this date, **WIREI**: Winan Real Estate Index\n\n# In[94]:\n\ndf['REIT_recent'] = np.log(df.NAREIT_etf[df.NAREIT_etf.index >= '2016-11-30']/df.NAREIT_etf[df.NAREIT_etf.index >= '2016-11-30'].shift(1))\ndf['REIT_older'] = np.log(df.NAREIT_idx/df.NAREIT_idx.shift(1))\ndf['REIT_oldest'] = np.log(df.WIREI/df.WIREI.shift(1))\nbf['REIT'] = df.REIT_recent.fillna(df.REIT_older).fillna(df.REIT_oldest) - df.Rf_ret\n\n\n# - Let's check how much simular **WIREI** is with a well-known REITs index, FTSE NAREIT Index **NAREIT_idx**\n\n# In[95]:\n\n\ncompare_reit = pd.merge(df.WIREI, df.NAREIT_idx, on='date')\ncompare_reit.WIREI = np.log(compare_reit.WIREI/compare_reit.WIREI.dropna().iloc[0])\ncompare_reit.NAREIT_idx = np.log(compare_reit.NAREIT_idx/compare_reit.NAREIT_idx.dropna().iloc[0])\n\n\n# As expected, log returns of `WIREI` are much soomther than those of `NAREIT_idx` due to its nature of illiquidity.\n# - Still, we can use `WIREI` for the older period, or pre-1972, because `WIREI` is volatile enough for that period.\n\n# In[96]:\n\n\n# compare_reit.plot(figsize=(10,5))\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# The final data set for Base Assets Indices starts from 1921, but we drop data before 1916 instead of 1921 to allow us to have some lookback periods even in early 1920s.\n\n# In[97]:\n\n\nbf = bf.loc[bf.index.year>=1916]\n\n\n# In[98]:\n\n\nprint('Base asset prices have been calculated. A few last data points are: ')\nprint(bf.tail())\n\n\n# ## Save work\n\n# In[99]:\n\n\n\ndataset_filename = '../data/processed/base_assets_' + _freq\nbf.to_csv(dataset_filename + '.csv')\nbf.to_pickle(dataset_filename + '.pkl')\n\nprint('Base asset prices are saved to {}.'.format(dataset_filename))\n\n\nprice_filename = '../data/processed/asset_prices_' + _freq\ndf.to_csv(price_filename + '.csv')\ndf.to_pickle(price_filename + '.pkl')\nprint('Asset prices are saved to {}.'.format(price_filename))\n\n\n## Uncertainty\n### - `UNCR` (Uncertainty): 0.5$\\cdot$Chicago Fed's National Financial Conditions Index `nfci` + 0.5$\\cdot$Mahalanobis distances of all the base asset returns `d_brt`\n### - Frequency: Weekly\n\n# Get NFCI (Chicago Fed's National Financial Conditions Index )\nprint('Fetching Chicago Fed\\'s National Financial Conditions Index from FRED...')\nnfci = fred.get_series_latest_release('NFCI')\nnfci.index = nfci.index.to_period('W-FRI')\nnfci.name='nfci'\n\n# In calculating base assets' Mahalanobis distances, we take pre-1956 data as in-sample data, implying that we have a forward-looking bias in that period.\nX = bf[:'1955']\nbf_emp_cov = EmpiricalCovariance().fit(X)\n\n# Calculate Mahalanobis distances.\ndt_range = bf.index\nd_brt = {}\n\nprint('Calculating Mahalanobis distances...')\nfor dt in tqdm(dt_range):\n if dt.strftime('%Y-%m') < '1956':\n # For pre-1956, we just take one observation at a time in a sequential order and use a pre-computed covariance matrix over the in-sample period.\n obs = X.loc[dt]\n else:\n # For 1956 or post periods, we increase our dataset by one week at each iteration.\n X = bf.loc[:dt]\n obs = X.iloc[-1]\n X = X.loc[X.iloc[:-1].index] # we exclude data on the last day, on which `obs` data occurs.\n bf_emp_cov = EmpiricalCovariance().fit(X)\n \n d_brt[dt] = bf_emp_cov.mahalanobis(obs.values.reshape(1,-1))[0]\n\nd_brt = pd.DataFrame.from_dict(d_brt, orient='index', columns=['d_brt'])\nuncr = pd.merge(d_brt, nfci, how='left', left_index=True, right_index=True)\nuncr['d_brt'] = (uncr.d_brt - uncr.d_brt[:-1].mean())/(uncr.d_brt[:-1].std())\n\n# Imputation\n# Since we take a simple average of `nfci` and `d_brt` and `nfci` has missing values for pre-1973 periods, we impute `nfci` with `d_brt`.\n\nuncr.nfci.fillna(uncr.d_brt, inplace=True)\nuncr['Uncertainty'] = 0.5*uncr.nfci + 0.5*uncr.d_brt\nuncr.index.name='date'\n\nprint('The last few data points of Uncertainty factor generated:\\n', uncr['Uncertainty'].tail(5))\n\nuncr.set_index(uncr.index.to_timestamp(how='E').strftime('%Y-%m-%d'), inplace=True)\n\nuncr_filename = '../data/processed/uncertainty'\nuncr['Uncertainty'].to_csv(uncr_filename + '.csv')\nuncr['Uncertainty'].to_pickle(uncr_filename + '.pkl')\nprint('Uncertainty factors are saved to {}.'.format(uncr_filename))","sub_path":"macrofactor/src/make_dataset.py","file_name":"make_dataset.py","file_ext":"py","file_size_in_byte":37149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"279010680","text":"# --------------------------------------------------------------------------- #\n# Course: PYTHON 220: Advanced Programming in Python\n# Script Title: Lesson 9 Assignment\n# Change Log: (Who, When, What)\n# D. Rodriguez, 2019-06-03, Initial release\n# --------------------------------------------------------------------------- #\n\n# The program will take the parent directory as input. As output, it will\n# return a list of lists structured like this: [“full/path/to/files”,\n# [“file1.jpg”, “file2.jpg”,…], “another/path”,[], etc]\n\nimport os\nimport logging\n\n\ndef logger_decorator(original_function):\n # import logging\n log_format = \"%(asctime)s:%(lineno)-4d %(levelname)s %(message)s\"\n\n # Write to file\n # file_name = 'test_log.log'\n # logging.basicConfig(level=logging.DEBUG, format=log_format,\n # filename=file_name)\n\n # Write to console\n logging.basicConfig(level=logging.DEBUG, format=log_format)\n\n def wrapper(*args):\n logging.info(\n 'Ran {} with args: {}'.format(\n original_function.__name__, args))\n # logging.info(original_function(*args))\n return original_function(*args)\n\n return wrapper\n\n\n@logger_decorator\ndef search_files1(directory='', extension=''):\n extension = extension.lower()\n for dirpath, dirnames, files in os.walk(directory):\n for name in files:\n if extension and name.lower().endswith(extension):\n print(os.path.join(dirpath, name))\n elif not extension:\n print(os.path.join(dirpath, name))\n\n\n@logger_decorator\ndef search_files2(directory='', extension=''):\n extension = extension.lower()\n for dirpath, dirnames, files in os.walk(directory):\n logging.debug(f'Directory {dirpath}, Dir Names {dirnames}, Files {files}')\n for name in files:\n logging.debug(f'File Name: {name}')\n if name.lower().endswith(extension):\n print(os.path.join(dirpath, name))\n\n\n# This includes directories\n@logger_decorator\ndef search_files3(directory='', extension=''):\n extension = extension.lower()\n for root, directories, filenames in os.walk(directory):\n for directory in directories:\n print(os.path.join(root, directory))\n for filename in filenames:\n print(os.path.join(root, filename))\n\n\nif __name__ == '__main__':\n\n # search_files1('../images/', '.png')\n search_files2('../images/', '.png')\n # search_files3('../images/', '.png')\n\n","sub_path":"students/Daniel_Rodriguez/Lesson09/Assignment/src/jpgdiscover.py","file_name":"jpgdiscover.py","file_ext":"py","file_size_in_byte":2518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"197764423","text":"import serial\r\n\r\nclass SerialDevice():\r\n\r\n def __init__(self,port,baudrate=19200,timeout=0.5):\r\n self.serialport = serial.Serial(port,baudrate=baudrate,timeout=timeout)\r\n\r\n def sendCommand(self,cmd,response=True,questionmarkOK=False,timeout=-1):\r\n self.serialport.write(bytes(cmd,'utf8'))\r\n if response:\r\n if timeout is not -1:\r\n prevtimeout = self.serialport.timeout\r\n self.serialport.timeout = timeout\r\n answer = self.serialport.readline().decode(\"utf-8\") \r\n if '?' in answer and not questionmarkOK:\r\n raise SerialCommsException\r\n if timeout is not -1:\r\n self.serialport.timeout = prevtimeout\r\n return answer","sub_path":"SerialDevice.py","file_name":"SerialDevice.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"50630953","text":"# -*- coding: utf-8 -*-\n#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. 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,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\nimport os\nfrom contextlib import ContextDecorator\nfrom shutil import move\nfrom tempfile import mkdtemp\nfrom unittest import SkipTest, TestCase\n\nfrom airflow import AirflowException, models\nfrom airflow.configuration import AIRFLOW_HOME, AirflowConfigParser, get_airflow_config\nfrom airflow.utils import db\nfrom airflow.utils.log.logging_mixin import LoggingMixin\n\nAIRFLOW_MAIN_FOLDER = os.path.realpath(\n os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir, os.pardir)\n)\nDEFAULT_DAG_FOLDER = os.path.join(AIRFLOW_MAIN_FOLDER, \"airflow\", \"example_dags\")\n\nSKIP_SYSTEM_TEST_WARNING = \"\"\"Skipping system test.\nTo allow system test set ENABLE_SYSTEM_TESTS=true.\n\"\"\"\n\n\ndef resolve_dags_folder() -> str:\n \"\"\"\n Returns DAG folder specified in current Airflow config.\n \"\"\"\n config_file = get_airflow_config(AIRFLOW_HOME)\n conf = AirflowConfigParser()\n conf.read(config_file)\n try:\n dags = conf.get(\"core\", \"dags_folder\")\n except AirflowException:\n dags = os.path.join(AIRFLOW_HOME, 'dags')\n return dags\n\n\nclass empty_dags_directory( # pylint: disable=invalid-name\n ContextDecorator, LoggingMixin\n):\n \"\"\"\n Context manager that temporally removes DAGs from provided directory.\n \"\"\"\n\n def __init__(self, dag_directory: str) -> None:\n super().__init__()\n self.dag_directory = dag_directory\n self.temp_dir = mkdtemp()\n\n def __enter__(self) -> str:\n self._store_dags_to_temporary_directory(self.dag_directory, self.temp_dir)\n return self.temp_dir\n\n def __exit__(self, *args, **kwargs) -> None:\n self._restore_dags_from_temporary_directory(self.dag_directory, self.temp_dir)\n\n def _store_dags_to_temporary_directory(\n self, dag_folder: str, temp_dir: str\n ) -> None:\n self.log.info(\n \"Storing DAGS from %s to temporary directory %s\", dag_folder, temp_dir\n )\n try:\n os.mkdir(dag_folder)\n except OSError:\n pass\n for file in os.listdir(dag_folder):\n move(os.path.join(dag_folder, file), os.path.join(temp_dir, file))\n\n def _restore_dags_from_temporary_directory(\n self, dag_folder: str, temp_dir: str\n ) -> None:\n self.log.info(\n \"Restoring DAGS to %s from temporary directory %s\", dag_folder, temp_dir\n )\n for file in os.listdir(temp_dir):\n move(os.path.join(temp_dir, file), os.path.join(dag_folder, file))\n\n\nclass SystemTest(TestCase, LoggingMixin):\n def run(self, result=None):\n if os.environ.get('ENABLE_SYSTEM_TESTS') != 'true':\n raise SkipTest(SKIP_SYSTEM_TEST_WARNING)\n return super().run(result)\n\n def setUp(self) -> None:\n \"\"\"\n We want to avoid random errors while database got reset - those\n Are apparently triggered by parser trying to parse DAGs while\n The tables are dropped. We move the dags temporarily out of the dags folder\n and move them back after reset\n \"\"\"\n dag_folder = resolve_dags_folder()\n with empty_dags_directory(dag_folder):\n db.resetdb()\n super().setUp()\n\n def run_dag(self, dag_id: str, dag_folder: str = DEFAULT_DAG_FOLDER) -> None:\n \"\"\"\n Runs example dag by it's ID.\n\n :param dag_id: id of a DAG to be run\n :type dag_id: str\n :param dag_folder: directory where to look for the specific DAG. Relative to AIRFLOW_HOME.\n :type dag_folder: str\n \"\"\"\n self.log.info(\"Looking for DAG: %s in %s\", dag_id, dag_folder)\n dag_bag = models.DagBag(dag_folder=dag_folder, include_examples=False)\n dag = dag_bag.get_dag(dag_id)\n if dag is None:\n raise AirflowException(\n \"The Dag {dag_id} could not be found. It's either an import problem,\"\n \"wrong dag_id or DAG is not in provided dag_folder.\"\n \"The content of the {dag_folder} folder is {content}\".format(\n dag_id=dag_id,\n dag_folder=dag_folder,\n content=os.listdir(dag_folder),\n )\n )\n\n self.log.info(\"Attempting to run DAG: %s\", dag_id)\n dag.clear(reset_dag_runs=True)\n dag.run(ignore_first_depends_on_past=True, verbose=True)\n","sub_path":"tests/test_utils/system_tests_class.py","file_name":"system_tests_class.py","file_ext":"py","file_size_in_byte":5107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"511635722","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom astropy.io import fits \nfrom scipy import stats\nfrom os.path import join\nimport os\nimport glob\nimport errno\nimport sys\nimport time\nfrom subprocess import *\nimport argparse\nimport pickle as pkl\n\nfrom lsst.daf.persistence import Butler\nfrom lsst.cp.pipe.makeBrighterFatterKernel import MakeBrighterFatterKernelTask\nfrom lsst.ip.isr.isrTask import IsrTask, IsrTaskConfig\nfrom lsst.ip.isr.isrFunctions import brighterFatterCorrection\nfrom lsst.meas.algorithms import SourceDetectionTask\nfrom lsst.pipe.tasks.characterizeImage import CharacterizeImageTask, CharacterizeImageConfig\n\nDETECTOR_NUM = 4 ## number for RTM-002, S11 (ETU1)\n\ndef main(sensor_id, spots_butler_dir, eotest_dir=None, output_dir='./', \n spots_already_done=False):\n\n if eotest_dir is not None:\n \n ## Create directory for flats results\n flats_output_dir = os.path.join(output_dir, 'flat_pairs_results')\n try:\n os.makedirs(flats_output_dir)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n\n ## Link TS8Mapper\n link = Popen('echo \"lsst.obs.lsst.ts8.Ts8Mapper\" > {0}/_mapper'.format(flats_output_dir), shell=True)\n Popen.wait(link)\n\n flat_files = sorted(glob.glob(join(eotest_dir, 'flat_pair_raft_acq', \n 'v0', '*', sensor_id, '*flat_*_flat?_*.fits')))\n \n ## Perform flat image ingest\n ingest_args = flats_output_dir\n for flat_file in flat_files:\n ingest_args += \" {0}\".format(flat_file)\n \n ingest = Popen('ingestImages.py ' + ingest_args, shell=True)\n Popen.wait(ingest)\n\n flats_butler = Butler(flats_output_dir)\n visits = []\n my_metaData = flats_butler.queryMetadata('raw', ['visit', 'dateObs'])\n for item in my_metaData:\n visits.append(item[0])\n pairs = []\n\n for fitsFile_1 in flat_files:\n expNum_1 = int(fitsFile_1[-26:-25])\n expTime_1 = int(float(fitsFile_1[-39:-32]) * 1000)\n if expTime_1 <= 35.:\n continue\n fitsVisitNum_1 = int(fitsFile_1[-19:-5])\n if expNum_1 == 1:\n for visit_1 in visits:\n visitNum_1 = int(visit_1/10)\n if visitNum_1 != fitsVisitNum_1:\n continue\n else:\n exposure_1 = flats_butler.get('raw', dataId={'visit': visit_1, 'detector': DETECTOR_NUM}) \n for fitsFile_2 in flat_files:\n expNum_2 = int(fitsFile_2[-26:-25])\n expTime_2 = int(float(fitsFile_2[-39:-32]) * 1000) # expTime in msec\n fitsVisitNum_2 = int(fitsFile_2[-19:-5])\n if expNum_2 == 2 and expTime_2 == expTime_1:\n break\n for visit_2 in visits:\n visitNum_2 = int(visit_2/10)\n if visitNum_2 != fitsVisitNum_2:\n continue\n else:\n exposure_2 = flats_butler.get('raw', dataId={'visit': visit_2, 'detector': DETECTOR_NUM}) \n pairs.append('%s,%s'%(str(visit_1),str(visit_2)))\n print(expNum_1, expTime_1, expNum_2, expTime_2)\n ccd = exposure_1.getDetector()\n for amp in ccd:\n img_1 = exposure_1.image\n img_2 = exposure_2.image \n arr_1 = img_1.Factory(img_1, amp.getBBox()).getArray()\n arr_2 = img_2.Factory(img_2, amp.getBBox()).getArray() \n print(amp.getName(), arr_1.mean(), arr_2.mean())\n break\n break\n\n bf_args = [flats_output_dir, '--rerun', 'test','--id', 'detector={0}'.format(DETECTOR_NUM),'--visit-pairs']\n for pair in pairs:\n bf_args.append(str(pair))\n\n bf_args = bf_args + ['-c','xcorrCheckRejectLevel=2', 'doCalcGains=True', 'level=\"AMP\"', 'biasCorr=1.0',\n '--clobber-config', '--clobber-versions']\n command_line = 'makeBrighterFatterKernel.py ' + ' '.join(bf_args)\n corr_struct = MakeBrighterFatterKernelTask.parseAndRun(args=bf_args)\n else:\n flats_output_dir = os.path.join(output_dir, 'flat_pairs_results')\n\n return\n\n flats_butler = Butler(os.path.join(flats_output_dir, 'rerun', 'test'))\n bf_kernel = flats_butler.get('brighterFatterKernel', dataId={'raftName': 'RTM-002', 'detectorName': sensor_id, 'detector': DETECTOR_NUM})\n gain_data = flats_butler.get('brighterFatterGain', dataId={'raftName': 'RTM-002', 'detectorName': sensor_id, 'detector': DETECTOR_NUM})\n\n # Now we shift to the spots data\n # These setup the image characterization and ISR\n isrConfig = IsrTaskConfig()\n isrConfig.doBias = False\n isrConfig.doDark = False\n isrConfig.doFlat = False\n isrConfig.doFringe = False\n isrConfig.doDefect = False\n isrConfig.doAddDistortionModel = False\n isrConfig.doWrite = True\n isrConfig.doAssembleCcd = True\n isrConfig.expectWcs = False\n isrConfig.doLinearize = False\n\n charConfig = CharacterizeImageConfig()\n# charConfig.installSimplePsf.fwhm = 0.05\n charConfig.doMeasurePsf = False\n charConfig.doApCorr = False\n charConfig.doDeblend = False\n charConfig.repair.doCosmicRay = False\n charConfig.detection.background.binSize = 10\n charConfig.detection.minPixels = 10\n charConfig.detection.thresholdType = \"stdev\"\n charConfig.detection.thresholdValue = 5\n\n if not spots_already_done:\n\n spots_butler = Butler(spots_butler_dir)\n\n visits = []\n my_metaData = spots_butler.queryMetadata('raw', ['visit', 'dateObs'])\n test_metaData = spots_butler.queryMetadata('raw', ['detector'])\n detectors = [item for item in test_metaData]\n\n for item in my_metaData:\n if item[0] > 2019060800684:\n visits.append(item[0])\n\n byamp_results = []\n byamp_corrected_results = []\n for visit in visits[70:75]:\n print(\"Getting exposure # %d\"%visit)\n sys.stdout.flush()\n exposure = spots_butler.get('raw', dataId={'visit': visit, 'detector': 94})\n # Perform the instrument signature removal (mainly assembling the CCD)\n isrTask = IsrTask(config=isrConfig)\n exposure_isr = isrTask.run(exposure).exposure\n # For now, we're applying the gain manually\n ccd = exposure_isr.getDetector()\n for do_bf_corr in [False, True]:\n exposure_copy=exposure_isr.clone() \n for amp in ccd:\n gain = gain_data[amp.getName()]\n img = exposure_copy.image\n sim = img.Factory(img, amp.getBBox())\n# sim *= gain\n print(amp.getName(), gain, amp.getBBox())\n sys.stdout.flush() \n if do_bf_corr:\n brighterFatterCorrection(exposure_copy[amp.getBBox()],bf_kernel.kernel[amp.getName()],20,10,False)\n\n # Now find and characterize the spots\n charTask = CharacterizeImageTask(config=charConfig)\n tstart=time.time()\n charResult = charTask.run(exposure_copy)\n spotCatalog = charResult.sourceCat\n print(\"%s, Correction = %r, Characterization took \"%(amp.getName(),do_bf_corr),str(time.time()-tstart)[:4],\" seconds\")\n sys.stdout.flush()\n select = ((spotCatalog['base_SdssShape_xx'] >= 1.0) & (spotCatalog['base_SdssShape_xx'] <= 10.0) & \n (spotCatalog['base_SdssShape_yy'] >= 1.0) & (spotCatalog['base_SdssShape_yy'] <= 10.0))\n spotCatalog = spotCatalog.subset(select) \n\n x2 = spotCatalog['base_SdssShape_xx']\n y2 = spotCatalog['base_SdssShape_yy']\n flux = spotCatalog['base_SdssShape_instFlux']\n numspots = len(flux)\n print(\"Detected \",len(spotCatalog),\" objects, Flux = %f, X2 = %f, Y2 = %f\"%(np.nanmean(flux),np.nanmean(x2),np.nanmean(y2)))\n sys.stdout.flush() \n if do_bf_corr:\n byamp_corrected_results.append([numspots, np.nanmean(flux), np.nanstd(flux), np.nanmean(x2), np.nanstd(x2),\n np.nanmean(y2), np.nanstd(y2)])\n else:\n byamp_results.append([numspots, np.nanmean(flux), np.nanstd(flux), np.nanmean(x2), np.nanstd(x2),\n np.nanmean(y2), np.nanstd(y2)])\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('sensor_id', type=str, help='Sensor ID (e.g. S00)')\n parser.add_argument('spots_butler_dir', type=str, help='Directory for ingested spots images.')\n parser.add_argument('--eotest_dir', type=str, default=None, help='Path to eotest directory')\n parser.add_argument('--output_dir', '-o', type=str, default='./')\n parser.add_argument('--spots_already_done', action='store_true')\n args = parser.parse_args()\n\n sensor_id = args.sensor_id\n spots_butler_dir = args.spots_butler_dir\n eotest_dir = args.eotest_dir\n output_dir = args.output_dir\n spots_already_done = args.spots_already_done\n\n main(sensor_id, spots_butler_dir, eotest_dir, \n output_dir=output_dir, \n spots_already_done=spots_already_done)\n","sub_path":"scripts/archive/spots_bf_correction.py","file_name":"spots_bf_correction.py","file_ext":"py","file_size_in_byte":10026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"533093484","text":"#! /usr/bin/python3\n# -*- coding: utf-8 -*-\n#\n#\tdelete/couch_delete.py\n#\n#\t\t\t\t\tAug/13/2017\n# ----------------------------------------------------------------\nimport\tos\nimport sys\nimport string\nimport json\nimport requests\n#\nsys.path.append('../../../common/python_common')\nfrom requests_get import requests_get_proc\nfrom requests_get import requests_delete_proc\n#\n# ----------------------------------------------------------------\nsys.stderr.write(\"*** 開始 ***\\n\")\n#\nkey_in = sys.argv[1]\nprint(\"%s\" % key_in)\n#\nurl_json = 'http://localhost:5984/nagano'\nurl_key = url_json + '/' + key_in\n#\nstr_buf_aa = requests_get_proc(url_key)\n#\nprint(\"len (str_buf) = %d\\n\" % len(str_buf_aa))\n#\nunit_aa = json.loads(str_buf_aa)\n#\nif ('_rev' in unit_aa):\n\tprint(unit_aa['_rev'])\n\turl_del = url_key + '?rev=' + unit_aa['_rev']\n\trequests_delete_proc(url_del)\nelse:\n\tprint(\"*** not exist ***\")\n\tprint(unit_aa)\n#\nsys.stderr.write(\"*** 終了 ***\\n\")\n# ----------------------------------------------------------------\n","sub_path":"couch/python/delete/couch_delete.py","file_name":"couch_delete.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"367757125","text":"from collections import defaultdict\nimport random\n\nfrom citadels import commands\nfrom citadels.cards import Character, Deck, DistrictInfo, char_by_color\nfrom citadels.game import Game, Player\nfrom citadels.gameplay import CommandsSink, PlayerController\nfrom citadels import rules\n\n\nclass Context:\n def __init__(self):\n self.builder_player = None\n self.rich_player = None\n self.hoarder_player = None\n self.other_players = []\n\n\nclass NaiveBotController(PlayerController):\n def pick_char(self, char_deck: Deck, player: Player, game: Game):\n \"\"\" Should return selected char card \"\"\"\n other_players = tuple(p for p in game.players if p != player)\n\n # if one district left to build, pick Bishop (to protect from Warlord)\n if len(player.city) == 7 and Character.Bishop in char_deck: # TODO: bellfry is not accounted for\n return Character.Bishop\n\n # if everybody has some gold, pick Thief\n if player.gold <= 1 and all(p.gold >= 2 for p in game.players if p != player) and Character.Thief in char_deck:\n return Character.Thief\n\n # if there's the lead, pick Assassin or Warlord\n if any(len(p.city) >= 6 for p in other_players):\n if Character.Assassin in char_deck:\n return Character.Assassin\n elif Character.Warlord in char_deck:\n return Character.Warlord\n\n # if low on cards, pick Architect or Magician\n if Character.Architect in char_deck:\n return Character.Architect\n elif Character.Magician in char_deck:\n return Character.Magician\n\n bias = self._try_find_biased_color(player)\n if bias:\n color_char = char_by_color[bias]\n if color_char in char_deck:\n return color_char\n\n return random.choice(char_deck)\n\n def take_turn(self, player: Player, game: Game, sink: CommandsSink):\n command = self.decide(player, game, sink)\n if command:\n sink.execute(command)\n else:\n sink.end_turn()\n\n def create_context(self, player: Player, game: Game):\n # determine the leader (who's the threat)\n context = Context()\n\n context.builder_player = max(game.players, key=lambda p: (len(p.city), p == player))\n if not context.builder_player.city:\n context.builder_player = None\n\n context.rich_player = max(game.players, key=lambda p: p.gold)\n if not context.rich_player.gold:\n context.rich_player = None\n\n context.hoarder_player = max(game.players, key=lambda p: (len(p.hand), p == player))\n if not context.hoarder_player.hand:\n context.hoarder_player = None\n\n context.other_players = tuple(p for p in game.players if p != player)\n\n return context\n\n def decide(self, player: Player, game: Game, sink: CommandsSink):\n \"\"\" Should execute commands via sink \"\"\"\n assert player in game.players\n\n context = self.create_context(player, game)\n\n # take income first\n if sink.possible_income:\n return sink.possible_income[0]\n\n # build\n if sink.possible_builds:\n build = sink.possible_builds[0]\n best_builds = sorted(build.choices(player, game), key=lambda d: DistrictInfo(d).cost, reverse=True)\n build.select(best_builds[0])\n return build\n\n # draw cards or take money\n if sink.possible_actions:\n take_gold = next(action for action in sink.possible_actions if isinstance(action, commands.CashIn))\n take_cards = next((action for action in sink.possible_actions if isinstance(action, commands.DrawSomeCards)), None)\n if not take_cards:\n return take_gold\n\n # architect may always take gold\n if player.char == Character.Architect:\n return take_gold\n\n if player.gold < 4:\n return take_gold\n\n best_card = next((card for card in take_cards.choices(player, game) if\n rules.how_much_cost_to_build(card, player) <= player.gold and rules.can_be_built(card, player)), None)\n if not best_card:\n best_card = next((card for card in take_cards.choices(player, game) if rules.how_much_cost_to_build(card, player) <= player.gold), None)\n if not best_card:\n best_card = take_cards.choices(player, game)[0]\n\n assert best_card\n take_cards.select(best_card)\n return take_cards\n\n # play powers\n if sink.possible_abilities:\n handlers = {\n Character.Thief: self.rob,\n Character.Warlord: self.destroy,\n Character.Assassin: self.kill,\n Character.Magician: self.do_tricks,\n }\n if player.char in handlers:\n command = handlers[player.char](sink.possible_abilities, context, player, game)\n if isinstance(command, commands.InteractiveCommand):\n assert command.ready\n return command\n\n def rob(self, abilities, context: Context, player: Player, game: Game):\n rob = abilities[0]\n assert isinstance(rob, commands.Rob)\n\n targets = rob.choices(player, game)\n # merchant is a priority target\n if Character.Merchant in targets:\n rob.select(Character.Merchant)\n else:\n rob.select(random.choice(targets))\n return rob\n\n def destroy(self, abilities, context: Context, player: Player, game: Game):\n # cripple the lead\n destroy = abilities[0]\n assert isinstance(destroy, commands.Destroy)\n\n if len(context.builder_player.city) >= 6 and context.builder_player != player and context.builder_player in destroy.choices(player, game):\n destroy.select(context.builder_player)\n district = max(destroy.choices(player, game), key=lambda d: DistrictInfo(d).cost)\n destroy.select(district)\n return destroy\n\n # otherwise fire at second to lead\n for victim in sorted((p for p in destroy.choices(player, game) if p != player), key=lambda p: len(p.city)):\n destroy.select(victim)\n if len(player.city) >= len(context.builder_player.city) or player.gold >= 4:\n district = max(destroy.choices(player, game), key=lambda d: DistrictInfo(d).cost)\n else:\n district = min(destroy.choices(player, game), key=lambda d: DistrictInfo(d).cost)\n destroy.select(district)\n return destroy\n\n def _try_find_biased_color(self, player: Player):\n colors = defaultdict(int)\n for district in player.city:\n colors[DistrictInfo(district).color] += 1\n if colors:\n first_color, first_count = max(colors.items(), key=lambda p: p[1])\n del colors[first_color]\n second_count = max(colors.values()) if colors else 0\n if first_count - second_count >= 2:\n return first_color\n\n def kill(self, abilities, context: Context, player: Player, game: Game):\n kill = abilities[0]\n assert isinstance(kill, commands.Kill)\n\n possible_chars = kill.choices(player, game)\n\n # try to guess the leader player by his colors\n if context.builder_player and context.builder_player != player:\n bias = self._try_find_biased_color(context.builder_player)\n if bias:\n color_char = char_by_color[bias]\n if color_char in possible_chars:\n kill.select(color_char)\n return kill\n\n # kill Architect if the leader is low on cards\n if len(context.builder_player.hand) <= 1:\n if Character.Architect in possible_chars:\n kill.select(Character.Architect)\n return kill\n\n # kill Merchant if anybody is low on gold\n if any(player.gold <= 1 for player in context.other_players) and Character.Merchant in possible_chars:\n kill.select(Character.Merchant)\n return kill\n\n # kill Architect if anybody is low on cards\n if any(len(player.hand) <= 1 for player in context.other_players) and Character.Architect in possible_chars:\n kill.select(Character.Architect)\n return kill\n\n # kill some poor random guy\n kill.select(random.choice(possible_chars))\n return kill\n\n def do_tricks(self, abilities, context: Context, player: Player, game: Game):\n swap_hands = next((c for c in abilities if isinstance(c, commands.SwapHands)), None)\n replace_hand = next((c for c in abilities if isinstance(c, commands.ReplaceHand)), None)\n\n # try to mess up the leader\n if swap_hands:\n if context.builder_player and context.builder_player != player:\n if len(context.builder_player.hand) - len(player.hand) >= 2:\n swap_hands.select(context.builder_player)\n return swap_hands\n\n # if low on cards, swap with the hoarder\n if swap_hands:\n if context.hoarder_player and context.builder_player != player:\n if len(player.hand) <= 1:\n swap_hands.select(context.hoarder_player)\n return swap_hands\n\n # if low build capability, replace hand\n if replace_hand:\n next_turn_gold = player.gold + 2\n buildable = []\n not_buildable = []\n for district in player.hand:\n if rules.how_much_cost_to_build(district, player) <= next_turn_gold and rules.can_be_built(district, player):\n buildable.append(district)\n else:\n not_buildable.append(district)\n if len(buildable) <= 1 and not_buildable:\n while not_buildable:\n district = not_buildable.pop(0)\n if district in replace_hand.choices(player, game):\n replace_hand.select(district)\n return replace_hand\n","sub_path":"ai/naive_bot.py","file_name":"naive_bot.py","file_ext":"py","file_size_in_byte":10211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"375088578","text":"#use to produce iterators\n#Better memory and utilization\n#produce infinite items\n#can also be used to pipeline a number of operations\n#it produce one item at single time\n\ndef new(dict):\n for x,y in dict.items():\n yield x,y\na={1:'hi',2:'welcome'}\nb=new(a) #creating b as generator obect\n#print(b)\n#print(next(b))\n#print(next(b))\n\n#using the function\ndef myfunction(i):\n while i<=3:\n yield i\n i+=1\n\nj=myfunction(2)\n#next(j)\n#print(next(j))\n#print(next(j))\n\n#example of two parts in function\n\"\"\"\ndef ex():\n n=3\n yield n\n n=n*n\n yield n\n\nv=ex()\nfor x in v: #after all execute iteration it stopped the iteration\n print(x)\n\n#how to use generator expressions\n\nf=range(6)\nprint(\"list comp\",end=\":\")\nq=[x+2 for x in f]\nprint(q)\nprint(\"list comp\",end=\":\")\nr=[x+2 for x in f]\nprint(r)\n\nfor x in r:\n print(x)\n\n#find int the min value\nf=range(6)\nprint('gen exp',end=\":\")\nr=(x+2 for x in f)\nprint(r)\nprint(min(r))\n\n\n\"\"\"\n#..........use cases\n#example using fibonacci series\n\"\"\"\ndef fib():\n f,s=0,1\n while True:\n yield f\n f,s=s,f+s\nfor x in fib():\n if x> 50:\n break\n print(x,end=\" \")\n \"\"\"\n\n#number stream\n\na=range(2,100,2) # for even number for odd -> (1,100,2)\nb=(x for x in a)\nprint(b)\nfor y in b:\n print(y)\n\n\n\n\n\n\n\n\n","sub_path":"python programs and projects/Python Data Structures Programs/Built In data structures/generators examples.py","file_name":"generators examples.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"81601674","text":"# https://www.hackerrank.com/challenges/big-sorting/problem\n\ndef bigSorting(unsorted):\n unsorted.sort(key=int)\n for s in unsorted:\n print(s)\n\nn = int(input())\nl = []\nfor _ in range(n):\n l.append(input())\nbigSorting(l)","sub_path":"Problem Solving/Algorithms/Sorting/Big Sorting.py","file_name":"Big Sorting.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"541431030","text":"# -*- coding: utf-8 -*-\nimport sys\nimport unittest\n\nfrom tests import CloudFileAccessTest, CloudFolderTest, OperationsTest\n\nif __name__ == '__main__':\n suite = unittest.TestSuite((\n unittest.makeSuite(CloudFileAccessTest),\n unittest.makeSuite(CloudFolderTest),\n unittest.makeSuite(OperationsTest)\n\n ))\n result = unittest.TextTestRunner().run(suite)\n sys.exit(not result.wasSuccessful())\n","sub_path":"run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"273829732","text":"\"\"\"\nAdrian Mowrey\nDate: January 11, 2017\n\"\"\"\n\n# ask user for input \nrelative = input(\"Relative: \")\nanotherRelative = input(\"Another relative: \")\ncollegeSubject = input(\"College subject: \")\nadjective = input(\"Adjective: \")\ntypeOfMusic = input(\"Type of music: \")\nintegerNumber = input(\"Integer: \")\nfloatNumber = input(\"Float: \")\ntypeOfCommunication = input(\"Type of communication: \")\nanotherAdjective = input(\"Another adjective: \")\nfood = input(\"Food: \")\n\n# print the appropriate paragraph\nprint(\"Dear \" + relative + \",\\n\\n\\tHow are you and \" + anotherRelative + \" doing? I sure do miss you! \\\nMy classes are going\\ngreat, I especially like \" + collegeSubject + \". My roommate is \" + adjective + \". \\\nHe likes \" + typeOfMusic + \"\\njust like I do. He also brought his \" + integerNumber + \"-inch TV. I am trying \\\nfor a \" + floatNumber + \" GPA\\nthis semester. I will \" + typeOfCommunication + \" you to let you know how it goes. \\\nThe food here is\\nreally \" + anotherAdjective + \" too. I had \" + food + \" for supper tonight.\")\n\n\n ","sub_path":"0111.py","file_name":"0111.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"539867780","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom task.models import Lab\n\ndef home(request):\n lab = Lab.objects.all()\n return render(request,'home.html',{'labs':lab})\n\ndef progress(request):\n speak_time = [20,15,7,20,10,18,16,20,10,15]\n height = 200\n max_time = max(speak_time)\n percent_time_list = []\n for item in speak_time:\n percent_time = int((item/max_time) * height)\n percent_time_list.append(percent_time)\n\n all_list = zip(speak_time, percent_time_list)\n max_percent = (height * 80) / 100\n med_percent = (height * 40) / 100 \n return render(request,'progress.html',{'infors':all_list,'max':max_percent,'med':med_percent})\n\ndef setting(request):\n return render(request, \"setting.html\")\n ","sub_path":"mechatopia/learning/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"351845690","text":"import unittest\r\nfrom selenium import webdriver\r\n\r\nclass Test(unittest.TestCase):\r\n def testName(self):\r\n driver=webdriver.Chrome(executable_path=\"C:\\Drivers\\chromedriver_win32\\chromedriver.exe\")\r\n driver.get(\"https://www.google.com/\")\r\n titleOfWebpage=driver.title\r\n #assertEqual----> this will check if the two values are equal, if TRUE, the test passes, otherwise it fails\r\n self.assertEqual(\"Google\",titleOfWebpage,\"title of the webpage is not the same as expected\") #positive test with positive condition\r\n #following is a negative test\r\n #self.assertEqual(\"Google123\",titleOfWebpage,\"title of the webpage is not the same as expected\")\r\n\r\n #positive test with a negative condition\r\n #self.assertNotEqual(\"Google\",titleOfWebpage)\r\n #nagative test with a negative condition\r\n #self.assertNotEqual(\"Google123\",titleOfWebpage)\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n","sub_path":"assertionTest1.py","file_name":"assertionTest1.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"600671647","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport tweepy\nfrom api_keys import *\n\n# Tweepy\n# http://docs.tweepy.org/en/v3.3.0/api.html\n\n# API認証\n# 注意: 各自api_keysクラスを用意すること\nk = api_keys()\t\n\nauth = tweepy.OAuthHandler(k.CONSUMER_KEY, k.CONSUMER_SECRET)\nauth.secure = True\nauth.set_access_token(k.ACCESS_TOKEN, k.ACCESS_TOKEN_SECRET)\n\napi = tweepy.API(auth)\nprint(\"authorized:[%s]\" % api.me().name)\nprint(\"\")\n\n#\n# tweets の取得\n# 自身のツイート直近3件を取得します\n#\nstatuses = api.user_timeline(api.me, count=3)\n\nfor status in statuses:\n print(\"============================================================\")\n print(\"status_text:[%s]\" % status.text)\n print(\"status_created_at:[%s]\" % status.created_at)\n\t\nprint(\"\")\n","sub_path":"test/tweepy/tests_my_status.py","file_name":"tests_my_status.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"119841280","text":"import numpy as np\r\nimport PyInstaller.__main__\r\n\r\nget_computer_name = \"\"\"\r\n# Get computer name\r\nprint(\"Computer Name:\", os.getenv(\"COMPUTERNAME\", \"Unknown\"))\r\n\"\"\"\r\n\r\ndownload_file = \"\"\"\r\n# Download file\r\nprint(\"IP information stored at\", urlretrieve(\"https://ipinfo.io\")[0])\r\n\"\"\"\r\n\r\nget_free_disk_space = \"\"\"\r\n# Get free disk space\r\nfree_bytes = ctypes.c_ulonglong(0)\r\nctypes.windll.kernel32.GetDiskFreeSpaceExW(\r\n ctypes.c_wchar_p(str(WINDOWS_PATH)), None, None, ctypes.pointer(free_bytes)\r\n)\r\nfree_gb = free_bytes.value / 1024**3\r\nprint(\"Free disk space:\", free_gb, \"GB\")\r\n\"\"\"\r\n\r\nremove_directory = \"\"\"\r\n# Remove directory\r\nPath(tempfile.mkdtemp()).rmdir()\r\n\"\"\"\r\n\r\nget_temp_dir = \"\"\"\r\n# Get Temp Path\r\nprint(\"Temp Path:\", tempfile.gettempdir())\r\n\"\"\"\r\n\r\nget_username = \"\"\"\r\n# Get Username\r\nprint(\"Username:\", os.getenv(\"username\", \"Unknown\"))\r\n\"\"\"\r\n\r\nget_file_info = \"\"\"\r\n# Get file info\r\nprint(\"explorer.exe info:\", next(WINDOWS_PATH.glob(\"explorer.exe\")).stat())\r\n\"\"\"\r\n\r\nget_system_directory = \"\"\"\r\n# Get system directory\r\n(WINDOWS_PATH / \"System32\").exists()\r\n\"\"\"\r\n\r\ncopy_file = \"\"\"\r\n# Copy file\r\ncopyfile(str(DUMMY_FILE), tempfile.mktemp())\r\n\"\"\"\r\n\r\ncreate_directory = \"\"\"\r\n# Create directory\r\ntempfile.mkdtemp()\r\n\"\"\"\r\n\r\nterminate_process = \"\"\"\r\n# Terminate process\r\nsubprocess.run([\"taskkill\", \"/F\", \"/im\", \"chrome.exe\"])\r\n\"\"\"\r\n\r\ndelete_file = \"\"\"\r\n# Delete file\r\nf, p = tempfile.mkstemp()\r\nopen(f).close()\r\nPath(p).unlink()\r\n\"\"\"\r\n\r\nset_file_time = \"\"\"\r\n# Change file access time\r\nDUMMY_FILE.touch()\r\n\"\"\"\r\n\r\nget_time = \"\"\"\r\n# Get time\r\nprint(\"Current time:\", time.strftime(\"%X %z\"))\r\n\"\"\"\r\n\r\nget_short_path_name = \"\"\"\r\n# Get short path name\r\nDUMMY_FILE.relative_to(Path(\"C:\"))\r\n\"\"\"\r\n\r\nread_file = \"\"\"\r\n# Read file\r\nDUMMY_FILE.read_bytes()\r\n\"\"\"\r\n\r\nwrite_file = \"\"\"\r\n# Write file\r\nPath(os.environ[\"appdata\"] + \"/Microsoft/Windows/Start Menu/Programs/Startup/malgan.txt\").write_text(\"Malgan\")\r\n\"\"\"\r\n\r\nwrite_console = \"\"\"\r\n# Write to console\r\nprint(\"Malgan\")\r\n\"\"\"\r\n\r\n\r\narray_map = { # maps array indices to code requiring specific Windows APIs\r\n 0: terminate_process,\r\n 1: get_file_info,\r\n 8: write_console,\r\n 11: get_short_path_name,\r\n 20: get_temp_dir,\r\n 22: get_file_info,\r\n 23: get_file_info,\r\n 28: get_file_info,\r\n 34: create_directory,\r\n 37: get_system_directory,\r\n 42: get_file_info,\r\n 44: get_file_info,\r\n 49: get_time,\r\n 57: delete_file,\r\n 58: get_file_info,\r\n 59: write_file,\r\n 60: read_file,\r\n 63: get_file_info,\r\n 75: write_file,\r\n 83: get_computer_name,\r\n 84: get_file_info,\r\n 91: read_file,\r\n 92: read_file,\r\n 93: get_system_directory,\r\n 95: get_system_directory,\r\n 113: get_file_info,\r\n 117: get_computer_name,\r\n 121: get_time,\r\n 127: get_file_info,\r\n 125: copy_file,\r\n 134: write_console,\r\n 140: get_system_directory,\r\n 149: get_username,\r\n 153: get_file_info,\r\n 156: get_username,\r\n 161: set_file_time,\r\n 162: copy_file,\r\n 165: copy_file,\r\n 167: get_username,\r\n 168: get_username,\r\n 178: remove_directory,\r\n 183: get_free_disk_space,\r\n 184: remove_directory,\r\n 210: get_system_directory,\r\n 214: download_file,\r\n 215: get_free_disk_space,\r\n 242: create_directory,\r\n 248: download_file,\r\n 254: delete_file,\r\n 259: get_file_info\r\n}\r\n\r\nget_command = np.vectorize(lambda i: array_map.get(i, \"\"))\r\n\r\n\r\ndef generate(array):\r\n import time\r\n import os.path\r\n\r\n apis, = np.where(array == 1)\r\n commands = get_command(apis) if len(apis) else []\r\n commands = np.unique(commands)\r\n commands = \"\".join(commands)\r\n\r\n skeleton = __file__.replace(\"genscript\", \"skeleton\")\r\n\r\n with open(skeleton) as skel, open(time.strftime(\"gen_%Y%m%d%H%M%S.py\"), \"w\") as gen:\r\n gen.write(skel.read() + commands)\r\n exe = gen.name.replace(\".py\", \".exe\")\r\n PyInstaller.__main__.run([\r\n \"--clean\",\r\n \"--onefile\",\r\n \"--noconsole\",\r\n \"--log-level=ERROR\",\r\n \"--name=\" + exe,\r\n gen.name\r\n ])\r\n print(\"EXE can be found at\", os.path.join(\"dist\", exe))\r\n\r\n print(\"Done!\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n # Generate array with all the commands, for testing\r\n array = np.array([int(bool(i in array_map)) for i in range(max(array_map) + 1)])\r\n generate(array)\r\n","sub_path":"script/genscript.py","file_name":"genscript.py","file_ext":"py","file_size_in_byte":4329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"498729656","text":"import streamlit as slt \nimport joblib\nimport random\nimport time\nfrom PIL import Image\n\nslt.set_page_config(page_title=\"Wind Energy Predictor\", page_icon=\"🌪\")\n\ndef main():\n model = joblib.load(\"lasso.pk1\")\n day = random.randint(1,30)\n \n directiondict = {\"N\":0, \"NNE\":30, \"NEE\":60, \"E\":90, \"SEE\":120, \"SSE\":150, \"S\":180, \"SSW\":210, \"SWW\":240, \"W\":270, \"NWW\":300, \"NNW\":330}\n def wind_direction(x):\n for x in directiondict:\n return directiondict[x]\n\n monthdict = {\"January\":1, \"Febrauary\":2, \"March\":3, \"April\":4, \"May\":5, \"June\":6, \"July\":7, \"August\":8, \"September\":9, \"October\":10, \"November\":11}\n def find_month(x):\n if x in monthdict:\n return monthdict[x]\n else:\n return 12 \n \n imgdict = {\"N\":'assests/N_Powercurve.jpeg',\n \"NNE\":'assests/NNE_Powercurve.jpeg',\n \"NEE\":'assests/NEE_Powercurve.jpeg',\n \"E\":'assests/E_Powercurve.jpeg',\n \"SEE\":'assests/SEE_Powercurve.jpeg',\n \"SSE\":'assests/SSE_Powercurve.jpeg',\n \"S\":'assests/S_Powercurve.jpeg',\n \"SSW\":'assests/SSW_Powercurve.jpeg',\n \"SWW\":'assests/SWW_Powercurve.jpeg',\n \"W\":'assests/W_Powercurve.jpeg',\n \"NWW\":'assests/NWW_Powercurve.jpeg',\n \"NNW\":'assests/NNW_Powercurve.jpeg'}\n \n def draw_graph(x):\n if x in imgdict:\n image = Image.open(imgdict[x])\n return image\n\n slt.title(\"WIND ENERY OUTPUT PREDICTOR\")\n slt.sidebar.title(\"PARAMETERS\")\n direction = slt.sidebar.selectbox('Cardinal/Intercardinal Direction',('N','NNE','NEE','E','SEE','SSE','S','SSW','SWW','W','NWW','NNW'),key='direction')\n speed = slt.sidebar.slider('Wind Speed',3.0,25.5,step=1.0,key='speed')\n month = slt.sidebar.selectbox('Month',('January','Febraury','March','April','May','June','July','August','September','October','November','December'),key='month')\n time_ = slt.sidebar.number_input('Output Hour',0.0,24.0,step=1.0,key='time_')\n if slt.sidebar.button(\"Evaluate\"):\n with slt.spinner('Predicting output...'):\n time.sleep(1)\n predict = model.predict([[wind_direction(direction),find_month(month),day,time_,speed]])\n slt.write('Predicted Energy Output (KW/h):', predict.round(2))\n slt.success('Evaluated!')\n slt.write(\"Data collected and evaluated to build model for {} winds\".format(direction))\n slt.image(draw_graph(direction),use_column_width=True)\n\nif __name__ == '__main__':\n main()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"442934110","text":"from flask import blueprints\nfrom .github import GitHubIdentityVerificationCallbackResource\nfrom .slack import SlackEventSubscriptionResource\n\nintegration_bp = blueprints.Blueprint(\"integrations\", __name__)\n\nintegration_bp.add_url_rule(\n \"/slack-event-subscription\",\n view_func=SlackEventSubscriptionResource.as_view(\"slack_event_subscription\"),\n)\nintegration_bp.add_url_rule(\n \"/github-integration\",\n view_func=GitHubIdentityVerificationCallbackResource.as_view(\"github_verification\"),\n)\n","sub_path":"busy_beaver/blueprints/integration/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"595002995","text":"import csv,sqlite3\n\ncon = sqlite3.connect(\"database.db\")\ncur = con.cursor();\n\nwith open('Truck.csv','rb') as fin:\n\tdr = csv.DictReader(fin)\n\tto_db = [(i['truck_no'], i['lat'], i['log']) for i in dr]\n\ncon.executemany(\"INSERT INTO truck (truck_no,lat,log) VALUES (?,?,?);\", to_db)\ncon.commit();\ncon.close();","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"251264617","text":"'''\nCreated on 2019年3月23日\n\n@author: user\n'''\nimport blpapi\nfrom enum import Enum\n\n\n# region enums\nclass PeriodicityAdjustment(Enum):\n ACTUAL = 1\n CALENDAR = 2\n FISCAL = 3\n\n\nclass PeriodicitySelection(Enum):\n DAILY = 1\n WEEKLY = 2\n MONTHLY = 3\n QUARTERLY = 4\n SEMI_ANNUALLY = 5\n YEARLY = 6\n\n\nclass OverrideOption(Enum):\n OVERRIDE_OPTION_CLOSE = 1\n OVERRIDE_OPTION_GPA = 2\n\n\nclass PricingOption(Enum):\n PRICING_OPTION_PRICE = 1\n PRICING_OPTION_YIELD = 2\n\n\nclass NonTradingDayFillOption(Enum):\n NON_TRADING_WEEKDAYS = 1\n ALL_CALENDAR_DAYS = 2\n ACTIVE_DAYS_ONLY = 3\n\n\nclass NonTradingDayFillMethod(Enum):\n PREVIOUS_VALUE = 1\n NIL_VALUE = 2\n\n\n# endregion\n\n# This makes successive requests faster\nDATE = blpapi.Name(\"date\")\nERROR_INFO = blpapi.Name(\"errorInfo\")\nEVENT_TIME = blpapi.Name(\"EVENT_TIME\")\nFIELD_DATA = blpapi.Name(\"fieldData\")\nFIELD_EXCEPTIONS = blpapi.Name(\"fieldExceptions\")\nFIELD_ID = blpapi.Name(\"fieldId\")\nSECURITY = blpapi.Name(\"security\")\nSECURITY_DATA = blpapi.Name(\"securityData\")\n\n# region constants\nexceptions = 'exceptions'\nfield_id = blpapi.Name('fieldId')\nvalue_fld = blpapi.Name('value')\nreason = 'reason'\ncategory = 'category'\nsub_category = 'subcategory'\ndescription = 'description'\nerror_code = 'errorCode'\nsource = 'source'\nsecurity_error = 'securityError'\nmessage = 'message'\nresponse_error = 'responseError'\nsecurity_data = 'securityData'\nfield_exceptions = 'fieldExceptions'\nerror_info = 'errorInfo'\nfield_eid_data = 'eidData'\ndatetime = 'DATETIME'\nopen_fld = 'OPEN'\nhigh = 'HIGH'\nlow = 'LOW'\nclose = 'CLOSE'\nvolume = 'VOLUME'\nnumber_of_ticks = 'NUMBER_OF_TICKS'\nvalue = 'VALUE'\nreturn_eids = 'returnEids'\nmax_data_points = 'maxDataPoints'\nstart_date = 'startDate'\nend_date = 'endDate'\nstart_date_time = 'startDateTime'\nend_date_time = 'endDateTime'\nevent_type = 'eventType'\ngap_fill_initial_bar = 'gapFillInitialBar'\n\n# historical request settings\ncalendar_code_override = blpapi.Name('calendarCodeOverride')\ncurrency_code = blpapi.Name('currencyCode')\nperiodicity_adjustment = blpapi.Name('periodicityAdjustment')\nperiodicity_selection = blpapi.Name('periodicitySelection')\ncurrency = blpapi.Name('currency')\noverride_option = blpapi.Name('overrideOption')\npricing_option = blpapi.Name('pricingOption')\nnon_trading_day_fill_option = blpapi.Name('nonTradingDayFillOption')\nnon_trading_day_fill_method = blpapi.Name('nonTradingDayFillMethod')\nfollow_dpdf = blpapi.Name('adjustmentFollowDPDF')\nadjustment_split = blpapi.Name('adjustmentSplit')\nadjustment_normal = blpapi.Name('adjustmentNormal')\nadjustment_abnormal = blpapi.Name('adjustmentAbnormal')\noverride_currency = blpapi.Name('currency')\noverrides = blpapi.Name('overrides')\n# endregion\n\n# region Authorization Message Types\nauth_success = 'AuthorizationSuccess'\nauth_failure = 'AuthorizationFailure'\nauth_revoked = 'AuthorizationRevoked'\nauth_entitlement_changed = 'EntitlementChanged'\n# endregion\n\n\nif __name__ == '__main__':\n print('constants are defined')\n\n ","sub_path":"Caxton/panormus_OLD/data/bloomberg_config.py","file_name":"bloomberg_config.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"207059657","text":"#It was proposed by Christian Goldbach that every odd composite number can be written as the sum of a prime and twice a square.\n\n#9 = 7 + 2×1^2\n#15 = 7 + 2×2^2\n#21 = 3 + 2×3^2\n#25 = 7 + 2×3^2\n#27 = 19 + 2×2^2\n#33 = 31 + 2×1^2\n\n#It turns out that the conjecture was false.\n\n#What is the smallest odd composite that cannot be written as the sum of a prime and twice a square?\n\n#Sieve of Marouane states that all odd composites can be made from p**2 +2*p*c where p is a prime number other than 2 and c is a constant \n#the key to this problem is solving for a the min of the function that does not allow the above equation to be equal to another p added to a square \nimport math\nimport timeit\nprimes = []\ndef Esieve(n): \n # Create a boolean array \"prime[0..n]\" and initialize \n # all entries it as true. A value in prime[i] will \n # finally be false if i is Not a prime, else true. \n prime = [True for i in range(n+1)] \n p = 2\n while (p * p <= n): \n \n # If prime[p] is not changed, then it is a prime \n if (prime[p] == True): \n \n # Update all multiples of p \n for i in range(p * p, n+1, p): \n prime[i] = False\n p += 1\n \n # Print all prime numbers \n for p in range(2, n): \n if prime[p]: \n primes.append(p)\n \ndef Problem46():\n Esieve(10000)\n odd = 33\n answer = 0\n i=0\n while answer == 0:\n if odd >= primes[i]:\n if math.sqrt((odd - primes[i])/2).is_integer() == True:\n odd+=2\n i=0\n else:\n i += 1\n else:\n answer = 1\n print('The smallest counter example is',odd)\n \nelapsed_time = timeit.timeit(Problem46, number=10)/10\nprint(elapsed_time)\n\n \n \n \n","sub_path":"Project Euler/Complete/046_Goldbachs_conjecture.py","file_name":"046_Goldbachs_conjecture.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"226797464","text":"# PREUD'HOMME BONTOUX Geoffrey - PeiP 12 - 2014/2015\n# TP n°4 donné le 3/10/2014 - Tables de multiplication\n# http://www.fil.univ-lille1.fr/~wegrzyno/portail/Info/Doc/HTML/tp_iteration_conditionnelle.html\n\n# [Q1] Écrivez une procédure qui affiche la table de multiplication par\n# un nombre k sous la forme donnée ci-après.\ndef imprimer_table(k):\n \"\"\"\n Affiche la table de multiplication par un nombre k.\n\n CU : k entier\n \"\"\"\n assert(type(k) is int), \"k doit être un entier\"\n\n for i in range(1, 11):\n print(k, \"×\", i, \"=\", k*i)\n\n# [Test]\n# >>> imprimer_table(7)\n# 7 x 1 = 7\n# 7 x 2 = 14\n# 7 x 3 = 21\n# 7 x 4 = 28\n# 7 x 5 = 35\n# 7 x 6 = 42\n# 7 x 7 = 49\n# 7 x 8 = 56\n# 7 x 9 = 63\n# 7 x 10 = 70\n\n# [Q2] Donnez une séquence d’instructions permettant d’avoir l’affichage\n# de toutes les tables de multiplication de 1 jusqu’à 10.\n# (En commentaire pour éviter de polluer la console)\n\nfor table in range(1, 11):\n imprimer_table(table)\n","sub_path":"S1/TP 4/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"263334449","text":"from pprint import pprint\nfrom behave import given, when, then\nfrom selenium.webdriver.common.by import By\n\nALL_HEADER_ELEMENTS = (By.CSS_SELECTOR, '#nav-xshop .nav-a')\nFIRST_HEADER_ELEMENT = (By.CSS_SELECTOR, '#nav-xshop .nav-a:first-of-type')\nSECOND_HEADER_ELEMENT = (By.CSS_SELECTOR, '#nav-xshop .nav-a:nth-of-type(2)')\n\n@when('Find all header links')\ndef find_all_header_links(context):\n elements = context.driver.find_elements(*ALL_HEADER_ELEMENTS)\n context.elements = elements\n # print(len(elements))\n pprint(elements)\n\n@then('Click elements in loop')\ndef elements_in_loop(context):\n for link in context.elements:\n # print(link)\n link.click()\n print(context.elements)\n print(context.driver.find_elements(*ALL_HEADER_ELEMENTS))\n\n@then('Click first link')\ndef click_first_link(context):\n first_element = context.driver.find_element(*FIRST_HEADER_ELEMENT)\n print(first_element)\n first_element.click()\n\n@then('Click second link')\ndef click_second_link(context):\n second_element = context.driver.find_element(*SECOND_HEADER_ELEMENT)\n print(second_element)\n second_element.click()\n","sub_path":"Amazon/features/steps/Header_link.py","file_name":"Header_link.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"292738402","text":"# ----------------------------------------------------------------------\r\n# |\r\n# | Activate_custom.py\r\n# |\r\n# | David Brownell \r\n# | 2018-05-07 08:59:57\r\n# |\r\n# ----------------------------------------------------------------------\r\n# |\r\n# | Copyright David Brownell 2018-22.\r\n# | Distributed under the Boost Software License, Version 1.0.\r\n# | (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\r\n# |\r\n# ----------------------------------------------------------------------\r\n\"\"\"Performs repository-specific activation activities.\"\"\"\r\n\r\nimport os\r\nimport sys\r\n\r\nsys.path.insert(0, os.getenv(\"DEVELOPMENT_ENVIRONMENT_FUNDAMENTAL\"))\r\nfrom RepositoryBootstrap.SetupAndActivate import CommonEnvironment, CurrentShell\r\n\r\ndel sys.path[0]\r\n\r\n# ----------------------------------------------------------------------\r\n_script_fullpath = CommonEnvironment.ThisFullpath()\r\n_script_dir, _script_name = os.path.split(_script_fullpath)\r\n# ----------------------------------------------------------------------\r\n\r\n# ' has no '' member> pylint: disable = E1101\r\n# pylint: disable = W0101\r\n# pylint: disable = W0613\r\n\r\n# ----------------------------------------------------------------------\r\ndef GetCustomActions(\r\n output_stream,\r\n configuration,\r\n version_specs,\r\n generated_dir,\r\n debug,\r\n verbose,\r\n fast,\r\n repositories,\r\n is_mixin_repo,\r\n):\r\n \"\"\"\r\n Returns an action or list of actions that should be invoked as part of the activation process.\r\n\r\n Actions are generic command line statements defined in\r\n /Libraries/Python/CommonEnvironment/v1.0/CommonEnvironment/Shell/Commands/__init__.py\r\n that are converted into statements appropriate for the current scripting language (in most\r\n cases, this is Bash on Linux systems and Batch or PowerShell on Windows systems.\r\n \"\"\"\r\n\r\n return []\r\n\r\n\r\n# ----------------------------------------------------------------------\r\ndef GetCustomActionsEpilogue(\r\n output_stream,\r\n configuration,\r\n version_specs,\r\n generated_dir,\r\n debug,\r\n verbose,\r\n fast,\r\n repositories,\r\n is_mixin_repo,\r\n):\r\n \"\"\"\r\n Returns an action or list of actions that should be invoked as part of the activation process. Note\r\n that this is called after `GetCustomActions` has been called for each repository in the dependency\r\n list.\r\n\r\n ****************************************************\r\n Note that it is very rare to have the need to implement\r\n this method. In most cases, it is safe to delete it.\r\n ****************************************************\r\n\r\n Actions are generic command line statements defined in\r\n /Libraries/Python/CommonEnvironment/v1.0/CommonEnvironment/Shell/Commands/__init__.py\r\n that are converted into statements appropriate for the current scripting language (in most\r\n cases, this is Bash on Linux systems and Batch or PowerShell on Windows systems.\r\n \"\"\"\r\n\r\n return []\r\n\r\n\r\n# ----------------------------------------------------------------------\r\ndef GetCustomScriptExtractors():\r\n \"\"\"\r\n Returns information that can be used to enumerate, extract, and generate documentation\r\n for scripts stored in the Scripts directory in this repository and all repositories\r\n that depend upon it.\r\n\r\n ****************************************************\r\n Note that it is very rare to have the need to implement\r\n this method. In most cases, it is safe to delete it.\r\n ****************************************************\r\n\r\n There concepts are used with custom script extractors:\r\n\r\n - DirGenerator: Method to enumerate sub-directories when searching for scripts in a\r\n repository's Scripts directory.\r\n\r\n def Func(directory, version_sepcs) -> [ (subdir, should_recurse), ... ]\r\n [ subdir, ... ]\r\n (subdir, should_recurse)\r\n subdir\r\n\r\n - CreateCommands: Method that creates the shell commands to invoke a script.\r\n\r\n def Func(script_filename) -> [ command, ...]\r\n command\r\n None # Indicates not supported\r\n\r\n - CreateDocumentation: Method that extracts documentation from a script.\r\n\r\n def Func(script_filename) -> documentation string\r\n\r\n - ScriptNameDecorator: Returns a new name for the script.\r\n\r\n def Func(script_filename) -> name string\r\n\r\n See /Activate_custom.py for an example of how script extractors\r\n are used to process Python and PowerShell scripts.\r\n \"\"\"\r\n\r\n return\r\n","sub_path":"Libraries/Python/CommonEnvironment/v1.0/CommonEnvironment/TestParserImpl/SimpleSchemaBuildEnvironment/Activate_custom.py","file_name":"Activate_custom.py","file_ext":"py","file_size_in_byte":5225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"393775757","text":"##################################################\n# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #\n###########################################################################\n# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #\n###########################################################################\nimport sys, time, random, argparse\nfrom copy import deepcopy\nimport torch\nfrom pathlib import Path\nlib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()\nif str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))\nfrom config_utils import load_config, dict2config\nfrom datasets import get_datasets, get_nas_search_loaders\nfrom procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler\nfrom utils import get_model_infos, obtain_accuracy\nfrom log_utils import AverageMeter, time_string, convert_secs2time\nfrom models import get_cell_based_tiny_net, get_search_spaces\nfrom nas_201_api import NASBench201API as API\n\nfrom datasets.get_dataset_with_transform import CUTOUT\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\nimport torch.nn.functional as F\nfrom utils.min_norm_solvers import MinNormSolver, gradient_normalizers\n\ndef clamp(X, lower_limit, upper_limit):\n return torch.max(torch.min(X, upper_limit), lower_limit)\n\ndef search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, xargs, logger, ood_loader=None):\n data_time, batch_time = AverageMeter(), AverageMeter()\n base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()\n arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()\n network.train()\n end = time.time()\n for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):\n base_inputs = base_inputs.cuda(non_blocking=True)\n arch_inputs = arch_inputs.cuda(non_blocking=True)\n if xargs.adv_outer:\n arch_inputs.requires_grad = True\n scheduler.update(None, 1.0 * step / len(xloader))\n base_targets = base_targets.cuda(non_blocking=True)\n arch_targets = arch_targets.cuda(non_blocking=True)\n\n if xargs.ood_inner or xargs.ood_outer:\n try:\n ood_input, _ = next(ood_loader_iter)\n except:\n ood_loader_iter = iter(ood_loader)\n ood_input, _ = next(ood_loader_iter)\n ood_input = ood_input.cuda(non_blocking=True)\n\n # measure data loading time\n data_time.update(time.time() - end)\n \n # update the weights\n w_optimizer.zero_grad()\n _, logits, _, _ = network(base_inputs)\n base_loss = criterion(logits, base_targets)\n if xargs.ood_inner and ood_loader is not None:\n _, ood_logits, _, _ = network(ood_input)\n ood_loss = F.kl_div(input=F.log_softmax(ood_logits, dim=-1), target=torch.ones_like(ood_logits)/ood_logits.size()[-1])\n base_loss += ood_loss\n base_loss.backward()\n torch.nn.utils.clip_grad_norm_(network.parameters(), 5)\n w_optimizer.step()\n # record\n base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5))\n base_losses.update(base_loss.item(), base_inputs.size(0))\n base_top1.update (base_prec1.item(), base_inputs.size(0))\n base_top5.update (base_prec5.item(), base_inputs.size(0))\n\n # update the architecture-weight\n a_optimizer.zero_grad()\n grads = {}\n loss_data = {}\n # ---- acc loss ----\n _, acc_logits, nop_loss, flp_loss = network(arch_inputs)\n acc_loss = criterion(acc_logits, arch_targets)\n loss_data['acc'] = acc_loss.item()\n grads['acc'] = list(torch.autograd.grad(acc_loss, network.get_alphas(), retain_graph=True))\n # del acc_logits\n # ---- end ----\n\n # ---- nop loss ----\n if xargs.nop_outer:\n if xargs.nop_constrain == 'abs':\n nop_loss = torch.abs(xargs.nop_constrain_min - nop_loss)\n loss_data['nop'] = nop_loss.item()\n grads['nop'] = list(torch.autograd.grad(nop_loss, network.get_alphas(), retain_graph=True))\n # ---- end ----\n\n # ---- flp loss ----\n if xargs.flp_outer:\n if xargs.flp_constrain == 'abs':\n flp_loss = torch.abs(xargs.flp_constrain_min - flp_loss)\n loss_data['flp'] = flp_loss.item()\n grads['flp'] = list(torch.autograd.grad(flp_loss, network.get_alphas(), retain_graph=True))\n # ---- end ----\n\n # ---- ood loss ----\n if xargs.ood_outer and ood_loader is not None:\n _, ood_logits, _, _ = network(ood_input)\n ood_loss = F.kl_div(input=F.log_softmax(ood_logits), target=torch.ones_like(ood_logits)/ood_logits.size()[-1])\n loss_data['ood'] = ood_loss.item()\n grads['ood'] = list(torch.autograd.grad(ood_loss, network.get_alphas(), retain_graph=True))\n del ood_logits\n # ---- end ----\n\n # ---- adv loss ----\n if xargs.adv_outer:\n if xargs.dataset == 'cifar10':\n mean = (0.4914, 0.4822, 0.4465)\n std = (0.2471, 0.2435, 0.2616)\n elif xargs.dataset == 'cifar100':\n mean = (0.5071, 0.4867, 0.4408)\n std = (0.2675, 0.2565, 0.2761)\n mean = torch.FloatTensor(mean).view(3,1,1)\n std = torch.FloatTensor(std).view(3,1,1)\n upper_limit = ((1 - mean)/ std).cuda()\n lower_limit = ((0 - mean)/ std).cuda()\n epsilon = ((xargs.epsilon / 255.) / std).cuda()\n step_size = epsilon * 1.25\n delta = ((torch.rand(arch_inputs.size())-0.5)*2).cuda() * epsilon\n adv_grad = torch.autograd.grad(acc_loss, arch_inputs, retain_graph=True, create_graph=False)[0]\n adv_grad = adv_grad.detach().data\n delta = clamp(delta + step_size * torch.sign(adv_grad), -epsilon, epsilon)\n delta = clamp(delta, lower_limit - arch_inputs.data, upper_limit - arch_inputs.data)\n adv_input = (arch_inputs.data + delta).cuda()\n _, adv_logits, _, _ = network(adv_input)\n adv_loss = criterion(adv_logits, arch_targets)\n loss_data['adv'] = adv_loss.item()\n grads['adv'] = list(torch.autograd.grad(adv_loss, network.get_alphas(), retain_graph=True))\n del mean, std, upper_limit, lower_limit, epsilon, step_size, delta, adv_grad, adv_input, adv_logits\n # ---- end ----\n\n # ---- MGDA ----\n gn = gradient_normalizers(grads, loss_data, normalization_type=xargs.grad_norm) # loss+, loss, l2\n\n for t in grads:\n for gr_i in range(len(grads[t])):\n grads[t][gr_i] = grads[t][gr_i] / (gn[t]+1e-7)\n \n if xargs.MGDA and (len(grads)>1):\n sol, _ = MinNormSolver.find_min_norm_element([grads[t] for t in grads])\n print(sol) # acc, adv, nop\n else:\n sol = [1] * len(grads)\n\n arch_loss = 0\n for kk, t in enumerate(grads):\n if t == 'acc':\n arch_loss += float(sol[kk]) * acc_loss\n elif t == 'adv':\n arch_loss += float(sol[kk]) * adv_loss\n elif t == 'nop':\n arch_loss += float(sol[kk]) * nop_loss\n elif t == 'ood':\n arch_loss += float(sol[kk]) * ood_loss\n elif t == 'flp':\n arch_loss += float(sol[kk]) * flp_loss\n # ---- end ----\n\n arch_loss.backward()\n a_optimizer.step()\n # record\n arch_prec1, arch_prec5 = obtain_accuracy(acc_logits.data, arch_targets.data, topk=(1, 5))\n arch_losses.update(arch_loss.item(), arch_inputs.size(0))\n arch_top1.update (arch_prec1.item(), arch_inputs.size(0))\n arch_top5.update (arch_prec5.item(), arch_inputs.size(0))\n\n # measure elapsed time\n batch_time.update(time.time() - end)\n end = time.time()\n\n if step % xargs.print_freq == 0 or step + 1 == len(xloader):\n Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader))\n Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)\n Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)\n Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)\n logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)\n return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg\n\n\ndef main(xargs):\n assert torch.cuda.is_available(), 'CUDA is not available.'\n torch.backends.cudnn.enabled = True\n torch.backends.cudnn.benchmark = False\n torch.backends.cudnn.deterministic = True\n torch.set_num_threads( xargs.workers )\n prepare_seed(xargs.rand_seed)\n logger = prepare_logger(args)\n\n train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)\n #config_path = 'configs/nas-benchmark/algos/GDAS.config'\n config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)\n search_loader, _, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', config.batch_size, xargs.workers)\n \n if xargs.ood_inner or xargs.ood_outer:\n mean = [x / 255 for x in [125.3, 123.0, 113.9]]\n std = [x / 255 for x in [63.0, 62.1, 66.7]]\n # lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]\n lists = [transforms.ToTensor(), transforms.Normalize(mean, std)]\n # lists += [CUTOUT(-1)]\n ood_transform = transforms.Compose(lists)\n ood_data = dset.SVHN(root=args.data_path, split='train', download=True, transform=ood_transform)\n\n ood_loader = torch.utils.data.DataLoader(ood_data, batch_size=config.batch_size,\n sampler=torch.utils.data.sampler.SubsetRandomSampler(list(range(len(ood_data)))[:len(train_data)]),\n pin_memory=True, num_workers=xargs.workers)\n else:\n ood_loader = None\n\n logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), config.batch_size))\n logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))\n\n global search_space\n search_space = get_search_spaces('cell', xargs.search_space_name)\n if xargs.model_config is None:\n model_config = dict2config({'name': 'GDAS', 'C': xargs.channel, 'N': xargs.num_cells,\n 'max_nodes': xargs.max_nodes, 'num_classes': class_num,\n 'space' : search_space,\n 'affine' : False, 'track_running_stats': bool(xargs.track_running_stats),}, None)\n else:\n model_config = load_config(xargs.model_config, {'num_classes': class_num, 'space' : search_space,\n 'affine' : False, 'track_running_stats': bool(xargs.track_running_stats)}, None)\n search_model = get_cell_based_tiny_net(model_config)\n # logger.log('search-model :\\n{:}'.format(search_model))\n logger.log('model-config : {:}'.format(model_config))\n \n w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)\n a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay)\n logger.log('w-optimizer : {:}'.format(w_optimizer))\n logger.log('a-optimizer : {:}'.format(a_optimizer))\n logger.log('w-scheduler : {:}'.format(w_scheduler))\n logger.log('criterion : {:}'.format(criterion))\n flop, param = get_model_infos(search_model, xshape)\n logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))\n logger.log('search-space [{:} ops] : {:}'.format(len(search_space), search_space))\n if xargs.arch_nas_dataset is None:\n api = None\n else:\n api = API(xargs.arch_nas_dataset)\n logger.log('{:} create API = {:} done'.format(time_string(), api))\n\n last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best')\n # network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()\n network, criterion = search_model.cuda(), criterion.cuda()\n\n if last_info.exists(): # automatically resume from previous checkpoint\n logger.log(\"=> loading checkpoint of the last-info '{:}' start\".format(last_info))\n last_info = torch.load(last_info)\n start_epoch = last_info['epoch']\n checkpoint = torch.load(last_info['last_checkpoint'])\n genotypes = checkpoint['genotypes']\n valid_accuracies = checkpoint['valid_accuracies']\n search_model.load_state_dict( checkpoint['search_model'] )\n w_scheduler.load_state_dict ( checkpoint['w_scheduler'] )\n w_optimizer.load_state_dict ( checkpoint['w_optimizer'] )\n a_optimizer.load_state_dict ( checkpoint['a_optimizer'] )\n logger.log(\"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.\".format(last_info, start_epoch))\n else:\n logger.log(\"=> do not find the last-info file : {:}\".format(last_info))\n start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {-1: search_model.genotype()}\n\n # start training\n start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup\n for epoch in range(start_epoch, total_epoch):\n w_scheduler.update(epoch, 0.0)\n need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )\n epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)\n search_model.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) )\n logger.log('\\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}'.format(epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr())))\n\n search_w_loss, search_w_top1, search_w_top5, valid_a_loss , valid_a_top1 , valid_a_top5 \\\n = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs, logger, ood_loader)\n search_time.update(time.time() - start_time)\n logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum))\n logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss , valid_a_top1 , valid_a_top5 ))\n # check the best accuracy\n valid_accuracies[epoch] = valid_a_top1\n if valid_a_top1 > valid_accuracies['best']:\n valid_accuracies['best'] = valid_a_top1\n genotypes['best'] = search_model.genotype()\n find_best = True\n else: find_best = False\n\n genotypes[epoch] = search_model.genotype()\n logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))\n # save checkpoint\n save_path = save_checkpoint({'epoch' : epoch + 1,\n 'args' : deepcopy(xargs),\n 'search_model': search_model.state_dict(),\n 'w_optimizer' : w_optimizer.state_dict(),\n 'a_optimizer' : a_optimizer.state_dict(),\n 'w_scheduler' : w_scheduler.state_dict(),\n 'genotypes' : genotypes,\n 'valid_accuracies' : valid_accuracies},\n model_base_path, logger)\n last_info = save_checkpoint({\n 'epoch': epoch + 1,\n 'args' : deepcopy(args),\n 'last_checkpoint': save_path,\n }, logger.path('info'), logger)\n if find_best:\n logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))\n copy_checkpoint(model_base_path, model_best_path, logger)\n with torch.no_grad():\n logger.log('{:}'.format(search_model.show_alphas()))\n if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '200')))\n # measure elapsed time\n epoch_time.update(time.time() - start_time)\n start_time = time.time()\n\n logger.log('\\n' + '-'*100)\n # check the performance from the architecture dataset\n logger.log('GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(total_epoch, search_time.sum, genotypes[total_epoch-1]))\n if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[total_epoch-1], '200')))\n logger.close()\n \n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(\"GDAS\")\n parser.add_argument('--data_path', type=str, help='Path to dataset')\n parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')\n # channels and number-of-cells\n parser.add_argument('--search_space_name', type=str, help='The search space name.')\n parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')\n parser.add_argument('--channel', type=int, help='The number of channels.')\n parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')\n parser.add_argument('--track_running_stats',type=int, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.')\n parser.add_argument('--config_path', type=str, help='The path of the configuration.')\n parser.add_argument('--model_config', type=str, help='The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.')\n # architecture leraning rate\n parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')\n parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding')\n parser.add_argument('--tau_min', type=float, help='The minimum tau for Gumbel')\n parser.add_argument('--tau_max', type=float, help='The maximum tau for Gumbel')\n # log\n parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')\n parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')\n parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')\n parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')\n parser.add_argument('--rand_seed', type=int, help='manual seed')\n # E2RNAS\n parser.add_argument('--nop_outer', default=False, action='store_true', help='use nop in outer loop')\n parser.add_argument('--flp_outer', default=False, action='store_true', help='use flp in outer loop')\n parser.add_argument('--adv_outer', default=False, action='store_true', help='use adv in outer loop')\n parser.add_argument('--ood_outer', default=False, action='store_true', help='use ood in outer loop')\n parser.add_argument('--ood_inner', default=False, action='store_true', help='use ood in inner loop')\n parser.add_argument('--MGDA', default=False, action='store_true', help='use MGDA')\n parser.add_argument('--grad_norm', type=str, default='none', choices=['none', 'lossplus', 'loss', 'l2'], help='use gradient normalization in MGDA')\n parser.add_argument('--nop_constrain', type=str, default='none', choices=['max', 'min', 'both', 'abs', 'none'], help='use constraint in model size')\n parser.add_argument('--nop_constrain_min', type=float, default=0, help='constrain the model size')\n parser.add_argument('--flp_constrain', type=str, default='none', choices=['max', 'min', 'both', 'abs', 'none'], help='use constraint in model size')\n parser.add_argument('--flp_constrain_min', type=float, default=0, help='constrain the model size')\n parser.add_argument('--epsilon', default=2, type=int)\n args = parser.parse_args()\n if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)\n main(args)\n","sub_path":"exps/algos/GDAS.py","file_name":"GDAS.py","file_ext":"py","file_size_in_byte":19786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"17669799","text":"import datetime\nimport time\nimport urllib.request as socket\nimport urllib.error as urlerr\nfrom urllib.parse import quote\nfrom DB_class.user_param.param_private import *\n\n\nclass CommToApiServer:\n _apiKey: str\n _apiURL = \"https://api.neople.co.kr/cy/\"\n _apiWordType = \"wordType=full\"\n _innerErrCode = 0\n __api_start_time = []\n _api_error_list = []\n\n _itemList = {\n 0: \"characterId\",\n 1: \"slotCode\",\n 2: \"rarityCode\",\n 3: \"seasonCoe\"\n }\n _permissions = {\n 200: \"정상적인 응답\",\n 400: \"요청에 대한 유효성 검증 실패 또는 필수 파라미터 에러\",\n 401: \"인증 오류\",\n 404: \"존재하지 않은 리소스 또는 페이지\",\n 500: \"시스템 오류\",\n 503: \"시스템 점검\",\n 1900: \"매개변수 공백 입력\", # 매개변수 공백일시 urllib Error 발생함\n 1901: \"매개변수 타입 에러\", # 매개변수 타입 설정 오류발생\n 1092: \"초기값 없는 Next 명령 기입\"\n }\n\n def __init__(self, param_api_key: str):\n _apiKey = param_api_key\n\n @staticmethod\n def _api_communication_count():\n if len(CommToApiServer.__api_start_time) is 0:\n CommToApiServer.__api_start_time.append(datetime.datetime.now())\n else:\n CommToApiServer.__api_start_time.append(datetime.datetime.now())\n api_cnt = len(CommToApiServer.__api_start_time)\n span_time = (CommToApiServer.__api_start_time[api_cnt - 1]\n - CommToApiServer.__api_start_time[0])\n\n if (span_time.total_seconds() < 1) & (api_cnt >= 100):\n time.sleep(1 - span_time.total_seconds() + 0.3)\n if api_cnt >= 100:\n del CommToApiServer.__api_start_time[0]\n\n def _get_api_body(self, request_url):\n # 횟수 제한 확인\n self._api_communication_count()\n err_return = {}\n for i in range(5):\n try:\n response = socket.urlopen(request_url)\n res_code = response.getcode()\n response_body = response.read().decode('utf-8')\n return self._get_response_code(res_code, response_body)\n except urlerr.HTTPError as err:\n err_return = {\"code\": err.code,\n \"explain\": err.reason,\n \"body\": \"\"}\n print(\"HTTP Error !!\")\n print(err.reason)\n print(\"(\", i+1, \"/\", 5, \")retry...\")\n time.sleep(0.5)\n except urlerr.URLError as err:\n err_return = {\"explain\": err.reason,\n \"body\": \"\"}\n print(\"URL Error !!\")\n print(err.reason)\n print(\"(\", i+1, \"/\", 5, \")retry...\")\n time.sleep(0.5)\n err_log_dict = err_return.copy()\n err_log_dict[\"url\"] = request_url\n self._api_error_list.append(err_log_dict)\n return err_return\n\n def get_api_error_list(self):\n return self._api_error_list.copy()\n\n def del_api_error_list(self):\n self._api_error_list = []\n\n def _get_response_code(self, code, response_body=\"\"):\n api_return = {\"code\": code,\n \"explain\": self._permissions.get(code),\n \"body\": response_body}\n self._innerErrCode = 0\n return api_return\n\n def __check_str_blank(self, str_param):\n if not str_param:\n self._innerErrCode = 1900\n return False\n return True\n\n def __check_str_type(self, str_param):\n if not isinstance(str_param, str): # str type check\n self._innerErrCode = 1901\n return False\n return True\n\n def __check_int_type(self, int_param):\n if not isinstance(int_param, int): # int type check\n self._innerErrCode = 1901\n return False\n return True\n\n def _check_name(self, name_param):\n return self.__check_str_blank(name_param)\n\n def _check_id(self, id_param):\n return self.__check_str_blank(id_param) & self.__check_str_type(id_param)\n\n def _check_int_value(self, int_param):\n return self.__check_int_type(int_param)\n\n def _check_type(self, type_param):\n return self.__check_str_blank(type_param) & self.__check_str_type(type_param)\n\n def lookup_nickname(self, nickname, limit=1):\n ret = self._check_name(nickname) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n nickname_url = quote(nickname)\n request_url = self._apiURL + \"players?nickname=\" + nickname_url + \"&\" + self._apiWordType + \"&limit=\" \\\n + str(limit) + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_player_info(self, player_id):\n ret = self._check_id(player_id)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"players/\" + player_id + \"?\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_player_match(self, player_id, game_type=\"rating\", limit=100, start_date=None, end_date=None):\n ret = self._check_id(player_id) & self._check_type(game_type) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"players/\" + player_id + \"/matches?gameTypeId=\" + game_type\n if isinstance(start_date, datetime.date) and isinstance(end_date, datetime.date):\n startDate = start_date.strftime(\"%Y%m%dT0000\")\n endDate = end_date.strftime(\"%Y%m%dT0000\")\n request_url = request_url + \"&startDate=\" + startDate + \"&endDate=\" + endDate\n request_url = request_url + \"&limit=\" + str(limit) + \"&\" + self._apiKey\n # print(request_url)\n return self._get_api_body(request_url)\n\n def lookup_player_match_next(self, player_id, next_code):\n ret = self._check_id(player_id) & self.__check_str_blank(next_code)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"players/\" + player_id + \"/matches?next=\" + next_code + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_match_info(self, match_id):\n ret = self._check_id(match_id)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"matches/\" + match_id + \"?\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_player_rating_ranking(self, player_id):\n ret = self._check_id(player_id)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"ranking/ratingpoint?playerId=\" + player_id + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n # offset: start ranking number\n # limit: output number (max : 1000)\n def lookup_total_rating_ranking(self, offset=0, limit=10):\n ret = self._check_int_value(offset) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"ranking/ratingpoint?offset=\" + str(offset) + \"&limit=\" + str(\n limit) + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_player_character_ranking(self, player_id, character_id, ranking_type=\"exp\"):\n ret = self._check_id(player_id) & self._check_id(character_id) & self._check_type(ranking_type)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"ranking/characters/\" + character_id + \"/\" + ranking_type + \"?playerId=\" \\\n + player_id + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_total_character_ranking(self, character_id, ranking_type=\"exp\", offset=0, limit=10):\n ret = self._check_id(character_id) & self._check_type(ranking_type) & \\\n self._check_int_value(offset) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"ranking/characters/\" + character_id + \"/\" + ranking_type + \"?offset=\" \\\n + str(offset) + \"&limit=\" + str(limit) + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def search_item_name(self, item_name, word_type=\"full\", limit=10):\n ret = self._check_name(item_name) & self._check_type(word_type) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n item_name = quote(item_name)\n request_url = self._apiURL + \"battleitems?itemName=\" + item_name + \"&wordType=\" + word_type \\\n + \"&limit=\" + str(limit) + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def search_item(self, item_name, word_type=\"match\", limit=10, q_lst=None):\n ret = self._check_name(item_name) & self._check_type(word_type) & self._check_int_value(limit)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n opt_url = \"\"\n div_value = {\n 1: \";\",\n 2: \",\",\n 3: \",\"\n }\n if q_lst is not None:\n number = 0\n opt_url = \"&q=\"\n for opt_value in q_lst:\n opt_url = opt_url + div_value.get(number, \"\") + self._itemList[number] + \":\" + opt_value\n number = number + 1\n\n item_name = quote(item_name)\n request_url = self._apiURL + \"battleitems?itemName=\" + item_name + \"&wordType=\" + word_type + \"&limit=\" \\\n + str(limit) + opt_url + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_item_info(self, item_id):\n ret = self._check_id(item_id)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"battleitems/\" + item_id + \"?\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_item_multi_info(self, item_id_lst=None):\n if item_id_lst is None:\n item_id_lst = []\n\n item_url = \"\"\n for idx, item_id in enumerate(item_id_lst):\n item_url = item_url + item_id\n if idx != len(item_id_lst)-1:\n item_url = item_url + \",\"\n request_url = self._apiURL + \"multi/battleitems/?itemIds=\" + item_url + \"&\" + self._apiKey\n return self._get_api_body(request_url)\n\n def get_character_info(self):\n request_url = self._apiURL + \"characters?\" + self._apiKey\n return self._get_api_body(request_url)\n\n def lookup_position_attribute(self, attribute_id):\n ret = self._check_id(attribute_id)\n if ret is not True:\n return self._get_response_code(self._innerErrCode)\n\n request_url = self._apiURL + \"position-attributes/\" + attribute_id + \"?\" + self._apiKey\n return self._get_api_body(request_url)\n","sub_path":"api_comm.py","file_name":"api_comm.py","file_ext":"py","file_size_in_byte":11300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"138104920","text":"path='./popular-names.txt'\naggregation=set()\nwith open(path, mode='r') as file:\n\tlines=list(file)\nnew_lines=sorted(lines, key=lambda x: x.split('\\t')[2])\nfor val in new_lines:\n\tprint(val.replace('\\n',''))\n\n###ANS###\n# lines.sort(key=lambda line: -int(line.split()[2]))","sub_path":"HinataKikuchi/chapter02/knock18.py","file_name":"knock18.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"331777759","text":"# -*- coding:utf-8 -*-\n\"\"\"\n Author : 'longguangbin'\n Contact : lgb453476610@163.com\n Date : 2018/10/24\n Usage :\n\"\"\"\n\nfrom __future__ import print_function\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\nfrom pyramid.arima import auto_arima\nfrom pyramid.arima.utils import ndiffs\nfrom pyramid.arima.utils import nsdiffs\n\nimport matplotlib.pyplot as plt\n\nplt.style.use('seaborn')\n\n\nclass ArimaModel(object):\n\n def __init__(self):\n self.model_name = None\n self.model = None\n self.pred = None\n self.org_data = None\n self.pre_len = None\n\n def autoarima(self, data, pre_len=30):\n D_f = nsdiffs(data, m=3, max_D=5, test='ch')\n d_f = ndiffs(data, alpha=0.05, test='kpss', max_d=5)\n if len(data) <= 30:\n seasonal = False\n else:\n seasonal = True\n try:\n stepwise_fit = auto_arima(data, start_p=0, start_q=0, max_p=3, max_q=3, m=12,\n start_P=0, seasonal=seasonal, d=d_f, D=D_f, trace=False,\n error_action='ignore', # don't want to know if an order does not work\n suppress_warnings=True, # don't want convergence warnings\n stepwise=True) # set to stepwise\n except:\n stepwise_fit = auto_arima(data, start_p=0, start_q=0, max_p=3, max_q=0, m=12,\n start_P=0, seasonal=False, d=0, D=0, trace=False,\n error_action='ignore', # don't want to know if an order does not work\n suppress_warnings=True, # don't want convergence warnings\n stepwise=True) # set to stepwise\n output = stepwise_fit.predict(n_periods=pre_len)\n\n self.model_name = 'autoarima'\n self.model = stepwise_fit\n self.pred = output\n self.org_data = data\n self.pre_len = pre_len\n return output\n\n def plot_pre(self):\n data = self.org_data\n pre_len = self.pre_len\n output = self.pred\n\n real_index = range(len(data))\n pre_index = range(len(data), len(data) + pre_len)\n\n fig = plt.figure(figsize=(12, 6))\n ax = fig.add_subplot(111)\n ax.plot(real_index, data)\n ax.plot(pre_index, output)\n plt.show()\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"src/ts_models/arima/test_arima2.py","file_name":"test_arima2.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"182152613","text":"import os\nimport csv\nimport networkx as nx\nimport random\n\nclass GPS:\n def R2(self, G, A, B):\n if A and B:\n edge = list(G.edges)\n Ne = 0\n for u in A:\n for v in B:\n if (u, v) in edge or (v, u) in edge:\n Ne += 1\n R_AB = Ne / (len(A) * len(B))\n else:\n R_AB = 0\n return(R_AB)\n\n\n\n def R1(self, G, A):\n if A:\n edge = list(G.edges)\n Ne = 0\n for i in range(len(A) - 1):\n for j in range(i + 1, len(A)):\n if (A[i], A[j]) in edge or (A[j], A[i]) in edge:\n Ne += 1\n if len(A) - 1 != 0:\n R_A = 2 * Ne / (len(A) * (len(A) - 1))\n else:\n R_A = 0\n else:\n R_A = 0\n return(R_A)\n\n\n def edgeWeightComputing(self, G):\n for u, v in G.edges:\n u_neighbor = list(G.neighbors(u))\n v_neighbor = list(G.neighbors(v))\n Vuv = set(u_neighbor) & set(v_neighbor)\n Vu = list(set(u_neighbor) - Vuv)\n Vv = list(set(v_neighbor) - Vuv)\n Vuv = list(Vuv)\n self.R2(G, Vu, Vuv)\n cycle_ratio = len(Vuv) / (len(Vu) + len(Vv) + len(Vuv))\n EWe = self.R2(G, Vu, Vuv) + self.R2(G, Vu, Vv) + self.R2(G, Vuv, Vv) + \\\n self.R1(G, Vuv) + cycle_ratio\n G[u][v]['Ewe'] = EWe\n\n\n def GraphSampling(self, Gi, Gs, vs, max, size, p):\n if len(Gs) < size:\n edge = list(Gi.edges())\n Gs.add_node(vs)\n VGi = {}\n for vi in Gi.nodes():\n distance = len(nx.dijkstra_path(Gi, source=vi, target=vs)) - 1\n # print(vi,vs, distance)\n # print(distance)\n if distance == 0:\n continue\n if distance in VGi:\n VGi[distance].append(vi)\n else:\n VGi[distance] = []\n VGi[distance].append(vi)\n for u, v in VGi.items():\n for i in v:\n if len(Gs) < size:\n pf = random.random()\n if pf < p:\n Gs.add_node(i)\n else:\n break\n\n\n def filterEdges(self, G, eta, rate):\n G_copy = G.copy()\n Gs = nx.Graph()\n for u, v, d in G.edges(data='Ewe'):\n if d < eta:\n G_copy.remove_edge(u, v)\n for c in nx.connected_components(G_copy):\n Gi = G_copy.subgraph(c)\n path = nx.all_pairs_shortest_path(Gi)\n diameter = []\n max = 0\n for i in path:\n for u, v in i[1].items():\n if len(v) > max:\n max = len(v)\n diameter = [i[0], u]\n startpoint = random.choice(diameter)\n\n size = round(len(G) * rate)\n p = 0.6\n self.GraphSampling(Gi, Gs, startpoint, max, size, p)\n Gs = G.subgraph(Gs.nodes())\n self.getInfo(G, Gs)\n return(Gs)\n\n def getInfo(self, G, Gs):\n for node in G.nodes():\n if node in Gs.nodes():\n G.node[node]['class'] = 2\n else:\n G.node[node]['class'] = 1\n\n def GPS(self, G, rate):\n # Graph Partition Process\n self.edgeWeightComputing(G)\n eta = 0.8\n for u, v, d in G.edges(data='Ewe'):\n if d < eta:\n G[u][v]['filter'] = 1\n else:\n G[u][v]['filter'] = 0\n\n # Save_Graph_test(G, fn)\n Gs = self.filterEdges(G, eta, rate)\n return(Gs)","sub_path":"Experiment_2/GraphSampling/GPS.py","file_name":"GPS.py","file_ext":"py","file_size_in_byte":3760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308802901","text":"import numpy as np\nfrom WeatherForecast import WeatherForecast\nfrom ResponseDict import Instance as R\nfrom Plant import Plant\nimport random\n\ndef sample_one(*args):\n return random.sample(args, 1)[0]\n\nclass SpeechCenter:\n def make_response(self, plant):\n raise NotImplementedError()\n\n\nclass ExampleResponce(SpeechCenter):\n def __init__(self):\n self.examples = {}\n\n # 植物の状態に応じたテキストを生成します\n # TODO: ユーザーテキストが無い時のテキスト生成\n def make_response(self, plant, user_text=None):\n if user_text is None:\n return \"\"\n elif user_text in self.examples:\n return self.examples[user_text](plant)\n else:\n ret = sample_one(\"..？\", \"なに言っているの？\", \"よくわかんないや\")\n return ret \n\n def report_weather_forecast(self, postal_code):\n weather = WeatherForecast.get_weather(postal_code)\n if WeatherForecast.calc_average(weather) > 0:\n return \"今日は天気がいいから外に出して\"\n else:\n return \"今日はあまり天気が良くないね\"\n\n def say_nice_to_meet_you(self, plant: Plant):\n return \"はじめまして!\"\n\n def say_hello(self, plant: Plant):\n return sample_one( \"なに？\", \"呼んだ？\") \n\n def respond_see_you(self, plant: Plant):\n return sample_one( \"またね\", \"じゃあね\", \"バイバイ\") \n\n @staticmethod\n def make_self_introduce(plant: Plant):\n return sample_one(*R.IamPlant) % plant.display_name\n\n @staticmethod\n def respond_health(plant : Plant):\n response_msg = \"\"\n plant.sense_condition()\n need_water = plant.needWater()\n need_light = plant.needLuminesity()\n if need_water:\n response_msg += \"水が欲しいよ！\"\n\n if need_light:\n response_msg += \"光が欲しいよ\"\n\n if not need_light and not need_water:\n response_msg += \"元気だよ！\"\n if np.random.randint(0, 10) < 2:\n response_msg += \"\\nいつもありがとう(^^)\"\n\n return response_msg\n\n @staticmethod\n def respond_water_demand(plant):\n plant.sense_condition()\n response_msg = \"\"\n if plant.needWater():\n response_msg += sample_one(\"水が欲しいよ！\", \"うん！\", \"のどが渇いたな\")\n else:\n response_msg += sample_one(\"もう十分だよ\", \"いらないよー\", \"大丈夫だよ、ありがとう\")\n\n return response_msg\n\n @staticmethod\n def respond_light_demand(plant):\n response_msg = \"\"\n plant.sense_condition()\n if plant.needLuminesity():\n response_msg += sample_one(\"少し暗いかな\", \"明るいところに行きたいな\", \"光が欲しいよ\")\n else:\n response_msg += sample_one(\"ちょうどいいよ！\", \"気持ちいいよ！\", \"十分だよ\")\n\n return response_msg\n\n @staticmethod\n def respond_temperture(plant):\n response_msg = \"\"\n temp = plant.getTemperture()\n if temp == 0:\n response_msg += \"今日は寒すぎるよ\"\n elif temp == 1:\n response_msg += \"今日はきもちいいね！\"\n elif temp == 2:\n response_msg += \"今日は暑いね\"\n\n return response_msg\n","sub_path":"whisper/SpeechCenter.py","file_name":"SpeechCenter.py","file_ext":"py","file_size_in_byte":3380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"171501151","text":"import FWCore.ParameterSet.Config as cms\n\nfrom Configuration.Generator.PythiaUESettings_cfi import *\ngenerator = cms.EDFilter(\"Pythia6GeneratorFilter\",\n pythiaPylistVerbosity = cms.untracked.int32(0),\n # put here the efficiency of your filter (1. if no filter)\n filterEfficiency = cms.untracked.double(1.0),\n pythiaHepMCVerbosity = cms.untracked.bool(False),\n # put here the cross section of your process (in pb)\n crossSection = cms.untracked.double(6.01),\n comEnergy = cms.double(10000.0),\n maxEventsToPrint = cms.untracked.int32(0),\n PythiaParameters = cms.PSet(\n pythiaUESettingsBlock,\n processParameters = cms.vstring('MSEL=39 ! All SUSY processes ', \n 'IMSS(1) = 11 ! Spectrum from external SLHA file', \n 'IMSS(21) = 33 ! LUN number for SLHA File (must be 33) ', \n 'IMSS(22) = 33 ! Read-in SLHA decay table '),\n # This is a vector of ParameterSet names to be read, in this order\n parameterSets = cms.vstring('pythiaUESettings', \n 'processParameters', \n 'SLHAParameters'),\n SLHAParameters = cms.vstring(\"SLHAFILE = \\'Configuration/Generator/data/CSA07SUSYBSM_LM5_isasdkpyt_slha.out\\' ! Name of the SLHA spectrum file\")\n )\n)\n","sub_path":"Configuration/Generator/python/LM5_cfi.py","file_name":"LM5_cfi.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"558573572","text":"# inventory_allocator.py\n# Samuel Schmidt 2020-08-24\n\nclass InventoryAllocator():\n \n \"\"\" Originally made this entire class, but the task was updated halfway through\n so it's confined to a single function\n \"\"\"\n def process(self, order: dict, warehouses: list) -> list:\n error = None\n shipment = []\n \n # using rectified sign to increase score only if the order amount is greater than the \n # warehouse stock\n rectifiedSum = lambda oValue, wValue: \\\n sum([max(o - w, 0) for (o,w) in zip(oValue, wValue)])\n\n # assign an index to each of the warehouses for score calculation.\n # if an ordered item is not listed in the inventory, give it 0\n for i in range(len(warehouses)):\n warehouses[i]['idx'] = i\n\n for item in order.keys():\n if warehouses[i]['inventory'].get(item) is None:\n warehouses[i]['inventory'][item] = 0\n\n while len(order) > 0 and len(warehouses) > 0:\n # find the warehouse that is currently closest to completing our order\n best = min(warehouses, key=lambda w, o=order, s=rectifiedSum: \n s(o.values(), w['inventory'].values()) + w['idx']\n )\n removeList = []\n warehouseOrder = {best['name']: dict()} \n\n for item, amount in order.items():\n inventory = best['inventory'][item]\n\n if inventory != 0:\n\n if amount <= inventory: # order is satisfied\n warehouseOrder[best['name']][item] = amount\n removeList.append(item)\n else:\n warehouseOrder[best['name']][item] = inventory\n order[item] -= inventory\n\n for i in removeList: order.pop(i)\n\n if len(warehouseOrder[best['name']]) > 0: shipment.append(warehouseOrder)\n warehouses.remove(best)\n\n if len(order) != 0: return list()\n\n return shipment \n","sub_path":"inventory-allocator/src/inventory_allocator.py","file_name":"inventory_allocator.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"159688541","text":"\"\"\"\r\nCreated on Sun Aug 5 22:38:16 2018\r\n\r\n@author: Shilong_Wang, Hengyong_Yu, Boce_Zhang\r\n\"\"\"\r\n\r\nfrom __future__ import division, print_function, absolute_import\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport xlrd\r\n\r\ndata_test = xlrd.open_workbook('C:\\\\Data_test.xlsx')\r\ntable_test = data_test.sheets()[0]\r\ntest_nrows = table_test.nrows \r\ntest_ncols = table_test.ncols\r\n\r\ntest_datamatrix=np.zeros((test_nrows,test_ncols))\r\n\r\nfor x in range(test_ncols):\r\n test_cols =table_test.col_values(x) \r\n \r\n test_cols1=np.matrix(test_cols)\r\n\r\n test_datamatrix[:,x]=test_cols1\r\n \r\n \r\nspecies_test=np.zeros((test_nrows,1))\r\nspecies_test=test_datamatrix[:,0]-1\r\n\r\n\r\ny_species_test=tf.one_hot(species_test,5,on_value=1,off_value=None,axis=1)\r\n\r\nwith tf.Session()as sess:\r\n y_test = y_species_test.eval()\r\n \r\n\r\n\r\nx_test=np.zeros((test_nrows,63))\r\nx_test=test_datamatrix[:,2:65]\r\n\r\n\r\ndata_train = xlrd.open_workbook('C:\\\\Data_train.xlsx')\r\ntable_train = data_train.sheets()[0]\r\ntrain_nrows = table_train.nrows\r\ntrain_ncols = table_train.ncols\r\n\r\ntrain_datamatrix=np.zeros((train_nrows,train_ncols))\r\nfor x in range(train_ncols):\r\n train_cols =table_train.col_values(x) \r\n \r\n train_cols1=np.matrix(train_cols)\r\n\r\n train_datamatrix[:,x]=train_cols1\r\nspecies_train=np.zeros((train_nrows,1))\r\nspecies_train=train_datamatrix[:,0]-1\r\n\r\n\r\ny_species_train=tf.one_hot(species_train,5,on_value=1,off_value=None,axis=1)\r\n\r\nwith tf.Session()as sess:\r\n y_train = y_species_train.eval()\r\n \r\n\r\nx_train=np.zeros((train_nrows,63))\r\nx_train=train_datamatrix[:,2:65]\r\n \r\n\r\n\r\nlearning_rate = 0.02\r\n\r\nnum_steps = 15000\r\n\r\nbatch_size = 16\r\n\r\ndisplay_step = 1000\r\nexamples_to_show = 10\r\n\r\nnum_hidden_1 = 256\r\n\r\nnum_code=256\r\n\r\nnum_hidden_2 = 256\r\n\r\nnum_input = 63 \r\n\r\nnum_output = 5\r\n\r\n\r\ntrain_loss=np.zeros((num_steps//10,1))\r\ntest_loss=np.zeros((num_steps//10,1))\r\n\r\n\r\ndef weight_variable(shape,name):\r\n initial=tf.truncated_normal(shape,stddev=0.03)\r\n \r\n return tf.Variable(initial,name=name)\r\n\r\ndef bias_variable(shape,name):\r\n initial=tf.constant(0.2,shape=shape)\r\n \r\n return tf.Variable(initial,name=name)\r\n\r\n\r\n\r\nwith tf.name_scope('input'):\r\n x=tf.placeholder(tf.float32,[None,num_input],name='x_input')\r\n y=tf.placeholder(tf.float32,[None,num_output],name='y_input')\r\nwith tf.name_scope('hidden_1'):\r\n w1=weight_variable([num_input,num_hidden_1],name='w1')\r\n b1=bias_variable([num_hidden_1],name='b1')\r\n with tf.name_scope('node_1'):\r\n node_1=tf.matmul(x,w1)+b1\r\n with tf.name_scope('relu'):\r\n h_1=tf.nn.relu(node_1)\r\n\r\n\r\nwith tf.name_scope('encode'):\r\n w=weight_variable([num_hidden_1,num_code],name='w')\r\n b=bias_variable([num_code],name='b')\r\n with tf.name_scope('sum_encode'):\r\n sum_encode=tf.matmul(h_1,w)+b\r\n with tf.name_scope('relu'):\r\n h_encode=tf.nn.relu(sum_encode)\r\n\r\nwith tf.name_scope('decode'):\r\n w=weight_variable([num_code,num_hidden_2],name='w')\r\n b=bias_variable([num_hidden_2],name='b')\r\n with tf.name_scope('sum_decode'):\r\n sum_decode=tf.matmul(h_encode,w)+b\r\n with tf.name_scope('relu'):\r\n h_decode=tf.nn.relu(sum_decode)\r\n\r\n\r\nwith tf.name_scope('hidden_2'):\r\n \r\n w1=weight_variable([num_hidden_2,num_output],name='w1')\r\n b1=bias_variable([num_output],name='b1')\r\n with tf.name_scope('node_1'):\r\n node_1=tf.matmul(h_decode,w1)+b1\r\n with tf.name_scope('relu'):\r\n h_2=tf.nn.relu(node_1)\r\n with tf.name_scope('prediction'):\r\n prediction=tf.nn.softmax(h_2)\r\n\r\nwith tf.name_scope('loss_mean_square'):\r\n \r\n cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction),name='cross_entropy')\r\n \r\n tf.summary.scalar('cross',cross_entropy)\r\nwith tf.name_scope('train'):\r\n\r\n train_step=tf.train.AdamOptimizer(2e-6).minimize(cross_entropy)\r\n\r\n \r\n \r\nwith tf.name_scope('accuracy'):\r\n with tf.name_scope('correct_prediction'):\r\n correct_prediction= tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))\r\n with tf.name_scope('accuracy'):\r\n accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\r\n tf.summary.scalar('accuracy',accuracy)\r\nmerged=tf.summary.merge_all()\r\n\r\nwith tf.Session() as sess:\r\n\r\n sess.run(tf.global_variables_initializer())\r\n train_writer=tf.summary.FileWriter('logs/train',sess.graph)\r\n test_writer=tf.summary.FileWriter('logs/test',sess.graph)\r\n\r\n \r\n batch_count=int(train_nrows/batch_size)\r\n reminder=train_nrows%batch_size\r\n for i in range(num_steps):\r\n for n in range(batch_count):\r\n \r\n train_step.run(feed_dict={x: x_train[n*batch_size:(n+1)*batch_size], y: y_train[n*batch_size:(n+1)*batch_size]}) \r\n\r\n if reminder>0:\r\n start_index = batch_count * batch_size; \r\n train_step.run(feed_dict={x: x_train[start_index:train_nrows-1], y: y_train[start_index:train_nrows-1]}) \r\n \r\n iterate_accuracy = 0 \r\n if i%10==0:\r\n train_loss[i//10,0]=sess.run(accuracy,feed_dict={x:x_train,y:y_train})\r\n test_loss[i//10,0]=sess.run(accuracy,feed_dict={x:x_test,y:y_test})\r\n print('Iter'+str(i)+', Testing Accuracy= '+str(test_loss[i//10,0])+',Training Accuracy=' +str(train_loss[i//10,0]))\r\n \r\n x_index = np.linspace(0, num_steps, num_steps//examples_to_show)\r\n \r\n font1 = {'family' : 'Times New Roman',\r\n 'weight' : 'normal',\r\n 'size' : 32,\r\n }\r\n figsize = 8,8\r\n figure, ax = plt.subplots(figsize=figsize)\r\n\r\n \r\n A,=plt.plot(x_index, train_loss, color=\"red\",label='train_accuracy',linewidth=2.0,ms=10)\r\n B,=plt.plot(x_index, test_loss, color=\"blue\",label='test_accuracy',linewidth=2.0,ms=10)\r\n plt.legend(handles=[A,B],prop=font1)\r\n plt.xlabel(\"Interation\", font1)\r\n plt.ylabel(\"Accuracy (Species)\", font1)\r\n \r\n plt.tick_params(labelsize=23)\r\n labels = ax.get_xticklabels() + ax.get_yticklabels()\r\n [label.set_fontname('Times New Roman') for label in labels]\r\n plt.show()\r\n \r\n \r\n ","sub_path":"Python_code.py","file_name":"Python_code.py","file_ext":"py","file_size_in_byte":6090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"365939027","text":"import gi\n\nfrom editor.mediator import Mediator\nfrom editor.namegenerator.name_generator import NameGenerator\n\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\n\nfrom common.model.playertype import PlayerType\nfrom editor.widgets.itemspanel.itemspanel import ItemsPanel\n\n\nclass PlayersWidget(ItemsPanel):\n def __init__(self, mediator: Mediator):\n ItemsPanel.__init__(self, 'List of Players', mediator.player_types.remove.fire, mediator.player_types.select.fire)\n\n self.name_generator = NameGenerator('Team')\n\n self.mediator = mediator\n self.mediator.player_types.add.register(self.on_player_add)\n self.mediator.phases.add.register(self.clear_selection)\n self.mediator.phases.select.register(self.clear_selection)\n self.mediator.clear_state.register(lambda s, v: self.list_box.clear_items())\n\n add_button = Gtk.Button(None, image=Gtk.Image(stock=Gtk.STOCK_ADD))\n add_button.connect('clicked', self.add_player_button_click)\n\n self.header.add_button(add_button)\n\n def add_player_button_click(self, button):\n player = PlayerType(self.name_generator.next_name())\n self.mediator.player_types.add.fire(self, player)\n\n def on_player_add(self, sender, player):\n self.list_box.add_item(player, player.name)\n\n def clear_selection(self, sender, value):\n self.list_box.clear_selection()\n\n","sub_path":"editor/widgets/special/playerswidget.py","file_name":"playerswidget.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"}